频率选择性信道下协同通信传输关键技术研究
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摘要
协同通信系统通过相邻节点以协作的方式共享各自的天线,构成虚拟的多天线环境,有效克服了信号在无线传输中的衰落。在不增加硬件复杂度的情形下,协同技术使得单天线系统同样可以获得多天线系统才具有的空间分集增益,提高了通信系统的可靠性和传输速率,从而成为无线通信领域的研究热点。其中,目的节点低复杂度的多同步参数估计算法、对子载波频偏(Carrier Frequency Offset,CFO)鲁棒性更强的信号检测算法、信道估计性能更优的导频序列设计以及满足分布式特性的中继节点功率分配算法等成为协同通信研究中的关键和难点技术。本文围绕这些关键技术在频率选择性信道下进行了研究。
论文首先针对频率选择性信道下采用循环前缀(Cyclic Prefix,CP)块传输体制的协同通信系统特点,设计了一种基于频分复用(Frequency DivisionMultiplexing,FDM)的片结构训练序列,提出了相应的低复杂度估计算法,解决了协同通信系统中目的节点多个同步参数估计难题。协同系统中继节点的分布式特性使得目的节点接收到的信号存在多个定时偏移(Timing Offset,TO)和多个CFO。利用CP时域上的冗余性,基于CP块传输体制的协同系统可以有效地对抗频率选择性信道下中继节点之间的多个TO。然而,大传输时延下较长的CP降低了系统的带宽利用率。同时,系统中存在的多个CFO限制了系统的性能。此时,协同通信系统需要估计出所有中继节点的TO和CFO信息并在后续处理过程中加以补偿。因而,对多个TO和CFO的估计是频率选择性信道下协同系统首先必须要解决的一个重要问题。为取得复杂度与带宽利用率的最佳折中,本文构造了一种新颖的基于片结构的训练序列。利用该片结构频域正交性和时域重复性,目的节点使用基于互相关的TO估计算法和基于信号子空间分解的CFO估计算法,仅需一个训练符号周期即可完成多个同步参数的估计,有效克服了时分复用(TimeDivision Multiplexing,TDM)同步算法带宽利用率低和传统FDM同步算法复杂度高的缺陷。通过合理设计片大小,文中所提出的算法在同步及误码率性能上显著的优于传统的同步算法。
针对无反馈同步机制场景下的协同空时和空频编码系统,论文提出了对抗多个CFO干扰的信号检测算法,以较低的复杂度,有效降低了多CFO干扰带来的性能损失。利用反馈同步机制,中继节点可以调整各自的传输窗口以及载波频率,使得所有中继节点的信号在抵达目的节点时可以保持TO和CFO上的一致。然而在无反馈同步机制的协同正交频分复用(Orthogonal Frequency-DivisionMultiplexing,OFDM)系统中,未补偿的多个CFO将在目的节点引起符号间干扰(Inter-Symbol Interference,ISI)和载波间干扰(Inter-Carrier Interference,ICI),降低系统的性能,甚至使得协同系统无法获得分集增益。针对协同空时分组编码(Space-Time Block Coding,STBC)系统,文中提出了加强迭代最大似然(Enhanced Iterative Maximum Likelihood,EIML)和高级加强迭代最大似然(Advance Enhanced Iterative Maximum Likelihood,AEIML)信号检测算法。通过矩阵运算,EIML和AEIML算法完全消除了多CFO引起的ICI,并在迭代过程中逐步消除ISI。仿真表明,EIML和AEIML信号检测算法均可获得优于迭代最大似然(IterativeMaximumLikelihood,IML)及最小均方误差(MinimumMean-SquareError,MMSE)等传统的信号检测算法性能,且避免了MMSE检测算法所需的矩阵求逆运算。针对协同空频分组编码(Space-Frequency Block Coding,SFBC)系统,文中提出了排序顺序并行干扰抵消(Ordered-Successive Parallel InterferenceCancellation,OSPIC)信号检测算法及其简化算法。通过排序顺序和并行两级干扰抵消,OSPIC及其简化算法可以有效消除多CFO引起的空频码块间干扰(Inter-Block Interference,IBI),且无需高阶矩阵的求逆操作和迭代运算,因而在信号检测性能上和复杂度上均优于传统协同SFBC中的信号检测算法。
针对存在虚载波场景下基于OFDM的协同通信系统,本文提出了信道估计性能最优的导频设计方案,克服了存在虚导频时传统导频方案信道估计性能急剧下降的问题。采用基于CP的OFDM体制,协同通信系统不仅可以对抗多个传输时延,同时可以克服频率选择性信道下时域均衡复杂度高的难题。然而,为了避免数据符号经过非理想滤波器时产生失真,基于OFDM的协同通信系统中每个符号的频谱边缘存在数目不等的虚载波。这些虚载波破坏了传统OFDM系统中使得信道估计性能最优的等间隔等功率的导频结构,而现有文献针对虚载波场景所设计的导频序列与无虚载波时最优的导频序列相比在信道估计性能上相差较远。为解决这一问题,本文首先针对OFDM频域均衡的性质,提出了最小化数据符号位置上平均信道估计均方误差(Mean-Square Error,MSE)准则。与传统导频设计中使用的最小化信道估计MSE准则不同,该准则仅考虑优化数据符号位置上信道估计的精度,更加适合在有虚载波的场景下使用。利用提出的新准则和Disjoint导频序列,文中给出了具有解析表达式的最优导频功率分配算法和高效的次优导频位置选择算法。仿真表明,与传统的部分等间隔导频方案相比,所提出的导频方案在频率选择性信道下估计性能具有4 ? 20dB不等的优势。
针对协同通信系统各中继节点空间独立分布及能量受限的特性,本文提出了满足各节点单独功率约束(Per-antenna Power Constraint,PPC)的协同发送波束成型(Cooperative Transmit Beamforming,CTB)算法,克服了总功率约束(TotalPower Constraint,TPC)下CTB系统由于功率分配时变所导致的天线放大器要求高及网络生存周期低等实用化问题。在平衰落信道下,由于优化问题中仅包含单个参数,PPC发送波束成型系数的设计可以简化为对其相位进行优化的问题,因而具有较低的求解复杂度。相比之下,频率选择性信道下的PPC优化问题求解复杂度高且通常无法获得解析解。本文针对频率选择性信道下基于CP的OFDM和SC-FDE CTB系统,首先使用凸优化工具求解了PPC约束下的发送波束成型系数。为了克服凸优化算法复杂度大且精度受限的问题,文中提出了三种不同应用场景的次优PPC算法,随后将次优的PPC算法推广到目的节点包含多个接收天线的协同通信系统中。次优的PPC算法具有解析表达式,因而复杂度远小于凸优化算法。仿真表明次优的PPC算法在不同的功率配置下,可以获得接近于同等总功率约束下的最优性能。
本文最后提出了低速率的时域信道状态信息(ChannelStateInformation,CSI)反馈算法,解决了频率选择性信道下CTB系统发送端获取波束成型系数时需要占用大量反馈开销的问题。在带宽受限的反馈信道下,发送端所使用的波束成型系数精度直接决定了CTB系统的性能。已有研究表明在平衰落信道中使用矢量量化(Vector Quantization,VQ)算法可以有效解决有限比特反馈问题。然而,在频率选择性信道下,使用已有的VQ反馈方案将导致反馈开销急剧上升。利用时域CSI长度远小于数据块的长度的特点,本文提出了时域CSI VQ算法。进一步分析信道相位和幅度对发送波束成型系统的性能影响,文中提出了非均匀反馈的方案。在总反馈开销的约束下,通过合理分配信道相位和幅度上的反馈比特数,非均匀反馈可以获得最优的反馈性能。仿真表明,文中提出的时域CSI反馈算法显著的优于基于分组和内插的反馈算法。与所提出的PPC算法相结合,本文所提出的协同发送波束成型设计方案可以获得性能与反馈开销上的最佳结合。
By sharing antennas of neighboring users in a cooperative manner to construct a vir-tual multiple-antenna environment, cooperative communication systems can combat thesignal fading in wireless propagation effectively. Without increasing the hardware com-plexity, the same spatial diversity gains of multiple-antenna systems can be achieved in asingle-antenna based cooperative system. Since the cooperative technique improves thereliability and bandwidth efficiency of communication systems, it becomes an importantresearch area for the wireless communications. The low complexity multiple parame-ters synchronization algorithms at destination node, multiple Carrier Frequency Offsets(CFOs) robust data detection algorithms, pilot design for optimizing channel estimation,and power allocation algorithms of relay nodes with distributed property are the key andchallenging techniques for cooperative communication research. Thus, this dissertationfocuses on these practically key techniques in the presence of frequency-selective chan-nels.
Consideringthe characteristicsofCyclicPrefix(CP)block transmissionbased coop-erative communication systems with frequency-selective channels, this dissertation pro-poses a Frequency Division Multiplexing (FDM) based tile-structure training sequenceand low complexity estimation algorithms correspondingly, which have addressed themultiple synchronization parameters estimation problem at the destination node. Due tothe distributed property of relay nodes, there are multiple Timing Offsets (TOs) and Car-rier Frequency Offsets (CFOs) presenting in the received signals at destination node. Ex-ploiting the time domain redundancy of CP, the CP block transmission based cooperativesystems can combat the multiple TOs among relay nodes in the presence of frequency-selective channels. However, for large transmission delay cooperative systems, the extralong CP reduces the bandwidth efficiency. Moreover, uncompensated multiple CFOs de-crease the systems’performance significantly. For this case, the estimates of the relaynodes’TO and CFO are needed for the compensation procedure. Thus, multiple TOs andCFOs estimation is an important issue which needs to be solved firstly in the deploymentof cooperative systems. To achieve a good tradeoff between computational complex-ity and bandwidth efficiency, a tile-structure based training sequence is proposed in thisdissertation. Employing the frequency-domain orthogonality and time-domain repetitiveproperty of proposed tile-structure training sequences, the destination node can estimate multiple parameters in one training sequence period via using a cross-correlation typetiming estimation algorithm and signal subspace decomposition based CFOs estimationalgorithm. Proposed synchronization methods have overcame the low bandwidth effi-ciency of Time Division Multiplexing (TDM) based synchronization schemes and highcomputationally complexity of the conventional FDM based synchronization algorithms.By judiciously designing the size of the tile, proposed algorithms are shown to have bettersynchronization and Bit Error Rate (BER) performance than the conventional synchro-nization methods.
For the cooperative space-time and space-frequency coding systems, where feed-back synchronization mechanism is useless, multiple CFOs interference cancellation datadetection algorithms are proposed in this dissertation to mitigate the multiple CFOs in-terference with low computationally complexity. Employing the feedback synchroniza-tion mechanism, relay nodes can adjust their own transmitting windows and carrier fre-quencies so that the relay nodes’signals arrive at destination node with synchronous TOsand CFOs. However, for the cooperative Orthogonal Frequency-Division Multiplexing(OFDM) systems without feedback synchronization mechanism, uncompensated multi-pleCFOswillcauseInter-SymbolInterference(ISI)andInter-CarrierInterference(ICI)atdestination node, and result in performance degradation, even destroy the diversity gainsof cooperative systems. For cooperative space-time block coded (STBC) systems in thepresence of multiple CFOs, Enhanced Iterative Maximum Likelihood (EIML) and Ad-vance Enhanced Iterative Maximum Likelihood (AEIML) data detection algorithms areinvestigatedinthisdissertation. BothproposedEIMLandAEIMLdetectorscangetridofinter-carrier-interference (ICI) caused by multiple CFOs in STBC systems perfectly viaa simple matrix multiplication. The residual inter-symbol-interference (ISI) is removedby performing iterative symbol detection. Simulation shows that proposed EIML andAEIML detectors not only outperform the conventional Iterative Maximum Likelihood(IML) and Minimum Mean-Square Error (MMSE) detectors significantly, but also avoidthe large matrix inversion required by MMSE detector. Moreover, to mitigate the inter-ference caused by multiple CFOs in cooperative Space-Frequency Block Coding (SFBC)systems, Ordered-Successive Parallel Interference Cancellation (OSPIC) and complexityreducedOSPIC(CR-OSPIC) algorithmsareproposed. By performing orderedsuccessiveand parallel interference cancellation detection successively, proposed OSPIC and CR-OSPIC detectors can reduce the inter-block-interference (IBI) caused by multiple CFOseffectively. Without large scale matrix inversion and iterative operation, proposed algo- rithms have both detection performance and complexity gains compared to conventionaldata detection methods in cooperative SFBC systems.
For OFDM based cooperative systems in the presence of virtual subcarriers, pilotschemes with optimal channel estimation performance are proposed in this dissertation.Proposed pilot schemes have overcame the problem that channel estimation with conven-tional pilot schemes suffers large performance degradation under the virtual subcarriersscenario. Employing CP based OFDM transmission scheme, cooperative communica-tion systems not only can combat multiple transmission delays, but also can overcomethe equalization problem under frequency-selective channels. However, to avoid data be-ing distorted by non-ideal filters, some carriers at the spectrum edges of each symbol aredeactivated in practical OFDM based cooperative systems. These deactivated virtual sub-carriers break the equal-powered equal-spaced pilot structure for the channel estimationof fully loaded OFDM systems and will cause severe performance degradation on pilot-based channel estimation. Existing pilot schemes designed for the virtual subcarriers casehave a large channel estimation performance degradation compared to that of the optimalpilot sequences. To solve this problem and consider the frequency domain equalizationproperty of OFDM systems, a new criterion, which minimizes the channel estimationMean Square Error (MSE) on data subcarriers, is proposed in this dissertation. Unlikeconventional pilot design using minimizing channel estimates MSE as design criterion,proposed criterion only optimizes the frequency domain channel estimates on the datacarriers, which is more proper for the virtual subcarriers scenario. Employing proposedcriterion and disjoint pilot sequence, a closed-form expression for the optimal power dis-tribution and an effective suboptimal placement solution are provided. The simulationresults show that the channel estimation performance of proposed disjoint pilot designhas 4-20dB gains over that of conventional partially equal-spaced pilot schemes underfrequency-selective channels.
Considering the characteristics that the relay nodes in cooperative systems are dis-tributed in space independently and limited in energy, the Cooperative Transmit Beam-forming (CTB) algorithms under Per-antenna Power Constraint (PPC) are studied in thisdissertation. Proposed PPC CTB schemes avoid the practical drawbacks of Total PowerConstraint (TPC) CTB schemes, i.e. high-power amplifier design perspective and lownetwork lifetime, since the powers allocated to different antennas may considerably varyover time under TPC. For the flat fading case, PPC beamforming design is equivalent tothephaseoptimizationproblemsincetheoptimizationobjectonlycontainsoneparameter. On the contrary, PPC beamforming design under frequency-selective channels has highcomputationally complexity and cannot find a closed-form solution. In this dissertation,the PPC transmit beamforming coefficients are obtained by employing convex optimiza-tiontoolsfirstlyfortheCPbasedOFDMandSC-FDECTBsystem. Toovercomethehighcomputationallycomplexityandlimitedaccuracyproblemsofconvexoptimizationmeth-ods, three suboptimal PPC beamforming schemes are proposed under different scenarios.Moreover, proposedsuboptimalPPCschemesareextendedtotheCTBsystemswherethedestination node is equipped with multiple receiving antennas. With closed-form expres-sions, proposed suboptimal PPC solutions have much lower complexity than the convexoptimization algorithms. It is shown in the simulation that suboptimal PPC solutions canhave a close performance to the TPC under different power allocation scenarios.
To reduce the large feedback overhead required by feeding back the Channel StateInformation (CSI) to the transmitters in CTB systems under frequency-selective chan-nels, a low rate time domain CSI feedback method is proposed. With bandwidth limitedfeedback channels, the performance of CTB systems is dominated by the accuracy ofbeamforming vectors used at transmitters. Existing research results show that the VectorQuantization (VQ) algorithms can solve the limited feedback problem effectively withflat fading channels. However, extending existing VQ methods to frequency-selectivechannels will require a large amount of feedback. Noting that the length of time domainCSIismuchsmallerthanthelengthofdatablock, timedomainCSIVQfeedbackschemesare studied in this dissertation. By analyzing the affection of phase and amplitude parts ofCSI on the performance of CTB systems, a non-uniform feedback scheme is proposed tofurther improve the feedback performance. Under the total feedback rate constraint, pro-posed non-uniform feedback scheme can achieve the best feedback performance by judi-ciously allocating the feedback rate on phase and amplitude parts of CSI nonuniformly.Simulation results show that proposed time domain feedback method outperforms bothcluster based and interpolation based feedback schemes significantly. Combining withthe PPC coefficients design algorithms studied previously, the CTB schemes proposed inthis dissertation can achieve a good tradeoff between performance and feedback rate.
论文首先针对频率选择性信道下采用循环前缀(Cyclic Prefix,CP)块传输体制的协同通信系统特点,设计了一种基于频分复用(Frequency DivisionMultiplexing,FDM)的片结构训练序列,提出了相应的低复杂度估计算法,解决了协同通信系统中目的节点多个同步参数估计难题。协同系统中继节点的分布式特性使得目的节点接收到的信号存在多个定时偏移(Timing Offset,TO)和多个CFO。利用CP时域上的冗余性,基于CP块传输体制的协同系统可以有效地对抗频率选择性信道下中继节点之间的多个TO。然而,大传输时延下较长的CP降低了系统的带宽利用率。同时,系统中存在的多个CFO限制了系统的性能。此时,协同通信系统需要估计出所有中继节点的TO和CFO信息并在后续处理过程中加以补偿。因而,对多个TO和CFO的估计是频率选择性信道下协同系统首先必须要解决的一个重要问题。为取得复杂度与带宽利用率的最佳折中,本文构造了一种新颖的基于片结构的训练序列。利用该片结构频域正交性和时域重复性,目的节点使用基于互相关的TO估计算法和基于信号子空间分解的CFO估计算法,仅需一个训练符号周期即可完成多个同步参数的估计,有效克服了时分复用(TimeDivision Multiplexing,TDM)同步算法带宽利用率低和传统FDM同步算法复杂度高的缺陷。通过合理设计片大小,文中所提出的算法在同步及误码率性能上显著的优于传统的同步算法。
针对无反馈同步机制场景下的协同空时和空频编码系统,论文提出了对抗多个CFO干扰的信号检测算法,以较低的复杂度,有效降低了多CFO干扰带来的性能损失。利用反馈同步机制,中继节点可以调整各自的传输窗口以及载波频率,使得所有中继节点的信号在抵达目的节点时可以保持TO和CFO上的一致。然而在无反馈同步机制的协同正交频分复用(Orthogonal Frequency-DivisionMultiplexing,OFDM)系统中,未补偿的多个CFO将在目的节点引起符号间干扰(Inter-Symbol Interference,ISI)和载波间干扰(Inter-Carrier Interference,ICI),降低系统的性能,甚至使得协同系统无法获得分集增益。针对协同空时分组编码(Space-Time Block Coding,STBC)系统,文中提出了加强迭代最大似然(Enhanced Iterative Maximum Likelihood,EIML)和高级加强迭代最大似然(Advance Enhanced Iterative Maximum Likelihood,AEIML)信号检测算法。通过矩阵运算,EIML和AEIML算法完全消除了多CFO引起的ICI,并在迭代过程中逐步消除ISI。仿真表明,EIML和AEIML信号检测算法均可获得优于迭代最大似然(IterativeMaximumLikelihood,IML)及最小均方误差(MinimumMean-SquareError,MMSE)等传统的信号检测算法性能,且避免了MMSE检测算法所需的矩阵求逆运算。针对协同空频分组编码(Space-Frequency Block Coding,SFBC)系统,文中提出了排序顺序并行干扰抵消(Ordered-Successive Parallel InterferenceCancellation,OSPIC)信号检测算法及其简化算法。通过排序顺序和并行两级干扰抵消,OSPIC及其简化算法可以有效消除多CFO引起的空频码块间干扰(Inter-Block Interference,IBI),且无需高阶矩阵的求逆操作和迭代运算,因而在信号检测性能上和复杂度上均优于传统协同SFBC中的信号检测算法。
针对存在虚载波场景下基于OFDM的协同通信系统,本文提出了信道估计性能最优的导频设计方案,克服了存在虚导频时传统导频方案信道估计性能急剧下降的问题。采用基于CP的OFDM体制,协同通信系统不仅可以对抗多个传输时延,同时可以克服频率选择性信道下时域均衡复杂度高的难题。然而,为了避免数据符号经过非理想滤波器时产生失真,基于OFDM的协同通信系统中每个符号的频谱边缘存在数目不等的虚载波。这些虚载波破坏了传统OFDM系统中使得信道估计性能最优的等间隔等功率的导频结构,而现有文献针对虚载波场景所设计的导频序列与无虚载波时最优的导频序列相比在信道估计性能上相差较远。为解决这一问题,本文首先针对OFDM频域均衡的性质,提出了最小化数据符号位置上平均信道估计均方误差(Mean-Square Error,MSE)准则。与传统导频设计中使用的最小化信道估计MSE准则不同,该准则仅考虑优化数据符号位置上信道估计的精度,更加适合在有虚载波的场景下使用。利用提出的新准则和Disjoint导频序列,文中给出了具有解析表达式的最优导频功率分配算法和高效的次优导频位置选择算法。仿真表明,与传统的部分等间隔导频方案相比,所提出的导频方案在频率选择性信道下估计性能具有4 ? 20dB不等的优势。
针对协同通信系统各中继节点空间独立分布及能量受限的特性,本文提出了满足各节点单独功率约束(Per-antenna Power Constraint,PPC)的协同发送波束成型(Cooperative Transmit Beamforming,CTB)算法,克服了总功率约束(TotalPower Constraint,TPC)下CTB系统由于功率分配时变所导致的天线放大器要求高及网络生存周期低等实用化问题。在平衰落信道下,由于优化问题中仅包含单个参数,PPC发送波束成型系数的设计可以简化为对其相位进行优化的问题,因而具有较低的求解复杂度。相比之下,频率选择性信道下的PPC优化问题求解复杂度高且通常无法获得解析解。本文针对频率选择性信道下基于CP的OFDM和SC-FDE CTB系统,首先使用凸优化工具求解了PPC约束下的发送波束成型系数。为了克服凸优化算法复杂度大且精度受限的问题,文中提出了三种不同应用场景的次优PPC算法,随后将次优的PPC算法推广到目的节点包含多个接收天线的协同通信系统中。次优的PPC算法具有解析表达式,因而复杂度远小于凸优化算法。仿真表明次优的PPC算法在不同的功率配置下,可以获得接近于同等总功率约束下的最优性能。
本文最后提出了低速率的时域信道状态信息(ChannelStateInformation,CSI)反馈算法,解决了频率选择性信道下CTB系统发送端获取波束成型系数时需要占用大量反馈开销的问题。在带宽受限的反馈信道下,发送端所使用的波束成型系数精度直接决定了CTB系统的性能。已有研究表明在平衰落信道中使用矢量量化(Vector Quantization,VQ)算法可以有效解决有限比特反馈问题。然而,在频率选择性信道下,使用已有的VQ反馈方案将导致反馈开销急剧上升。利用时域CSI长度远小于数据块的长度的特点,本文提出了时域CSI VQ算法。进一步分析信道相位和幅度对发送波束成型系统的性能影响,文中提出了非均匀反馈的方案。在总反馈开销的约束下,通过合理分配信道相位和幅度上的反馈比特数,非均匀反馈可以获得最优的反馈性能。仿真表明,文中提出的时域CSI反馈算法显著的优于基于分组和内插的反馈算法。与所提出的PPC算法相结合,本文所提出的协同发送波束成型设计方案可以获得性能与反馈开销上的最佳结合。
By sharing antennas of neighboring users in a cooperative manner to construct a vir-tual multiple-antenna environment, cooperative communication systems can combat thesignal fading in wireless propagation effectively. Without increasing the hardware com-plexity, the same spatial diversity gains of multiple-antenna systems can be achieved in asingle-antenna based cooperative system. Since the cooperative technique improves thereliability and bandwidth efficiency of communication systems, it becomes an importantresearch area for the wireless communications. The low complexity multiple parame-ters synchronization algorithms at destination node, multiple Carrier Frequency Offsets(CFOs) robust data detection algorithms, pilot design for optimizing channel estimation,and power allocation algorithms of relay nodes with distributed property are the key andchallenging techniques for cooperative communication research. Thus, this dissertationfocuses on these practically key techniques in the presence of frequency-selective chan-nels.
Consideringthe characteristicsofCyclicPrefix(CP)block transmissionbased coop-erative communication systems with frequency-selective channels, this dissertation pro-poses a Frequency Division Multiplexing (FDM) based tile-structure training sequenceand low complexity estimation algorithms correspondingly, which have addressed themultiple synchronization parameters estimation problem at the destination node. Due tothe distributed property of relay nodes, there are multiple Timing Offsets (TOs) and Car-rier Frequency Offsets (CFOs) presenting in the received signals at destination node. Ex-ploiting the time domain redundancy of CP, the CP block transmission based cooperativesystems can combat the multiple TOs among relay nodes in the presence of frequency-selective channels. However, for large transmission delay cooperative systems, the extralong CP reduces the bandwidth efficiency. Moreover, uncompensated multiple CFOs de-crease the systems’performance significantly. For this case, the estimates of the relaynodes’TO and CFO are needed for the compensation procedure. Thus, multiple TOs andCFOs estimation is an important issue which needs to be solved firstly in the deploymentof cooperative systems. To achieve a good tradeoff between computational complex-ity and bandwidth efficiency, a tile-structure based training sequence is proposed in thisdissertation. Employing the frequency-domain orthogonality and time-domain repetitiveproperty of proposed tile-structure training sequences, the destination node can estimate multiple parameters in one training sequence period via using a cross-correlation typetiming estimation algorithm and signal subspace decomposition based CFOs estimationalgorithm. Proposed synchronization methods have overcame the low bandwidth effi-ciency of Time Division Multiplexing (TDM) based synchronization schemes and highcomputationally complexity of the conventional FDM based synchronization algorithms.By judiciously designing the size of the tile, proposed algorithms are shown to have bettersynchronization and Bit Error Rate (BER) performance than the conventional synchro-nization methods.
For the cooperative space-time and space-frequency coding systems, where feed-back synchronization mechanism is useless, multiple CFOs interference cancellation datadetection algorithms are proposed in this dissertation to mitigate the multiple CFOs in-terference with low computationally complexity. Employing the feedback synchroniza-tion mechanism, relay nodes can adjust their own transmitting windows and carrier fre-quencies so that the relay nodes’signals arrive at destination node with synchronous TOsand CFOs. However, for the cooperative Orthogonal Frequency-Division Multiplexing(OFDM) systems without feedback synchronization mechanism, uncompensated multi-pleCFOswillcauseInter-SymbolInterference(ISI)andInter-CarrierInterference(ICI)atdestination node, and result in performance degradation, even destroy the diversity gainsof cooperative systems. For cooperative space-time block coded (STBC) systems in thepresence of multiple CFOs, Enhanced Iterative Maximum Likelihood (EIML) and Ad-vance Enhanced Iterative Maximum Likelihood (AEIML) data detection algorithms areinvestigatedinthisdissertation. BothproposedEIMLandAEIMLdetectorscangetridofinter-carrier-interference (ICI) caused by multiple CFOs in STBC systems perfectly viaa simple matrix multiplication. The residual inter-symbol-interference (ISI) is removedby performing iterative symbol detection. Simulation shows that proposed EIML andAEIML detectors not only outperform the conventional Iterative Maximum Likelihood(IML) and Minimum Mean-Square Error (MMSE) detectors significantly, but also avoidthe large matrix inversion required by MMSE detector. Moreover, to mitigate the inter-ference caused by multiple CFOs in cooperative Space-Frequency Block Coding (SFBC)systems, Ordered-Successive Parallel Interference Cancellation (OSPIC) and complexityreducedOSPIC(CR-OSPIC) algorithmsareproposed. By performing orderedsuccessiveand parallel interference cancellation detection successively, proposed OSPIC and CR-OSPIC detectors can reduce the inter-block-interference (IBI) caused by multiple CFOseffectively. Without large scale matrix inversion and iterative operation, proposed algo- rithms have both detection performance and complexity gains compared to conventionaldata detection methods in cooperative SFBC systems.
For OFDM based cooperative systems in the presence of virtual subcarriers, pilotschemes with optimal channel estimation performance are proposed in this dissertation.Proposed pilot schemes have overcame the problem that channel estimation with conven-tional pilot schemes suffers large performance degradation under the virtual subcarriersscenario. Employing CP based OFDM transmission scheme, cooperative communica-tion systems not only can combat multiple transmission delays, but also can overcomethe equalization problem under frequency-selective channels. However, to avoid data be-ing distorted by non-ideal filters, some carriers at the spectrum edges of each symbol aredeactivated in practical OFDM based cooperative systems. These deactivated virtual sub-carriers break the equal-powered equal-spaced pilot structure for the channel estimationof fully loaded OFDM systems and will cause severe performance degradation on pilot-based channel estimation. Existing pilot schemes designed for the virtual subcarriers casehave a large channel estimation performance degradation compared to that of the optimalpilot sequences. To solve this problem and consider the frequency domain equalizationproperty of OFDM systems, a new criterion, which minimizes the channel estimationMean Square Error (MSE) on data subcarriers, is proposed in this dissertation. Unlikeconventional pilot design using minimizing channel estimates MSE as design criterion,proposed criterion only optimizes the frequency domain channel estimates on the datacarriers, which is more proper for the virtual subcarriers scenario. Employing proposedcriterion and disjoint pilot sequence, a closed-form expression for the optimal power dis-tribution and an effective suboptimal placement solution are provided. The simulationresults show that the channel estimation performance of proposed disjoint pilot designhas 4-20dB gains over that of conventional partially equal-spaced pilot schemes underfrequency-selective channels.
Considering the characteristics that the relay nodes in cooperative systems are dis-tributed in space independently and limited in energy, the Cooperative Transmit Beam-forming (CTB) algorithms under Per-antenna Power Constraint (PPC) are studied in thisdissertation. Proposed PPC CTB schemes avoid the practical drawbacks of Total PowerConstraint (TPC) CTB schemes, i.e. high-power amplifier design perspective and lownetwork lifetime, since the powers allocated to different antennas may considerably varyover time under TPC. For the flat fading case, PPC beamforming design is equivalent tothephaseoptimizationproblemsincetheoptimizationobjectonlycontainsoneparameter. On the contrary, PPC beamforming design under frequency-selective channels has highcomputationally complexity and cannot find a closed-form solution. In this dissertation,the PPC transmit beamforming coefficients are obtained by employing convex optimiza-tiontoolsfirstlyfortheCPbasedOFDMandSC-FDECTBsystem. Toovercomethehighcomputationallycomplexityandlimitedaccuracyproblemsofconvexoptimizationmeth-ods, three suboptimal PPC beamforming schemes are proposed under different scenarios.Moreover, proposedsuboptimalPPCschemesareextendedtotheCTBsystemswherethedestination node is equipped with multiple receiving antennas. With closed-form expres-sions, proposed suboptimal PPC solutions have much lower complexity than the convexoptimization algorithms. It is shown in the simulation that suboptimal PPC solutions canhave a close performance to the TPC under different power allocation scenarios.
To reduce the large feedback overhead required by feeding back the Channel StateInformation (CSI) to the transmitters in CTB systems under frequency-selective chan-nels, a low rate time domain CSI feedback method is proposed. With bandwidth limitedfeedback channels, the performance of CTB systems is dominated by the accuracy ofbeamforming vectors used at transmitters. Existing research results show that the VectorQuantization (VQ) algorithms can solve the limited feedback problem effectively withflat fading channels. However, extending existing VQ methods to frequency-selectivechannels will require a large amount of feedback. Noting that the length of time domainCSIismuchsmallerthanthelengthofdatablock, timedomainCSIVQfeedbackschemesare studied in this dissertation. By analyzing the affection of phase and amplitude parts ofCSI on the performance of CTB systems, a non-uniform feedback scheme is proposed tofurther improve the feedback performance. Under the total feedback rate constraint, pro-posed non-uniform feedback scheme can achieve the best feedback performance by judi-ciously allocating the feedback rate on phase and amplitude parts of CSI nonuniformly.Simulation results show that proposed time domain feedback method outperforms bothcluster based and interpolation based feedback schemes significantly. Combining withthe PPC coefficients design algorithms studied previously, the CTB schemes proposed inthis dissertation can achieve a good tradeoff between performance and feedback rate.
引文
[1] Tse D N C, Viswanath P. Fundamentals of Wireless Communication [M]. Cam-bridge, U.K.: Cambridge Univeristy Press, 2005.
[2] Vucetic B, Yuan J. Space-Time Coding [M]. NewYork: John Wiley & Sons Inc,2003.
[3] Telatar I E. Capacity of Multi-antenna Gaussian channels [J]. AT&T Bell Labs.Internal Tech. Memo. 1995.
[4] Foschini G J. Layered Space-Time Architecture for Wireless Communication in aFading Environment When Using Multiple Antennas [J]. Bell Labs Tech. J. 1996,1 (2): 41–59.
[5] Paulraj A J, Gore D A, Nabar R U, et al. An Overview of MIMO Communica-tions–a Key to Gigabit Wireless [J]. Proc. IEEE. 2004, 92 (2): 198–218.
[6] Nosratinia A, Hunter T E, Hedayat A. Cooperative Communication in WirelessNetworks [J]. IEEE Commun. Mag. 2004: 74–80.
[7] Scaglione A, Goeckel D L, Laneman J N. Cooperative Communications in MobileAd Hoc Networks [J]. IEEE Signal Process. Mag. 2006: 18–29.
[8] Fitzek F H P, Katz M D. Cooperation in Wireless Networks: Principles and Appli-cations [M]. Netherlands: Springer Publisher, 2006.
[9] Liu P, Tao Z, Lin Z, et al. Cooperative Wireless Communications: a Cross-LayerApproach [J]. IEEE Trans. Wireless Commun. 2006, 13 (4): 84–92.
[10] Sendonaris A, Erkip E, Aazhang B. Increasing Uplink Capacity via User Cooper-ation Diversity [C]. In Proc. IEEE Int. Symp. Info. Th. (ISIT 1998). Cambridge,USA, Aug. 1998: 156–156.
[11] Sendonaris A, Erkip E, Aazhang B. User Cooperation Diversity– Part I: SystemDescription [J]. IEEE Trans. Commun. 2003, 51 (11): 1927–1938.
[12] Sendonaris A, Erkip E, Aazhang B. User Cooperation Diversity– Part II: Imple-mentation Aspects and Performance Analysis [J]. IEEE Trans. Commun. 2003,51 (11): 1939–1948.
[13] Van der Meulen E C. Three-terminal Communication Channels [J]. Adv. Appl.Probab. 1971, 3: 120–154.
[14] Cover T, El Gamal A. Capacity theorems for the relay channel [J]. IEEE Trans.Inf. Theory. 1979, 25 (5): 572–584.
[15] Wornell G W, Laneman J N. Energy-efficient antenna sharing and relaying forwireless networks [C]. In Proc. IEEE Wireless Comm. and Net. Conf. (WCNC2000). Chicago, USA, Sep. 2000: 7–12.
[16] Laneman J N, Wornell G W. Exploiting Distributed Spatial Diversity in WirelessNetworks [C]. In Proc. IEEE Allerton Conf. Communications, Control, and Com-puting. Monticello, Italy, Oct. 2000: 1–10.
[17] Laneman J N, Wornell G W, Tse D N C. An Efficient Protocol for Realizing Co-operative Diversity in Wireless Networks [C]. In Proc. IEEE Int. Symp. Info. Th.(ISIT 2001). Washington, DC, USA, Jun. 2001: 294–294.
[18] Laneman J N. Cooperative Diversity in Wireless Networks: Algorithms and Ar-chitectures [D]. Cambridge, MA, USA: MIT, 2002.
[19] Laneman J N, Wornell G W. Distributed Space-Time-Coded Protocols for Ex-ploiting Cooperative Diversity in Wireless Networks [J]. IEEE Trans. Inf. Theory.2003, 49 (10): 2415–2425.
[20] Laneman J N, Tse D N C, Wornell G W. Cooperative Diversity in Wireless Net-works: Efficient Protocols and Outage Behavior [J]. IEEE Trans. Inf. Theory.2004, 50 (12): 3062–3080.
[21] Gupta P, Kumar P R. The Capacity of Wireless Networks [J]. IEEE Trans. Inf.Theory. 2000, 46 (2): 388–404.
[22] Grossglauser M, Tse D N C. Mobility Increases the Capacity of Ad Hoc WirelessNetworks [J]. IEEE/ACM Trans. Netw. 2002, 10 (4): 477–486.
[23] Gupta P, Kumar P R. Towards an Information Theory of Large Networks: anAchievable Rate Region [J]. IEEE Trans. Inf. Theory. 2003, 49 (8): 1877–1894.
[24] Aazhang B, Blum R S, Laneman J N, et al. Special Issue on Cooperative Commu-nications and Networking [J]. IEEE J. Sel. Areas Commun. 2007, 25 (2).
[25] Bhargava V K, Letaief K B. Special Issue on Cooperative Communications [J].IEEE Trans. Wireless Commun. 2008, 7 (5).
[26] KramerG,BerryR,ElGamalA,etal.SpecialIssueonModels,Theory,andCodesfor Relaying and Cooperation in Communication Networks [J]. IEEE Trans. Inf.Theory. 2007, 53 (10).
[27] Chin W H, Qian Y, Giambene G. Special Issue on Advances in cooperative andrelay communications [J]. IEEE Commun. Mag. 2009, 47 (2).
[28] Fitzek F H P. Special Issue on Cooperative in Wireless Networks [J]. WirelessPersonal Communication, Springer. 2007, 43 (1).
[29] Genc V, Murphy S, Yu Y, et al. IEEE 802.16j Relay-Based Wireless Access Net-works: an Overview [J]. IEEE Trans. Wireless Commun. 2008, 15 (5): 56–63.
[30] Peters S W, Heath R W, Jr. The Future of WiMAX: Multihop Relaying with IEEE802.16j [J]. IEEE Commun. Mag. 2009, 44 (1): 104–111.
[31] Stefanov A, Erkip E. Cooperative Coding for Wireless Networks [J]. IEEE Trans.Commun. 2004, 52 (9): 1470–1476.
[32] Hunter T E, Nosratinia A. Diversity through Coded Cooperation [J]. IEEE Trans.Wireless Commun. 2006, 5 (2): 283–289.
[33] Hunter T E, Sanayei S, Nosratinia A. Outage Analysis of Coded Cooperation [J].IEEE Trans. Inf. Theory. 2006, 52 (2): 375–391.
[34] Anghel P A, Kaveh M. Exact Symbol Error Probability of a Cooperative Net-workinaRayleigh-FadingEnvironment[J].IEEETrans.WirelessCommun.2004,3 (5): 1416–1421.
[35] Ribeiro A, Cai X, Giannakis G B. Symbol Error Probabilities for General Coop-erative Links [J]. IEEE Trans. Wireless Commun. 2005, 4 (3): 1264–1273.
[36] Lee I, Kim D. BER Analysis for Decode-and-Forward Relaying in DissimilarRayleigh Fading Channels [J]. IEEE Commun. Lett. 2007, 11 (1): 52–54.
[37] Sadek A K, Su W, Liu K J R. Multinode Cooperative Communications in WirelessNetworks [J]. IEEE Trans. Signal Process. 2007, 55 (1): 341–355.
[38] Bolcskei H, Nabar R U, Oyman O, et al. Capacity Scaling Laws in MIMO RelayNetworks [J]. IEEE Trans. Wireless Commun. 2006, 5 (6): 1433–1444.
[39] Adinoyi A, Yanikomeroglu H. Cooperative Relaying in Multi-Antenna Fixed Re-lay Networks [J]. IEEE Trans. Wireless Commun. 2007, 6 (2): 533–544.
[40] MuhaidatH,UysalM.CooperativeDiversitywithMulti-AntennaNodesinFadingRelay Channels [J]. IEEE Trans. Wireless Commun. 2008, 7 (8): 3036–3046.
[41] Li Z, Xia X-G. A Simple Alamouti Space-Time Transmission Scheme for Asyn-chronous Cooperative Systems [J]. IEEE Signal Process. Lett. 2007, 14 (11):804–807.
[42] MheidatH,UysalM,Al-DhahirN.EqualizationTechniquesforDistributedSpace-Time Block Codes With Amplify-and-Forward Relaying [J]. IEEE Trans. SignalProcess. 2007, 55 (5): 1839–1852.
[43] Host-Madsen A, Zhang J. Capacity Bounds and Power Allocation for WirelessRelay Channels [J]. IEEE Trans. Inf. Theory. 2005, 51 (6): 2020–2040.
[44] KramerG,GastparM,GuptaP.CooperativeStrategiesandCapacityTheoremsforRelay Networks [J]. IEEE Trans. Inf. Theory. 2005, 51 (9): 3037–3063.
[45] Dabora R, Servetto S D. On the Role of Estimate-and-Forward With Time Shar-ing in Cooperative Communication [J]. IEEE Trans. Inf. Theory. 2008, 54 (10):4409–4431.
[46] Kim T T, Skoglund M, Caire G. Quantifying the Loss of Compress-Forward Re-laying Without Wyner-Ziv Coding [J]. IEEE Trans. Inf. Theory. 2009, 55 (4):1529–1533.
[47] Hunter T E, Nosratinia A. Cooperative Diversity through Coding [C]. In Proc.IEEEInt.Symp.Info.Th.(ISIT2002).Laussane,Switzerland,Jul.2002:220–220.
[48] Chen D, Laneman J N. Modulation and Demodulation for Cooperative Diversityin Wireless Systems [J]. IEEE Trans. Wireless Commun. 2006, 5 (7): 1785–1794.
[49] XiongH,XuJ,WangP.Frequency-DomainEqualizationandDiversityCombiningFor Demodulate-and-Forward Cooperative Systems [C]. In Proc. IEEE Int. Conf.on Acoust., Speech and Sig. Proc. (ICASSP 2008). Las Vegas, USA, Mar. 2008:3245–3248.
[50] Annavajjala R, Maaref A, Zhang J. Demodulate-and-Forward Relaying withHigher Order Modulations: Impact of Channel State Uncertainty [C]. In Proc.IEEE Int. Conf. on Commun. (ICC 2010). Cape Town, South Africa, May 2010:1–5.
[51] Bao X, Li J. Efficient Message Relaying for Wireless User Cooperation: Decode-Amplify-Forward (DAF) and Hybrid DAF and Coded-Cooperation [J]. IEEETrans. Wireless Commun. 2007, 6 (11): 3975–3984.
[52] Azarian K, El Gamal H, Schniter P. On the Optimality of the ARQ-DDF Proto-col [J]. IEEE Trans. Inf. Theory. 2008, 54 (4): 1718–1724.
[53] Li Q, Li K H, Teh K C. An Achievable Rate Region of Cooperative Multiple-Access Channels with Hybrid CF and DF Cooperation [J]. IEEE Commun. Lett.2009, 13 (8): 591–593.
[54] AnghelPA,LeusG,KavehM.DistributedSpace-TimeCooperativeSystemswithRegenerativeRelays[J].IEEETrans.WirelessCommun.2006,5(11):3130–3141.
[55] JingY,HassibiB.DistributedSpace-TimeCodinginWirelessRelayNetworks[J].IEEE Trans. Wireless Commun. 2006, 5 (12): 3524–3536.
[56] Koyuncu E, Jing Y, Jafarkhani H. Distributed Beamforming in Wireless RelayNetworkswithQuantizedFeedback[J].IEEEJ.Sel.AreasCommun.2008,26(8):1429–1439.
[57] Rappaport T S. Wireless Communications [M]. New Jersey: Prentice Hall PTR,2002.
[58] Mei Y, Hua Y, Swami A, et al. Combating Synchronization Errors in CooperativeRelays[C].InProc.IEEEInt.Conf.Acoust.,Speech,andSignalProcess.(ICASSP2005). Philadelphia, USA, Mar. 2005: 18–23.
[59] Li X. Space-Time Coded Multi-Transmission Among Distributed TransmittersWithout Perfect Synchronization [J]. IEEE Signal Process. Lett. 2004, 11 (12):948–951.
[60] Barbarossa S, Scutari G. Cooperative Diversity Through Virtual Arrays in Mul-tihop Networks [C]. In Proc. IEEE Int. Conf. on Acoust., Speech and Sig. Proc.(ICASSP 2003). Hong Kong, China, Apr. 2003: 209–212.
[61] Barbarossa S, Scutari G. Distributed Space-Time Coding for Multihop Net-works [C]. In Proc. IEEE Int. Conf. on Commun. (ICC 2004). Paris, France, Jun.2004: 916–920.
[62] Mheidat H, Uysal M. Single-Carrier Frequency Domain Equalization for Broad-band Cooperative Communications [C]. In Proc. IEEE Wireless Comm. and Net.Conf. (WCNC 2006). New Orleans, USA, Apr. 2006: 1578–1584.
[63] Wang Z D, Ma X L, Giannakis G B. OFDM or Single-Carrier Block Transmis-sions? [J]. IEEE Trans. Commun. 2004, 52 (3): 380–394.
[64] PancaldiF,VitettaGM,KalbasiR,etal.Single-CarrierFrequencyDomainEqual-ization [J]. IEEE Signal Process. Mag. 2008, 25 (5): 37–56.
[65] Ng F, Li X. Cooperative STBC-OFDM Transmissions with Imperfect Synchro-nization in Time and Frequency [C]. In Proc. 39th Asilomar Conf. on Signals,Systems, and Computers. Pacific Grove, USA, Nov. 2005: 524–528.
[66] Pollet T, Moeneclaey M. The Effect of Carrier Frequency Offset on the Perfor-manceofBandLimitedSingleCarrierandOFDMSignals[C].InProc.IEEEGlob.Telecom. Conf. (GLOBECOM 1996). London, UK, Nov. 1996: 719–723.
[67] Ma X, Kobayashi H, Schwartz S. Effect of Frequency Offset on BER of OFDMand Single Carrier Systems [C]. In Proc. IEEE Int. Symp. on Personal, Indoor andMobile Radio Commun. (PIMRC 2003). Beijing, China, Sep. 2003: 2239–2243.
[68] Oh M K, Ma X L, Giannakis G B, et al. Cooperative Synchronization and ChannelEstimationinWirelessSensorNetworks[C].InProc.RecordoftheThirty-SeventhAsilomar Conf. on Signals, Systems and Computers. Pacific Grove, USA, Nov.2003: 238–242.
[69] Schmidl T M, Cox D C. Robust Frequency and Timing Synchronization forOFDM [J]. IEEE Trans. Commun. 1997, 45 (12): 1613–1621.
[70] Shi K, Serpedin E. Coarse Frame and Carrier Synchronization of OFDM Systems:A New Metric and Comparison [J]. IEEE Trans. Wireless Commun. 2004, 3 (4):1271–1284.
[71] Ghogho M, Swami A. Frame and Frequency Acquisition for OFDM [J]. IEEESignal Process. Lett. 2008, 15: 605–608.
[72] Van de Beek J, Borjesson P, Boucheret M L, et al. A Time and Frequency Syn-chronization Scheme for Multiuser OFDM [J]. IEEE J. Sel. Areas Commun. 1999,17 (11): 1900–1914.
[73] Barbarossa S, Pompili M, Giannakis G B. Channel-independent SynchronizationofOrthogonalFrequencyDivisionMultipleAccessSystems[J].IEEEJ.Sel.AreasCommun. 2002, 20 (2): 474–486.
[74] Cao Z, Tureli U, Yao Y. Deterministic Multiuser Carrier-Frequency Offset Esti-mation for Interleaved OFDMA Uplink [J]. IEEE Trans. Commun. 2004, 52 (9):1585–1594.
[75] Radovic D S, Eric M M. Performance of Subspace Based Multi-user CFO Estima-tionforInterleavedOFDMAUplink[C].InProc.Int.Conf.onComputerasaTool(EUROCON 2005). Belgrade, Serbia and Montenegro, Nov. 2005: 1602–1605.
[76] Lee J, Lee S, Bang K-J, et al. Carrier Frequency Offset Estimation Using ESPRITfor Interleaved OFDMA Uplink Systems [J]. IEEE Trans. Veh. Technol. 2007,56 (5): 3227–3231.
[77] Pun M-O, Morelli M, Kuo C-C. Maximum-likelihood Synchronization and Chan-nel Estimation for OFDMA Uplink Transmissions [J]. IEEE Trans. Commun.2006, 54 (4): 726–736.
[78] Pun M-O, Morelli M, Kuo C-C. Iterative Detection and Frequency Synchroniza-tion for OFDMA Uplink Transmissions [J]. IEEE Trans. Wireless Commun. 2007,6 (2): 629–639.
[79] Dao D N, Tellambura C. Intercarrier Interference Self-Cancellation Space-Frequency Codes for MIMO-OFDM [J]. IEEE Trans. Veh. Technol. 2005, 54 (5):1729–1738.
[80] LiZ,XiaX-G.AnAlamoutiCodedOFDMTransmissionforCooperativeSystemsRobust to Both Timing Errors and Frequency Offsets [J]. IEEE Trans. WirelessCommun. 2008, 7 (5): 1839–1844.
[81] Li Z, Qu D, Zhu G. An Equalization Technique for Distributed STBC-OFDM Sys-tem with Multiple Carrier Frequency Offsets [C]. In Proc. IEEE Wireless Com-mun.andNetworkingConf.(WCNC2006).LasVegas,USA,Apr.2006:839–843.
[82] Tian F, Xia X-G, Ching P C. Equalization in Space-Frequency Coded CooperativeCommunication System with Multiple Frequency Offsets [C]. In Proc. IEEE Int.Symp. on Signals, Circuits and Systems. New Orleans, USA, Jul. 2007: 1–4.
[83] Kim Y J, Lee H, Chung H K, et al. An Iterative Decoding Technique for Coopera-tive STBC-OFDM Systems with Multiple Carrier Frequency Offsets [C]. In Proc.IEEE Int. Symp. on Personal, Indoor and Mobile Radio Commun. (PIMRC 2007).Athens, Greece, Sep. 2007: 1–4.
[84] Sreedhar D, Chockalingam A. Interference Mitigation in Cooperative SFBC-OFDM [J]. EURASIP Journal on Advances in Signal Processing. 2008, 2008:1–11.
[85] Tian F, Xia X-G, Ching P C. Signal Detection in a Cooperative Communica-tion System with Multiple CFOs by Exploiting the Properties of Space-FrequencyCodes[C].InProc.IEEEInt.Conf.onCommun.(ICC2008).Beijing,China,May2008: 4575–4579.
[86] Tian F, Xia X-G, Ma W-K, et al. Full Diversity under Multiple Carrier FrequencyOffsets of a Family of Space-Frequency Codes [C]. In Proc. IEEE Int. Conf. onAcoustics, Speech, and Signal Proc. Taipei, Taiwan, Apr. 2009: 2733–2736.
[87] MorelliM,MengaliU.AComparisonofPilot-AidedChannelEstimationMethodsfor OFDM Systems [J]. IEEE Trans. Signal Process. 2001, 49 (12): 3065–3073.
[88] Song S, Singer A. Pilot-Aided OFDM Channel Estimation in the Presence of theGuard Band [J]. IEEE Trans. Commun. 2007, 55 (8): 1459–1465.
[89] Hu D, Yang L, Shi Y, et al. Optimal Pilot Sequence Design for Channel Estimationin MIMO OFDM Systems [J]. IEEE Commun. Lett. 2006, 10 (1): 1–3.
[90] ZhangJ,ZhouW,SunH,etal.ANovelPilotSequencesDesignforMIMOOFDMSystems With Virtual Subcarriers [C]. In Proc. Asia-Pacific Conf. on Commun.(APCC 2007). Bangkok, Thailand, Oct. 2007: 501–504.
[91] Palomar D P, Cioffi J M, Lagunas M A. Joint Tx-Rx Beamforming Design forMulticarrier MIMO Channels: a Unified Framework for Convex Optimization [J].IEEE Trans. Signal Process. 2003, 51 (9): 2381–2401.
[92] Pascual-Iserte A, Perez-Neira A I, Lagunas M A. On Power Allocation Strategiesfor Maximum Signal to Noise and Interference Ratio in an OFDM-MIMO Sys-tem [J]. IEEE Trans. Wireless Commun. 2004, 3 (3): 808–820.
[93] WieselA,EldarYC,ShamaiS.LinearPrecodingviaConicOptimizationforFixedMIMO Receivers [J]. IEEE Trans. Signal Process. 2006, 54 (1): 161–176.
[94] CodreanuM,TolliA,JunttiM,etal.JointDesignofTx-RxBeamformersinMIMODownlink Channel [J]. IEEE Trans. Signal Process. 2007, 55 (9): 4639–4655.
[95] Ding Z, Chin W H, Leung K K. Distributed Beamforming and Power Alloca-tion for Cooperative Networks [J]. IEEE Trans. Wireless Commun. 2008, 7 (5):1817–1822.
[96] Havary-Nassab V, Shahbazpanahi S, Grami A, et al. Distributed Beamforming forRelay Networks Based on Second-Order Statistics of the Channel State Informa-tion [J]. IEEE Trans. Signal Process. 2008, 56 (9): 4306–4316.
[97] Liang Y, Schober R, Gerstacker W. Time-Domain Transmit Beamforming forMIMO-OFDM Systems with Finite Rate Feedback [J]. IEEE Trans. Commun.2009, 57 (9): 2828–2838.
[98] Love D J, Heath R W, Jr. Equal Gain Transmission in Multiple-Input Multiple-Output Wireless Systems [J]. IEEE Trans. Commun. 2003, 51 (7): 1102–1110.
[99] Yu W, Lan T. Transmitter Optimization for the Multi-Antenna Downlink withPer-Antenna Power Constraints [J]. IEEE Trans. Signal Process. 2007, 55 (6):2646–2660.
[100] ZhengX,XieY,LiJ,etal.MIMOTransmitBeamformingunderUniformElemen-tal Power Constraint [J]. IEEE Trans. Signal Process. 2007, 55 (11): 5395–5406.
[101] Tolli A, Codreanu M, Juntti M. Linear Multiuser MIMO Transceiver Design withQuality of Service and Per-Antenna Power Constraints [J]. IEEE Trans. SignalProcess. 2008, 56 (7): 3049–3055.
[102] Murthy C R, Zheng J, Rao B D. Performance of Quantized Equal Gain Transmis-sion With Noisy Feedback Channels [J]. IEEE Trans. Signal Process. 2008, 56 (6):2451–2460.
[103] Abdel-Samad A, Davidson T N, Gershman A B. Robust Transmit Eigen Beam-forming Based on Imperfect Channel State Information [J]. IEEE Trans. SignalProcess. 2006, 54 (5): 1596–1609.
[104] JonietzC,GerstackerWH,SchoberR.RobustTransmitProcessingforFrequency-Selective Fading Channels with Imperfect Channel Feedback [J]. IEEE Trans.Wireless Commun. 2008, 7 (12): 5356–5368.
[105] El-Keyi A, Champagne B. Collaborative Uplink Transmit Beamforming With Ro-bustness Against Channel Estimation Errors [J]. IEEE Trans. Veh. Technol. 2009,58 (1): 126–139.
[106] Isukapalli Y, Annavajjala R, Rao B D. Performance Analysis of Transmit Beam-forming for MISO Systems with Imperfect Feedback [J]. IEEE Trans. Commun.2009, 57 (1): 222–231.
[107] Zhao Y, Adve R, Lim T J. Beamforming with Limited Feedback in Amplify-and-Forward Cooperative Networks [J]. IEEE Trans. Wireless Commun. 2008, 7 (12):5145–5149.
[108] Murthy C R, Rao B D. Quantization Methods for Equal Gain Transmission withFinite Rate Feedback [J]. IEEE Trans. Signal Process. 2007, 55 (1): 233–245.
[109] Love D J, Heath R W, Jr, Lau V K N, et al. An Overview of Limited Feedback inWireless Communication Systems [J]. IEEE J. Sel. Areas Commun. 2008, 26 (8):1341–1365.
[110] Kim Y-G, Beaulieu N C. On MIMO beamforming systems using quantized feed-back [J]. IEEE Trans. Commun. 2010, 58 (3): 820–827.
[111] Jagannathan S, Aghajan H, Goldsmith A. The Effect of Time Synchronization Er-rors on the Performance of Cooperative MISO Systems [C]. In Proc. IEEE Glob.Telecom. Conf. Workshops (GLOBECOM Workshops 2004). Dallas, USA, Dec.2004: 102–107.
[112] Y?lmaz O¨zgu¨r A. Cooperative Diversity in Carrier Frequency Offset [J]. IEEECommun. Lett. 2007, 11 (4): 307–309.
[113] Wei S, Goeckel D L, Valenti M C. Asynchronous Cooperative Diversity [J]. IEEETrans. Wireless Commun. 2006, 5 (6): 1547–1557.
[114] Wei S. Diversity¨CMultiplexing Tradeoff of Asynchronous Cooperative Diversityin Wireless Networks [J]. IEEE Trans. Inf. Theory. 2007, 53 (11): 4150–4172.
[115] Nahas M, Saadani A, Hachem W. On the Outage Probability of AsynchronousWireless Cooperative Networks [C]. In Proc. IEEE Veh. Technol. Conf. Fall (VTC2008). Calgary, Canada, Sep. 2008: 1–5.
[116] Guo X, Xia X G. Distributed Linear Convolutive Space Time Codes for Asyn-chronous Cooperative Communication Networks [J]. IEEE Trans. Wireless Com-mun. 2008, 7 (5): 1857–1861.
[117] MorelliM,KuoC-CJ,PunM-O.SynchronizationTechniquesforOrthogonalFre-quency Division Multiple Access (OFDMA): a Tutorial Review [J]. Proc. IEEE.2007, 95 (7): 1394–1427.
[118] LeeJ-H,KimS-C.TimeandFrequencySynchronizationforOFDMAUplinkSys-tem Using the SAGE Algorithm [J]. IEEE Trans. Wireless Commun. 2007, 6 (4):1176–1181.
[119] Morelli M. Timing and Frequency Synchronization for the Uplink of an OFDMASystem [J]. IEEE Trans. Commun. 2004, 52 (2): 296–306.
[120] Kay S M. Fundamentals of Statistical Signal Processing: Estimation Theory [M].Upper Saddle River: PRENTICE HALL, 1993.
[121] Minn H, Bhargava V K, Letaief K B. A Robust Timing and Frequency Synchro-nization for OFDM Systems [J]. IEEE Trans. Wireless Commun. 2003, 2 (4):822–839.
[122] SunP,ZhangL.ASimpleSolutionfortheCarrierFrequencyOffsetEstimationforTile-Based OFDMA Uplink [C]. In Proc. IEEE Asia-Pacific Conf. on Commun.(APCC 2009). Shanghai, China, Oct. 2009: 365–368.
[123] SanguinettiL,MorelliM,PoorHV.AnESPRIT-BasedApproachforInitialRang-ing in OFDMA Systems [J]. IEEE Trans. Commun. 2009, 57 (11): 3225–3229.
[124] Roy R, Paulraj A, Kailath T. ESPRIT– Direction-of-Arrival Estimation by Sub-space Rotation Methods [J]. IEEE Trans. Acoust., Speech, Signal Process. 1989,37 (7): 984–995.
[125] Stoica P, Moses R L. Introduction to Spectral Analysis [M]. New Jersey, USA:Prentice Hall, 1997.
[126] Meyer C D. Matrix Analysis and Applied Linear Algebra [M]. Philadelphia:SIAM, 2000.
[127] Bachl R. The Forward-Backward Averaging Technique Applied to TLS-ESPRITProcessing [J]. IEEE Trans. Signal Process. 1995, 43 (11): 2691–2699.
[128] Fu X Y, Minn H. Initial Uplink Synchronization and Power Control (Ranging Pro-cess) for OFDMA Systems [C]. In Proc. IEEE Glob. Telecom. Conf. (GLOBE-COM 2004). Dallas, USA, Dec. 2004: 3999–4003.
[129] Zhou E, Zhao H, Wang W B. Timing Synchronization for Interleaved OFDMAUplink System [C]. In Proc. IEEE Int. Conf. on Commun., Circuits and Systems(ICCCAS 2006). Chengdu, China, Jun. 2006: 1147–1152.
[130] Mietzner J, Eick J, Hoeher P A. On Distributed Space-Time Coding TechniquesforCooperativeWirelessNetworksandTheirSensitivitytoFrequencyOffsets[C].In Proc. Int. ITG Workshop on Smart Antennas. Munich, Germany, Mar. 2004:114–121.
[131] Alamouti S M. A Simple Transmit Diversity Scheme for Wireless Communica-tions [J]. 1998, 16 (10): 1415–1458.
[132] Lee K F, Williams D B. A Space-Time Coded Transmitter Diversity Technique forFrequency Selective Fading Channels [C]. In Proc. IEEE Sensor Array and Mul-tichannel Signal Processing Workshop. Cambridge, USA, Mar. 2000: 149–152.
[133] VielmonA,LiY,BarryJR.PerformanceofTransmitDiversityoverTime-VaryingRayleigh-Fading Channels [C]. In Proc. IEEE Glob. Commun. Conf. (GLOBE-COM 2001). San Antonio, USA, Nov. 2001: 3242–3246.
[134] Lin D-B, Chiang P-H, Li H-J. Performance Analysis of Two-Branch Transmit Di-versityBlock-CodedOFDMSystemsinTime-VaryingMultipathRayleigh-FadingChannels [J]. IEEE Trans. Veh. Technol. 2005, 54 (1): 136–148.
[135] Bauch G. Space-Time Block Codes versus Space-Frequency Block Codes [C].In Proc. IEEE Veh. Technol. Conf. (VTC Spring 2003). Jeju, Korea, Apr. 2003:567–571.
[136] Li Y, Zhang W, Xia X-G. Distributive High-Rate Space-Frequency Codes Achiev-ing Full Cooperative and Multipath Diversities for Asynchronous CooperativeCommunications [J]. IEEE Trans. Veh. Technol. 2009, 58 (1): 207–217.
[137] Kay S M. Fundamentals of Statistical Signal Processing, Volume II: DetectionTheory [M]. Upper Saddle River: PRENTICE HALL, 1998.
[138] Agrawal A, Ginis G, Cioffi J M. Channel Diagonalization Through OrthogonalSpace-Time Coding [C]. In Proc. IEEE Int. Conf. on Commun. (ICC 2002). NewYork, USA, May 2002: 1621–1624.
[139] Sreedhar D, Chockalingam A. Detection of SFBC OFDM Signals in Frequency-and Time-Selective MIMO Channels [C]. In Proc. IEEE Wireless Commun. andNetworking Conf. (WCNC 2007). Hong Kong, China, Mar. 2007: 852–857.
[140] Golden G D, Foschini G J, Valenzuela R A, et al. Detection Algorithm and Ini-tial Laboratory Results Using V-BLAST Space-Time Communication Architec-ture [J]. IEE Electron. Lett. 1999, 35 (1): 14–16.
[141] Hassibi B. A Fast Square-Root Implementatin for BLAST [C]. In Proc.IEEE Asilomar Conf. Signals, Syst., Comput. Pacific Grove, USA, Oct. 2000:1255–1259.
[142] Andrews J G. Interference Cancellation for Cellular Systems: A Contemporaryoverview [J]. IEEE Wireless Commun. 2005, 12 (2): 19–29.
[143] Cormen T H, Leiserson C E, Rivest R L, et al. Introduction to Algorithms [M].2nd ed. Massachusetts, USA: MIT Press and McGraw-Hill, 2001.
[144] Luo Z, Zhao M, Liu S, et al. Generalized Parallel Interference CancellationWith Near-Optimal Detection Performance [J]. IEEE Trans. Signal Process. 2008,56 (1): 304–312.
[145] Shang Y, Xia X-G. On Fast Recursive Algorithms for V-BLAST with Optimal Or-dered SIC Detection [J]. IEEE Trans. Wireless Commun. 2009, 8 (6): 2860–2865.
[146] Sezginer S, Bianchi P, Hachem W. Asymptotic Cram′er-Rao Bounds and TrainingDesign for Uplink MIMO-OFDMA Systems With Frequency Offsets [J]. IEEETrans. Signal Process. 2007, 55 (7): 3606–3622.
[147] Chen J, Wu Y-C, Ma S, et al. Joint CFO and Channel Estimation for MultiuserMIMO-OFDM Systems with Optimal Training Sequences [J]. IEEE Trans. SignalProcess. 2008, 56 (9): 4008–4019.
[148] LiY,CiminiLJ,SollenbergerNR.RobustChannelEstimationforOFDMSystemswith Rapid Dispersive Fading Channels [J]. IEEE Trans. Commun. 1998, 46 (7):902–915.
[149] Moulines E, Duhamel P, Cardoso J F, et al. Subspace Methods for the Blind Identi-fication of Multichannel FIR Filters [J]. IEEE Trans. Signal Process. 1995, 43 (2):516–525.
[150] TongL,PerreauS.MultichannelBlindIdentification:fromSubspacetoMaximumLikelihood Methods [J]. Proc. IEEE. 1998, 86 (10): 1951–1968.
[151] Bo¨cskei H, Heath R W, Jr, Paulraj A J. Blind Channel Identification and Equal-ization in OFDM-Based Multiantenna Systems [J]. IEEE Trans. Signal Process.2002, 50 (1): 96–109.
[152] Li C, Roy S. Subspace-Based Blind Channel Estimation for OFDM by ExploitingVirtual Carriers [J]. IEEE Trans. Wireless Commun. 2003, 2 (1): 141–150.
[153] Shin C, Heath R W, Jr, Powers E J. Blind Channel Estimation for MIMO-OFDMSystems [J]. IEEE Trans. Veh. Technol. 2007, 56 (2): 670–685.
[154] Wan F, Zhu W-P, Swamy M N S. A Semiblind Channel Estimation Approach forMIMO-OFDMSystems[J].IEEETrans.SignalProcess.2008,56(7):2821–2834.
[155] Sari H, Karam G, Jeanclaude I. Transmission Techniques for Digital TerrestrialTV Broadcasting [J]. IEEE Commun. Mag. 1995, 33 (2): 100–109.
[156] Barhumi I, Leus G, Moonen M. Optimal Training Design for MIMO OFDM Sys-tems in Mobile Wireless Channels [J]. IEEE Trans. Signal Process. 2003, 51 (6):1615–1624.
[157] Ma X, Yang L, Giannakis G B. Optimal Training for MIMO Frequency-SelectiveFading Channels [J]. IEEE Trans. Commun. 2005, 4 (2): 453–466.
[158] ZhangJ,HeZ,HuangY.TSKFuzzyApproachtoChannelEstimationForMIMO-OFDM Systems [J]. IEEE Signal Process. Lett. 2007, 14 (6): 381–384.
[159] Chen R, Zhang H. On MM-type Channel Estimation for MIMO OFDM Sys-tems [J]. IEEE Trans. Commun. 2007, 6 (3): 1046–1055.
[160] Han B, Gao X, You X, et al. An Iterative Joint Channel Estimation and SymbolDetection Algorithm Applied in OFDM System With High Data to Pilot PowerRatio [C]. In Proc. IEEE Int. Conf. on Commun. (ICC 2003). Anchorage, USA,May 2003: 2076–2080.
[161] IEEE-SA Standards Board. Wireless LAN Medium Access Control (MAC) andPhysical Layer (PHY) Specifications: High Speed Physical Layer in the 5 GHZBand [S]. IEEE802.11a, 1999.
[162] IEEE-SA Standards Board. Draft IEEE Standard for Local and metropolitan areanetworksPart16: AirInterfaceforFixedBroadbandWirelessAccessSystems[S].IEEE Std 802.16d-2004, 2004.
[163] IEEE-SA Standards Board. IEEE Standard for Local and Metropolitan Area Net-works Part 16: Air Interface for Fixed and Mobile Broadband Wireless AccessSystems Amendment for Physical and Medium Access Control Layers for Com-binedFixedandMobileOperationinLicensedBands[S].IEEEStd802.16e-2005,2006.
[164] Larsson E, Li J. Preamble Design for Multiple-Antenna OFDM-Based WLANswith Null Subcarriers [J]. IEEE Signal Process. Lett. 2001, 8 (11): 285–288.
[165] Zhu Q, Liu Z. Preamble Design for Multiple-Antenna OFDM [J]. IEEE Trans.Wireless Commun. 2008, 7 (4): 1123–1127.
[166] Lee J, Choi J, Lou H-L. Joint Maximum Likelihood Estimation of Channel andPreamble Sequence for WiMAX Systems [J]. IEEE Trans. Wireless Commun.2008, 7 (11): 4294–4303.
[167] Jiang B, Wang H, Gao X, et al. Preamble-Based Channel Estimation for Amplify-and-Forward OFDM Relay Networks [C]. In Proc. IEEE Glob. Telecom. Conf.(GLOBECOM 2009). Honolulu, USA, Nov. 2009: 1–5.
[168] Katselis D, Kofidis E, Rontogiannis A, et al. Preamble-Based Channel Estima-tion for CP-OFDM and OFDM/OQAM Systems: A Comparative Study [J]. IEEETrans. Signal Process. 2010, 58 (5): 2911–2916.
[169] Negi R, Cioffi J. Pilot Tone Selection for Channel Estimation in a Mobile OFDMSystem [J]. IEEE Trans. Consum. Electron. 1998, 44 (3): 1122–1128.
[170] Li Y. Pilot-Symbol-Aided Channel Estimation for OFDM in Wireless Systems [J].IEEE Trans. Veh. Technol. 2000, 49 (4): 1207–1215.
[171] SongW-G,LimJ-T.Pilot-SymbolAidedChannelEstimationforOFDM withFastFading Channels [J]. IEEE Trans. Broadcast. 2003, 49 (4): 398–402.
[172] Ghogho M, Swami A. Training Design for Multipath Channel and Frequency-Offset Estimation in MIMO Systems [J]. IEEE Trans. Signal Process. 2006,54 (10): 3957–3965.
[173] Fertl P, Matz G. Channel Estimation in Wireless OFDM Systems With IrregularPilot Distribution [J]. IEEE Trans. Signal Process. 2010, 58 (6): 3180–3194.
[174] Tong L, Sadler B M, Dong M. Pilot-Assisted Wireless Transmissions - GeneralModel, Design Criteria, and Signal Processing [J]. IEEE Signal Process. Mag.2004, 21 (6): 12–25.
[175] Ozdemir M K, Arslan H. Channel Estimation for Wireless OFDM Systems [J].IEEE Commun. Surveys Tuts. 2007, 9 (2): 18–48.
[176] Manton J. Optimal Training Sequences and Pilot Tones for OFDM Systems [J].IEEE Commun. Lett. 2001, 5 (4): 151–153.
[177] Minn H, Al-Dhahir N. Optimal Training Signals for MIMO OFDM Channel Esti-mation [J]. IEEE Trans. Wireless Commun. 2006, 5 (5): 1158–1168.
[178] Minn H, Al-Dhahir N, Li Y. Optimal Training Signals for MIMO OFDM ChannelEstimation in the Presence of Frequency Offset and Phase Noise [J]. IEEE Trans.Commun. 2006, 54 (10): 1754–1759.
[179] Eur.Telecommun.Standard.RadioBroadcasingSystem,DigitalAudioBroadcast-ing (DAB) to Mobile, Portable, and Fixed Receiver [S]. Sophia-Antipolis, Val-bonne, France: ETS 300 401, ETSI, 1995.
[180] Eur. Telecommun. Standard. Digital Video Broadcasting (DVB-T); Frame Struc-ture, Channel Coding, Modulation for Digital Terrestrial Television [S]. Sophia-Antipolis, Valbonne, France: ETS 300 744, ETSI, 1997.
[181] Dowler A, Doufexi A, Nix A. Performance Evaluation of Channel EstimationTechniques for a Mobile Fourth Generation Wide Area OFDM System [C]. InProc. IEEE Veh. Technol. Conf. (VTC 2002). Vancouver, Canada, Sep. 2002:2036–2040.
[182] Coope I D. On Matrix Trace Inequalities and Related Topics for Products of Her-mitian Matrix [J]. J. Math. Anal. Appl. 1994, 188 (3): 999–1001.
[183] Mudumbai R, Barriac G, Madhow U. On the Feasibility of Distributed Beam-forming in Wireless Networks [J]. IEEE Trans. Wireless Commun. 2007, 6 (5):1754–1763.
[184] MudumbaiR,BrownIIIDR,PoorHV.DistributedTransmitBeamforming:Chal-lenges and Recent Progress [J]. IEEE Commun. Mag. 2009: 102–110.
[185] Mudumbai R, Hespanha J, Madhow U, et al. Distributed Transmit BeamformingUsing Feedback Control [J]. IEEE Trans. Inf. Theory. 2010, 56 (1): 411–426.
[186] Choi J, Heath R W, Jr. Interpolation Based Transmit Beamforming for MIMO-OFDM with Limited Feedback [J]. IEEE Trans. Signal Process. 2005, 53 (11):4125–4135.
[187] S′anchez-Gar′?caJ,Soriano-EquiguaL,HeathRW,Jr.QuantizedAntennaCombin-ing for Multiuser MIMO-OFDM with Limited Feedback [J]. IEEE Signal Process.Lett. 2009, 16 (12): 1027–1030.
[188] Liang Y, Schober R, Gerstacker W. Transmit Beamforming for Frequency-Selective Channels with Decision-Feedback Equalization [J]. IEEE Trans. Wire-less Commun. 2007, 6 (12): 4401–4411.
[189] van Nee R, Prasad R. OFDM for Wireless Multimedia Communications [M]. Nor-wood, MA, USA: Artech House, 2000.
[190] Ghogho M, Gil-Jimenez V P, Swami A. Multipath Diversity and Coding Gains ofCyclic-Prefixed Single Carrier Systems [C]. In Proc. IEEE Int. Conf. on Acoust.,Speech and Sig. Proc. (ICASSP 2009). Taipei, Taiwan, Apr. 2009: 2837–2840.
[191] Huang Q, Ghogho M, Wei J. Transmit Beamforming for MISO Frequency-selective Channels with Total and Per-Antenna Power Constraints [C]. In Proc.IEEE Int. Conf. on Acoust., Speech and Sig. Proc. (ICASSP 2010). Dallas, USA,Mar. 2010.
[192] Boyd S, Vandenberghe L. Convex Optimization [M]. Cambridge, UK: CambridgeUniv. Press, 2004.
[193] Shannon C E. The Zero Error Capacity of a Noisy Channel [J]. IEEE Trans. Inf.Theory. 1956, 2 (3): 8–19.
[194] Shannon C E. Channels with Side Information at the Transmitter [J]. IBM JournalResearch and Dev. 1958, 2: 289–293.
[195] Schalkwijk J. Coding Scheme for Additive Noise Channels with Feedback - PartII: Band-limited Signals [J]. IEEE Trans. Inf. Theory. 1966, 12 (2): 183–189.
[196] Hayes J. Adaptive Feedback Communications [J]. IEEE Trans. Commun. 1968,16 (1): 29–34.
[197] Linde Y, Buzo A, Gray R M. An Algorithm for Vector Quantizer Design [J]. IEEETrans. Commun. 1980, 28 (1): 85–95.
[198] Gray R M, Neuhoff D L. Quantization [J]. IEEE Trans. Inf. Theory. 1980, 44 (6):2325–2383.
[199] Gru¨nheid R, Bolinth E, Rohling H. A Blockwise Loading Algorithm for the Adap-tive Modulation Technique in OFDM Systems [C]. In Proc. IEEE Veh. Technol.Conf. Fall (VTC 2001). Atlantic city, USA, Oct. 2001: 948–951.
[200] Love D J, Heath R W, Jr, Strohmer T. Grassmannian Beamforming for Multiple-Input Multiple-Output Wireless Systems [J]. IEEE Trans. Inf. Theory. 2003,49 (10): 2735–2747.
[201] Stoica P, Arye N. MUSIC, Maximum Likelihood, and Cramer-Rao Bound [J].IEEE Trans. Acoust., Speech, Signal Process. 1989, 37 (5): 720–741.
[2] Vucetic B, Yuan J. Space-Time Coding [M]. NewYork: John Wiley & Sons Inc,2003.
[3] Telatar I E. Capacity of Multi-antenna Gaussian channels [J]. AT&T Bell Labs.Internal Tech. Memo. 1995.
[4] Foschini G J. Layered Space-Time Architecture for Wireless Communication in aFading Environment When Using Multiple Antennas [J]. Bell Labs Tech. J. 1996,1 (2): 41–59.
[5] Paulraj A J, Gore D A, Nabar R U, et al. An Overview of MIMO Communica-tions–a Key to Gigabit Wireless [J]. Proc. IEEE. 2004, 92 (2): 198–218.
[6] Nosratinia A, Hunter T E, Hedayat A. Cooperative Communication in WirelessNetworks [J]. IEEE Commun. Mag. 2004: 74–80.
[7] Scaglione A, Goeckel D L, Laneman J N. Cooperative Communications in MobileAd Hoc Networks [J]. IEEE Signal Process. Mag. 2006: 18–29.
[8] Fitzek F H P, Katz M D. Cooperation in Wireless Networks: Principles and Appli-cations [M]. Netherlands: Springer Publisher, 2006.
[9] Liu P, Tao Z, Lin Z, et al. Cooperative Wireless Communications: a Cross-LayerApproach [J]. IEEE Trans. Wireless Commun. 2006, 13 (4): 84–92.
[10] Sendonaris A, Erkip E, Aazhang B. Increasing Uplink Capacity via User Cooper-ation Diversity [C]. In Proc. IEEE Int. Symp. Info. Th. (ISIT 1998). Cambridge,USA, Aug. 1998: 156–156.
[11] Sendonaris A, Erkip E, Aazhang B. User Cooperation Diversity– Part I: SystemDescription [J]. IEEE Trans. Commun. 2003, 51 (11): 1927–1938.
[12] Sendonaris A, Erkip E, Aazhang B. User Cooperation Diversity– Part II: Imple-mentation Aspects and Performance Analysis [J]. IEEE Trans. Commun. 2003,51 (11): 1939–1948.
[13] Van der Meulen E C. Three-terminal Communication Channels [J]. Adv. Appl.Probab. 1971, 3: 120–154.
[14] Cover T, El Gamal A. Capacity theorems for the relay channel [J]. IEEE Trans.Inf. Theory. 1979, 25 (5): 572–584.
[15] Wornell G W, Laneman J N. Energy-efficient antenna sharing and relaying forwireless networks [C]. In Proc. IEEE Wireless Comm. and Net. Conf. (WCNC2000). Chicago, USA, Sep. 2000: 7–12.
[16] Laneman J N, Wornell G W. Exploiting Distributed Spatial Diversity in WirelessNetworks [C]. In Proc. IEEE Allerton Conf. Communications, Control, and Com-puting. Monticello, Italy, Oct. 2000: 1–10.
[17] Laneman J N, Wornell G W, Tse D N C. An Efficient Protocol for Realizing Co-operative Diversity in Wireless Networks [C]. In Proc. IEEE Int. Symp. Info. Th.(ISIT 2001). Washington, DC, USA, Jun. 2001: 294–294.
[18] Laneman J N. Cooperative Diversity in Wireless Networks: Algorithms and Ar-chitectures [D]. Cambridge, MA, USA: MIT, 2002.
[19] Laneman J N, Wornell G W. Distributed Space-Time-Coded Protocols for Ex-ploiting Cooperative Diversity in Wireless Networks [J]. IEEE Trans. Inf. Theory.2003, 49 (10): 2415–2425.
[20] Laneman J N, Tse D N C, Wornell G W. Cooperative Diversity in Wireless Net-works: Efficient Protocols and Outage Behavior [J]. IEEE Trans. Inf. Theory.2004, 50 (12): 3062–3080.
[21] Gupta P, Kumar P R. The Capacity of Wireless Networks [J]. IEEE Trans. Inf.Theory. 2000, 46 (2): 388–404.
[22] Grossglauser M, Tse D N C. Mobility Increases the Capacity of Ad Hoc WirelessNetworks [J]. IEEE/ACM Trans. Netw. 2002, 10 (4): 477–486.
[23] Gupta P, Kumar P R. Towards an Information Theory of Large Networks: anAchievable Rate Region [J]. IEEE Trans. Inf. Theory. 2003, 49 (8): 1877–1894.
[24] Aazhang B, Blum R S, Laneman J N, et al. Special Issue on Cooperative Commu-nications and Networking [J]. IEEE J. Sel. Areas Commun. 2007, 25 (2).
[25] Bhargava V K, Letaief K B. Special Issue on Cooperative Communications [J].IEEE Trans. Wireless Commun. 2008, 7 (5).
[26] KramerG,BerryR,ElGamalA,etal.SpecialIssueonModels,Theory,andCodesfor Relaying and Cooperation in Communication Networks [J]. IEEE Trans. Inf.Theory. 2007, 53 (10).
[27] Chin W H, Qian Y, Giambene G. Special Issue on Advances in cooperative andrelay communications [J]. IEEE Commun. Mag. 2009, 47 (2).
[28] Fitzek F H P. Special Issue on Cooperative in Wireless Networks [J]. WirelessPersonal Communication, Springer. 2007, 43 (1).
[29] Genc V, Murphy S, Yu Y, et al. IEEE 802.16j Relay-Based Wireless Access Net-works: an Overview [J]. IEEE Trans. Wireless Commun. 2008, 15 (5): 56–63.
[30] Peters S W, Heath R W, Jr. The Future of WiMAX: Multihop Relaying with IEEE802.16j [J]. IEEE Commun. Mag. 2009, 44 (1): 104–111.
[31] Stefanov A, Erkip E. Cooperative Coding for Wireless Networks [J]. IEEE Trans.Commun. 2004, 52 (9): 1470–1476.
[32] Hunter T E, Nosratinia A. Diversity through Coded Cooperation [J]. IEEE Trans.Wireless Commun. 2006, 5 (2): 283–289.
[33] Hunter T E, Sanayei S, Nosratinia A. Outage Analysis of Coded Cooperation [J].IEEE Trans. Inf. Theory. 2006, 52 (2): 375–391.
[34] Anghel P A, Kaveh M. Exact Symbol Error Probability of a Cooperative Net-workinaRayleigh-FadingEnvironment[J].IEEETrans.WirelessCommun.2004,3 (5): 1416–1421.
[35] Ribeiro A, Cai X, Giannakis G B. Symbol Error Probabilities for General Coop-erative Links [J]. IEEE Trans. Wireless Commun. 2005, 4 (3): 1264–1273.
[36] Lee I, Kim D. BER Analysis for Decode-and-Forward Relaying in DissimilarRayleigh Fading Channels [J]. IEEE Commun. Lett. 2007, 11 (1): 52–54.
[37] Sadek A K, Su W, Liu K J R. Multinode Cooperative Communications in WirelessNetworks [J]. IEEE Trans. Signal Process. 2007, 55 (1): 341–355.
[38] Bolcskei H, Nabar R U, Oyman O, et al. Capacity Scaling Laws in MIMO RelayNetworks [J]. IEEE Trans. Wireless Commun. 2006, 5 (6): 1433–1444.
[39] Adinoyi A, Yanikomeroglu H. Cooperative Relaying in Multi-Antenna Fixed Re-lay Networks [J]. IEEE Trans. Wireless Commun. 2007, 6 (2): 533–544.
[40] MuhaidatH,UysalM.CooperativeDiversitywithMulti-AntennaNodesinFadingRelay Channels [J]. IEEE Trans. Wireless Commun. 2008, 7 (8): 3036–3046.
[41] Li Z, Xia X-G. A Simple Alamouti Space-Time Transmission Scheme for Asyn-chronous Cooperative Systems [J]. IEEE Signal Process. Lett. 2007, 14 (11):804–807.
[42] MheidatH,UysalM,Al-DhahirN.EqualizationTechniquesforDistributedSpace-Time Block Codes With Amplify-and-Forward Relaying [J]. IEEE Trans. SignalProcess. 2007, 55 (5): 1839–1852.
[43] Host-Madsen A, Zhang J. Capacity Bounds and Power Allocation for WirelessRelay Channels [J]. IEEE Trans. Inf. Theory. 2005, 51 (6): 2020–2040.
[44] KramerG,GastparM,GuptaP.CooperativeStrategiesandCapacityTheoremsforRelay Networks [J]. IEEE Trans. Inf. Theory. 2005, 51 (9): 3037–3063.
[45] Dabora R, Servetto S D. On the Role of Estimate-and-Forward With Time Shar-ing in Cooperative Communication [J]. IEEE Trans. Inf. Theory. 2008, 54 (10):4409–4431.
[46] Kim T T, Skoglund M, Caire G. Quantifying the Loss of Compress-Forward Re-laying Without Wyner-Ziv Coding [J]. IEEE Trans. Inf. Theory. 2009, 55 (4):1529–1533.
[47] Hunter T E, Nosratinia A. Cooperative Diversity through Coding [C]. In Proc.IEEEInt.Symp.Info.Th.(ISIT2002).Laussane,Switzerland,Jul.2002:220–220.
[48] Chen D, Laneman J N. Modulation and Demodulation for Cooperative Diversityin Wireless Systems [J]. IEEE Trans. Wireless Commun. 2006, 5 (7): 1785–1794.
[49] XiongH,XuJ,WangP.Frequency-DomainEqualizationandDiversityCombiningFor Demodulate-and-Forward Cooperative Systems [C]. In Proc. IEEE Int. Conf.on Acoust., Speech and Sig. Proc. (ICASSP 2008). Las Vegas, USA, Mar. 2008:3245–3248.
[50] Annavajjala R, Maaref A, Zhang J. Demodulate-and-Forward Relaying withHigher Order Modulations: Impact of Channel State Uncertainty [C]. In Proc.IEEE Int. Conf. on Commun. (ICC 2010). Cape Town, South Africa, May 2010:1–5.
[51] Bao X, Li J. Efficient Message Relaying for Wireless User Cooperation: Decode-Amplify-Forward (DAF) and Hybrid DAF and Coded-Cooperation [J]. IEEETrans. Wireless Commun. 2007, 6 (11): 3975–3984.
[52] Azarian K, El Gamal H, Schniter P. On the Optimality of the ARQ-DDF Proto-col [J]. IEEE Trans. Inf. Theory. 2008, 54 (4): 1718–1724.
[53] Li Q, Li K H, Teh K C. An Achievable Rate Region of Cooperative Multiple-Access Channels with Hybrid CF and DF Cooperation [J]. IEEE Commun. Lett.2009, 13 (8): 591–593.
[54] AnghelPA,LeusG,KavehM.DistributedSpace-TimeCooperativeSystemswithRegenerativeRelays[J].IEEETrans.WirelessCommun.2006,5(11):3130–3141.
[55] JingY,HassibiB.DistributedSpace-TimeCodinginWirelessRelayNetworks[J].IEEE Trans. Wireless Commun. 2006, 5 (12): 3524–3536.
[56] Koyuncu E, Jing Y, Jafarkhani H. Distributed Beamforming in Wireless RelayNetworkswithQuantizedFeedback[J].IEEEJ.Sel.AreasCommun.2008,26(8):1429–1439.
[57] Rappaport T S. Wireless Communications [M]. New Jersey: Prentice Hall PTR,2002.
[58] Mei Y, Hua Y, Swami A, et al. Combating Synchronization Errors in CooperativeRelays[C].InProc.IEEEInt.Conf.Acoust.,Speech,andSignalProcess.(ICASSP2005). Philadelphia, USA, Mar. 2005: 18–23.
[59] Li X. Space-Time Coded Multi-Transmission Among Distributed TransmittersWithout Perfect Synchronization [J]. IEEE Signal Process. Lett. 2004, 11 (12):948–951.
[60] Barbarossa S, Scutari G. Cooperative Diversity Through Virtual Arrays in Mul-tihop Networks [C]. In Proc. IEEE Int. Conf. on Acoust., Speech and Sig. Proc.(ICASSP 2003). Hong Kong, China, Apr. 2003: 209–212.
[61] Barbarossa S, Scutari G. Distributed Space-Time Coding for Multihop Net-works [C]. In Proc. IEEE Int. Conf. on Commun. (ICC 2004). Paris, France, Jun.2004: 916–920.
[62] Mheidat H, Uysal M. Single-Carrier Frequency Domain Equalization for Broad-band Cooperative Communications [C]. In Proc. IEEE Wireless Comm. and Net.Conf. (WCNC 2006). New Orleans, USA, Apr. 2006: 1578–1584.
[63] Wang Z D, Ma X L, Giannakis G B. OFDM or Single-Carrier Block Transmis-sions? [J]. IEEE Trans. Commun. 2004, 52 (3): 380–394.
[64] PancaldiF,VitettaGM,KalbasiR,etal.Single-CarrierFrequencyDomainEqual-ization [J]. IEEE Signal Process. Mag. 2008, 25 (5): 37–56.
[65] Ng F, Li X. Cooperative STBC-OFDM Transmissions with Imperfect Synchro-nization in Time and Frequency [C]. In Proc. 39th Asilomar Conf. on Signals,Systems, and Computers. Pacific Grove, USA, Nov. 2005: 524–528.
[66] Pollet T, Moeneclaey M. The Effect of Carrier Frequency Offset on the Perfor-manceofBandLimitedSingleCarrierandOFDMSignals[C].InProc.IEEEGlob.Telecom. Conf. (GLOBECOM 1996). London, UK, Nov. 1996: 719–723.
[67] Ma X, Kobayashi H, Schwartz S. Effect of Frequency Offset on BER of OFDMand Single Carrier Systems [C]. In Proc. IEEE Int. Symp. on Personal, Indoor andMobile Radio Commun. (PIMRC 2003). Beijing, China, Sep. 2003: 2239–2243.
[68] Oh M K, Ma X L, Giannakis G B, et al. Cooperative Synchronization and ChannelEstimationinWirelessSensorNetworks[C].InProc.RecordoftheThirty-SeventhAsilomar Conf. on Signals, Systems and Computers. Pacific Grove, USA, Nov.2003: 238–242.
[69] Schmidl T M, Cox D C. Robust Frequency and Timing Synchronization forOFDM [J]. IEEE Trans. Commun. 1997, 45 (12): 1613–1621.
[70] Shi K, Serpedin E. Coarse Frame and Carrier Synchronization of OFDM Systems:A New Metric and Comparison [J]. IEEE Trans. Wireless Commun. 2004, 3 (4):1271–1284.
[71] Ghogho M, Swami A. Frame and Frequency Acquisition for OFDM [J]. IEEESignal Process. Lett. 2008, 15: 605–608.
[72] Van de Beek J, Borjesson P, Boucheret M L, et al. A Time and Frequency Syn-chronization Scheme for Multiuser OFDM [J]. IEEE J. Sel. Areas Commun. 1999,17 (11): 1900–1914.
[73] Barbarossa S, Pompili M, Giannakis G B. Channel-independent SynchronizationofOrthogonalFrequencyDivisionMultipleAccessSystems[J].IEEEJ.Sel.AreasCommun. 2002, 20 (2): 474–486.
[74] Cao Z, Tureli U, Yao Y. Deterministic Multiuser Carrier-Frequency Offset Esti-mation for Interleaved OFDMA Uplink [J]. IEEE Trans. Commun. 2004, 52 (9):1585–1594.
[75] Radovic D S, Eric M M. Performance of Subspace Based Multi-user CFO Estima-tionforInterleavedOFDMAUplink[C].InProc.Int.Conf.onComputerasaTool(EUROCON 2005). Belgrade, Serbia and Montenegro, Nov. 2005: 1602–1605.
[76] Lee J, Lee S, Bang K-J, et al. Carrier Frequency Offset Estimation Using ESPRITfor Interleaved OFDMA Uplink Systems [J]. IEEE Trans. Veh. Technol. 2007,56 (5): 3227–3231.
[77] Pun M-O, Morelli M, Kuo C-C. Maximum-likelihood Synchronization and Chan-nel Estimation for OFDMA Uplink Transmissions [J]. IEEE Trans. Commun.2006, 54 (4): 726–736.
[78] Pun M-O, Morelli M, Kuo C-C. Iterative Detection and Frequency Synchroniza-tion for OFDMA Uplink Transmissions [J]. IEEE Trans. Wireless Commun. 2007,6 (2): 629–639.
[79] Dao D N, Tellambura C. Intercarrier Interference Self-Cancellation Space-Frequency Codes for MIMO-OFDM [J]. IEEE Trans. Veh. Technol. 2005, 54 (5):1729–1738.
[80] LiZ,XiaX-G.AnAlamoutiCodedOFDMTransmissionforCooperativeSystemsRobust to Both Timing Errors and Frequency Offsets [J]. IEEE Trans. WirelessCommun. 2008, 7 (5): 1839–1844.
[81] Li Z, Qu D, Zhu G. An Equalization Technique for Distributed STBC-OFDM Sys-tem with Multiple Carrier Frequency Offsets [C]. In Proc. IEEE Wireless Com-mun.andNetworkingConf.(WCNC2006).LasVegas,USA,Apr.2006:839–843.
[82] Tian F, Xia X-G, Ching P C. Equalization in Space-Frequency Coded CooperativeCommunication System with Multiple Frequency Offsets [C]. In Proc. IEEE Int.Symp. on Signals, Circuits and Systems. New Orleans, USA, Jul. 2007: 1–4.
[83] Kim Y J, Lee H, Chung H K, et al. An Iterative Decoding Technique for Coopera-tive STBC-OFDM Systems with Multiple Carrier Frequency Offsets [C]. In Proc.IEEE Int. Symp. on Personal, Indoor and Mobile Radio Commun. (PIMRC 2007).Athens, Greece, Sep. 2007: 1–4.
[84] Sreedhar D, Chockalingam A. Interference Mitigation in Cooperative SFBC-OFDM [J]. EURASIP Journal on Advances in Signal Processing. 2008, 2008:1–11.
[85] Tian F, Xia X-G, Ching P C. Signal Detection in a Cooperative Communica-tion System with Multiple CFOs by Exploiting the Properties of Space-FrequencyCodes[C].InProc.IEEEInt.Conf.onCommun.(ICC2008).Beijing,China,May2008: 4575–4579.
[86] Tian F, Xia X-G, Ma W-K, et al. Full Diversity under Multiple Carrier FrequencyOffsets of a Family of Space-Frequency Codes [C]. In Proc. IEEE Int. Conf. onAcoustics, Speech, and Signal Proc. Taipei, Taiwan, Apr. 2009: 2733–2736.
[87] MorelliM,MengaliU.AComparisonofPilot-AidedChannelEstimationMethodsfor OFDM Systems [J]. IEEE Trans. Signal Process. 2001, 49 (12): 3065–3073.
[88] Song S, Singer A. Pilot-Aided OFDM Channel Estimation in the Presence of theGuard Band [J]. IEEE Trans. Commun. 2007, 55 (8): 1459–1465.
[89] Hu D, Yang L, Shi Y, et al. Optimal Pilot Sequence Design for Channel Estimationin MIMO OFDM Systems [J]. IEEE Commun. Lett. 2006, 10 (1): 1–3.
[90] ZhangJ,ZhouW,SunH,etal.ANovelPilotSequencesDesignforMIMOOFDMSystems With Virtual Subcarriers [C]. In Proc. Asia-Pacific Conf. on Commun.(APCC 2007). Bangkok, Thailand, Oct. 2007: 501–504.
[91] Palomar D P, Cioffi J M, Lagunas M A. Joint Tx-Rx Beamforming Design forMulticarrier MIMO Channels: a Unified Framework for Convex Optimization [J].IEEE Trans. Signal Process. 2003, 51 (9): 2381–2401.
[92] Pascual-Iserte A, Perez-Neira A I, Lagunas M A. On Power Allocation Strategiesfor Maximum Signal to Noise and Interference Ratio in an OFDM-MIMO Sys-tem [J]. IEEE Trans. Wireless Commun. 2004, 3 (3): 808–820.
[93] WieselA,EldarYC,ShamaiS.LinearPrecodingviaConicOptimizationforFixedMIMO Receivers [J]. IEEE Trans. Signal Process. 2006, 54 (1): 161–176.
[94] CodreanuM,TolliA,JunttiM,etal.JointDesignofTx-RxBeamformersinMIMODownlink Channel [J]. IEEE Trans. Signal Process. 2007, 55 (9): 4639–4655.
[95] Ding Z, Chin W H, Leung K K. Distributed Beamforming and Power Alloca-tion for Cooperative Networks [J]. IEEE Trans. Wireless Commun. 2008, 7 (5):1817–1822.
[96] Havary-Nassab V, Shahbazpanahi S, Grami A, et al. Distributed Beamforming forRelay Networks Based on Second-Order Statistics of the Channel State Informa-tion [J]. IEEE Trans. Signal Process. 2008, 56 (9): 4306–4316.
[97] Liang Y, Schober R, Gerstacker W. Time-Domain Transmit Beamforming forMIMO-OFDM Systems with Finite Rate Feedback [J]. IEEE Trans. Commun.2009, 57 (9): 2828–2838.
[98] Love D J, Heath R W, Jr. Equal Gain Transmission in Multiple-Input Multiple-Output Wireless Systems [J]. IEEE Trans. Commun. 2003, 51 (7): 1102–1110.
[99] Yu W, Lan T. Transmitter Optimization for the Multi-Antenna Downlink withPer-Antenna Power Constraints [J]. IEEE Trans. Signal Process. 2007, 55 (6):2646–2660.
[100] ZhengX,XieY,LiJ,etal.MIMOTransmitBeamformingunderUniformElemen-tal Power Constraint [J]. IEEE Trans. Signal Process. 2007, 55 (11): 5395–5406.
[101] Tolli A, Codreanu M, Juntti M. Linear Multiuser MIMO Transceiver Design withQuality of Service and Per-Antenna Power Constraints [J]. IEEE Trans. SignalProcess. 2008, 56 (7): 3049–3055.
[102] Murthy C R, Zheng J, Rao B D. Performance of Quantized Equal Gain Transmis-sion With Noisy Feedback Channels [J]. IEEE Trans. Signal Process. 2008, 56 (6):2451–2460.
[103] Abdel-Samad A, Davidson T N, Gershman A B. Robust Transmit Eigen Beam-forming Based on Imperfect Channel State Information [J]. IEEE Trans. SignalProcess. 2006, 54 (5): 1596–1609.
[104] JonietzC,GerstackerWH,SchoberR.RobustTransmitProcessingforFrequency-Selective Fading Channels with Imperfect Channel Feedback [J]. IEEE Trans.Wireless Commun. 2008, 7 (12): 5356–5368.
[105] El-Keyi A, Champagne B. Collaborative Uplink Transmit Beamforming With Ro-bustness Against Channel Estimation Errors [J]. IEEE Trans. Veh. Technol. 2009,58 (1): 126–139.
[106] Isukapalli Y, Annavajjala R, Rao B D. Performance Analysis of Transmit Beam-forming for MISO Systems with Imperfect Feedback [J]. IEEE Trans. Commun.2009, 57 (1): 222–231.
[107] Zhao Y, Adve R, Lim T J. Beamforming with Limited Feedback in Amplify-and-Forward Cooperative Networks [J]. IEEE Trans. Wireless Commun. 2008, 7 (12):5145–5149.
[108] Murthy C R, Rao B D. Quantization Methods for Equal Gain Transmission withFinite Rate Feedback [J]. IEEE Trans. Signal Process. 2007, 55 (1): 233–245.
[109] Love D J, Heath R W, Jr, Lau V K N, et al. An Overview of Limited Feedback inWireless Communication Systems [J]. IEEE J. Sel. Areas Commun. 2008, 26 (8):1341–1365.
[110] Kim Y-G, Beaulieu N C. On MIMO beamforming systems using quantized feed-back [J]. IEEE Trans. Commun. 2010, 58 (3): 820–827.
[111] Jagannathan S, Aghajan H, Goldsmith A. The Effect of Time Synchronization Er-rors on the Performance of Cooperative MISO Systems [C]. In Proc. IEEE Glob.Telecom. Conf. Workshops (GLOBECOM Workshops 2004). Dallas, USA, Dec.2004: 102–107.
[112] Y?lmaz O¨zgu¨r A. Cooperative Diversity in Carrier Frequency Offset [J]. IEEECommun. Lett. 2007, 11 (4): 307–309.
[113] Wei S, Goeckel D L, Valenti M C. Asynchronous Cooperative Diversity [J]. IEEETrans. Wireless Commun. 2006, 5 (6): 1547–1557.
[114] Wei S. Diversity¨CMultiplexing Tradeoff of Asynchronous Cooperative Diversityin Wireless Networks [J]. IEEE Trans. Inf. Theory. 2007, 53 (11): 4150–4172.
[115] Nahas M, Saadani A, Hachem W. On the Outage Probability of AsynchronousWireless Cooperative Networks [C]. In Proc. IEEE Veh. Technol. Conf. Fall (VTC2008). Calgary, Canada, Sep. 2008: 1–5.
[116] Guo X, Xia X G. Distributed Linear Convolutive Space Time Codes for Asyn-chronous Cooperative Communication Networks [J]. IEEE Trans. Wireless Com-mun. 2008, 7 (5): 1857–1861.
[117] MorelliM,KuoC-CJ,PunM-O.SynchronizationTechniquesforOrthogonalFre-quency Division Multiple Access (OFDMA): a Tutorial Review [J]. Proc. IEEE.2007, 95 (7): 1394–1427.
[118] LeeJ-H,KimS-C.TimeandFrequencySynchronizationforOFDMAUplinkSys-tem Using the SAGE Algorithm [J]. IEEE Trans. Wireless Commun. 2007, 6 (4):1176–1181.
[119] Morelli M. Timing and Frequency Synchronization for the Uplink of an OFDMASystem [J]. IEEE Trans. Commun. 2004, 52 (2): 296–306.
[120] Kay S M. Fundamentals of Statistical Signal Processing: Estimation Theory [M].Upper Saddle River: PRENTICE HALL, 1993.
[121] Minn H, Bhargava V K, Letaief K B. A Robust Timing and Frequency Synchro-nization for OFDM Systems [J]. IEEE Trans. Wireless Commun. 2003, 2 (4):822–839.
[122] SunP,ZhangL.ASimpleSolutionfortheCarrierFrequencyOffsetEstimationforTile-Based OFDMA Uplink [C]. In Proc. IEEE Asia-Pacific Conf. on Commun.(APCC 2009). Shanghai, China, Oct. 2009: 365–368.
[123] SanguinettiL,MorelliM,PoorHV.AnESPRIT-BasedApproachforInitialRang-ing in OFDMA Systems [J]. IEEE Trans. Commun. 2009, 57 (11): 3225–3229.
[124] Roy R, Paulraj A, Kailath T. ESPRIT– Direction-of-Arrival Estimation by Sub-space Rotation Methods [J]. IEEE Trans. Acoust., Speech, Signal Process. 1989,37 (7): 984–995.
[125] Stoica P, Moses R L. Introduction to Spectral Analysis [M]. New Jersey, USA:Prentice Hall, 1997.
[126] Meyer C D. Matrix Analysis and Applied Linear Algebra [M]. Philadelphia:SIAM, 2000.
[127] Bachl R. The Forward-Backward Averaging Technique Applied to TLS-ESPRITProcessing [J]. IEEE Trans. Signal Process. 1995, 43 (11): 2691–2699.
[128] Fu X Y, Minn H. Initial Uplink Synchronization and Power Control (Ranging Pro-cess) for OFDMA Systems [C]. In Proc. IEEE Glob. Telecom. Conf. (GLOBE-COM 2004). Dallas, USA, Dec. 2004: 3999–4003.
[129] Zhou E, Zhao H, Wang W B. Timing Synchronization for Interleaved OFDMAUplink System [C]. In Proc. IEEE Int. Conf. on Commun., Circuits and Systems(ICCCAS 2006). Chengdu, China, Jun. 2006: 1147–1152.
[130] Mietzner J, Eick J, Hoeher P A. On Distributed Space-Time Coding TechniquesforCooperativeWirelessNetworksandTheirSensitivitytoFrequencyOffsets[C].In Proc. Int. ITG Workshop on Smart Antennas. Munich, Germany, Mar. 2004:114–121.
[131] Alamouti S M. A Simple Transmit Diversity Scheme for Wireless Communica-tions [J]. 1998, 16 (10): 1415–1458.
[132] Lee K F, Williams D B. A Space-Time Coded Transmitter Diversity Technique forFrequency Selective Fading Channels [C]. In Proc. IEEE Sensor Array and Mul-tichannel Signal Processing Workshop. Cambridge, USA, Mar. 2000: 149–152.
[133] VielmonA,LiY,BarryJR.PerformanceofTransmitDiversityoverTime-VaryingRayleigh-Fading Channels [C]. In Proc. IEEE Glob. Commun. Conf. (GLOBE-COM 2001). San Antonio, USA, Nov. 2001: 3242–3246.
[134] Lin D-B, Chiang P-H, Li H-J. Performance Analysis of Two-Branch Transmit Di-versityBlock-CodedOFDMSystemsinTime-VaryingMultipathRayleigh-FadingChannels [J]. IEEE Trans. Veh. Technol. 2005, 54 (1): 136–148.
[135] Bauch G. Space-Time Block Codes versus Space-Frequency Block Codes [C].In Proc. IEEE Veh. Technol. Conf. (VTC Spring 2003). Jeju, Korea, Apr. 2003:567–571.
[136] Li Y, Zhang W, Xia X-G. Distributive High-Rate Space-Frequency Codes Achiev-ing Full Cooperative and Multipath Diversities for Asynchronous CooperativeCommunications [J]. IEEE Trans. Veh. Technol. 2009, 58 (1): 207–217.
[137] Kay S M. Fundamentals of Statistical Signal Processing, Volume II: DetectionTheory [M]. Upper Saddle River: PRENTICE HALL, 1998.
[138] Agrawal A, Ginis G, Cioffi J M. Channel Diagonalization Through OrthogonalSpace-Time Coding [C]. In Proc. IEEE Int. Conf. on Commun. (ICC 2002). NewYork, USA, May 2002: 1621–1624.
[139] Sreedhar D, Chockalingam A. Detection of SFBC OFDM Signals in Frequency-and Time-Selective MIMO Channels [C]. In Proc. IEEE Wireless Commun. andNetworking Conf. (WCNC 2007). Hong Kong, China, Mar. 2007: 852–857.
[140] Golden G D, Foschini G J, Valenzuela R A, et al. Detection Algorithm and Ini-tial Laboratory Results Using V-BLAST Space-Time Communication Architec-ture [J]. IEE Electron. Lett. 1999, 35 (1): 14–16.
[141] Hassibi B. A Fast Square-Root Implementatin for BLAST [C]. In Proc.IEEE Asilomar Conf. Signals, Syst., Comput. Pacific Grove, USA, Oct. 2000:1255–1259.
[142] Andrews J G. Interference Cancellation for Cellular Systems: A Contemporaryoverview [J]. IEEE Wireless Commun. 2005, 12 (2): 19–29.
[143] Cormen T H, Leiserson C E, Rivest R L, et al. Introduction to Algorithms [M].2nd ed. Massachusetts, USA: MIT Press and McGraw-Hill, 2001.
[144] Luo Z, Zhao M, Liu S, et al. Generalized Parallel Interference CancellationWith Near-Optimal Detection Performance [J]. IEEE Trans. Signal Process. 2008,56 (1): 304–312.
[145] Shang Y, Xia X-G. On Fast Recursive Algorithms for V-BLAST with Optimal Or-dered SIC Detection [J]. IEEE Trans. Wireless Commun. 2009, 8 (6): 2860–2865.
[146] Sezginer S, Bianchi P, Hachem W. Asymptotic Cram′er-Rao Bounds and TrainingDesign for Uplink MIMO-OFDMA Systems With Frequency Offsets [J]. IEEETrans. Signal Process. 2007, 55 (7): 3606–3622.
[147] Chen J, Wu Y-C, Ma S, et al. Joint CFO and Channel Estimation for MultiuserMIMO-OFDM Systems with Optimal Training Sequences [J]. IEEE Trans. SignalProcess. 2008, 56 (9): 4008–4019.
[148] LiY,CiminiLJ,SollenbergerNR.RobustChannelEstimationforOFDMSystemswith Rapid Dispersive Fading Channels [J]. IEEE Trans. Commun. 1998, 46 (7):902–915.
[149] Moulines E, Duhamel P, Cardoso J F, et al. Subspace Methods for the Blind Identi-fication of Multichannel FIR Filters [J]. IEEE Trans. Signal Process. 1995, 43 (2):516–525.
[150] TongL,PerreauS.MultichannelBlindIdentification:fromSubspacetoMaximumLikelihood Methods [J]. Proc. IEEE. 1998, 86 (10): 1951–1968.
[151] Bo¨cskei H, Heath R W, Jr, Paulraj A J. Blind Channel Identification and Equal-ization in OFDM-Based Multiantenna Systems [J]. IEEE Trans. Signal Process.2002, 50 (1): 96–109.
[152] Li C, Roy S. Subspace-Based Blind Channel Estimation for OFDM by ExploitingVirtual Carriers [J]. IEEE Trans. Wireless Commun. 2003, 2 (1): 141–150.
[153] Shin C, Heath R W, Jr, Powers E J. Blind Channel Estimation for MIMO-OFDMSystems [J]. IEEE Trans. Veh. Technol. 2007, 56 (2): 670–685.
[154] Wan F, Zhu W-P, Swamy M N S. A Semiblind Channel Estimation Approach forMIMO-OFDMSystems[J].IEEETrans.SignalProcess.2008,56(7):2821–2834.
[155] Sari H, Karam G, Jeanclaude I. Transmission Techniques for Digital TerrestrialTV Broadcasting [J]. IEEE Commun. Mag. 1995, 33 (2): 100–109.
[156] Barhumi I, Leus G, Moonen M. Optimal Training Design for MIMO OFDM Sys-tems in Mobile Wireless Channels [J]. IEEE Trans. Signal Process. 2003, 51 (6):1615–1624.
[157] Ma X, Yang L, Giannakis G B. Optimal Training for MIMO Frequency-SelectiveFading Channels [J]. IEEE Trans. Commun. 2005, 4 (2): 453–466.
[158] ZhangJ,HeZ,HuangY.TSKFuzzyApproachtoChannelEstimationForMIMO-OFDM Systems [J]. IEEE Signal Process. Lett. 2007, 14 (6): 381–384.
[159] Chen R, Zhang H. On MM-type Channel Estimation for MIMO OFDM Sys-tems [J]. IEEE Trans. Commun. 2007, 6 (3): 1046–1055.
[160] Han B, Gao X, You X, et al. An Iterative Joint Channel Estimation and SymbolDetection Algorithm Applied in OFDM System With High Data to Pilot PowerRatio [C]. In Proc. IEEE Int. Conf. on Commun. (ICC 2003). Anchorage, USA,May 2003: 2076–2080.
[161] IEEE-SA Standards Board. Wireless LAN Medium Access Control (MAC) andPhysical Layer (PHY) Specifications: High Speed Physical Layer in the 5 GHZBand [S]. IEEE802.11a, 1999.
[162] IEEE-SA Standards Board. Draft IEEE Standard for Local and metropolitan areanetworksPart16: AirInterfaceforFixedBroadbandWirelessAccessSystems[S].IEEE Std 802.16d-2004, 2004.
[163] IEEE-SA Standards Board. IEEE Standard for Local and Metropolitan Area Net-works Part 16: Air Interface for Fixed and Mobile Broadband Wireless AccessSystems Amendment for Physical and Medium Access Control Layers for Com-binedFixedandMobileOperationinLicensedBands[S].IEEEStd802.16e-2005,2006.
[164] Larsson E, Li J. Preamble Design for Multiple-Antenna OFDM-Based WLANswith Null Subcarriers [J]. IEEE Signal Process. Lett. 2001, 8 (11): 285–288.
[165] Zhu Q, Liu Z. Preamble Design for Multiple-Antenna OFDM [J]. IEEE Trans.Wireless Commun. 2008, 7 (4): 1123–1127.
[166] Lee J, Choi J, Lou H-L. Joint Maximum Likelihood Estimation of Channel andPreamble Sequence for WiMAX Systems [J]. IEEE Trans. Wireless Commun.2008, 7 (11): 4294–4303.
[167] Jiang B, Wang H, Gao X, et al. Preamble-Based Channel Estimation for Amplify-and-Forward OFDM Relay Networks [C]. In Proc. IEEE Glob. Telecom. Conf.(GLOBECOM 2009). Honolulu, USA, Nov. 2009: 1–5.
[168] Katselis D, Kofidis E, Rontogiannis A, et al. Preamble-Based Channel Estima-tion for CP-OFDM and OFDM/OQAM Systems: A Comparative Study [J]. IEEETrans. Signal Process. 2010, 58 (5): 2911–2916.
[169] Negi R, Cioffi J. Pilot Tone Selection for Channel Estimation in a Mobile OFDMSystem [J]. IEEE Trans. Consum. Electron. 1998, 44 (3): 1122–1128.
[170] Li Y. Pilot-Symbol-Aided Channel Estimation for OFDM in Wireless Systems [J].IEEE Trans. Veh. Technol. 2000, 49 (4): 1207–1215.
[171] SongW-G,LimJ-T.Pilot-SymbolAidedChannelEstimationforOFDM withFastFading Channels [J]. IEEE Trans. Broadcast. 2003, 49 (4): 398–402.
[172] Ghogho M, Swami A. Training Design for Multipath Channel and Frequency-Offset Estimation in MIMO Systems [J]. IEEE Trans. Signal Process. 2006,54 (10): 3957–3965.
[173] Fertl P, Matz G. Channel Estimation in Wireless OFDM Systems With IrregularPilot Distribution [J]. IEEE Trans. Signal Process. 2010, 58 (6): 3180–3194.
[174] Tong L, Sadler B M, Dong M. Pilot-Assisted Wireless Transmissions - GeneralModel, Design Criteria, and Signal Processing [J]. IEEE Signal Process. Mag.2004, 21 (6): 12–25.
[175] Ozdemir M K, Arslan H. Channel Estimation for Wireless OFDM Systems [J].IEEE Commun. Surveys Tuts. 2007, 9 (2): 18–48.
[176] Manton J. Optimal Training Sequences and Pilot Tones for OFDM Systems [J].IEEE Commun. Lett. 2001, 5 (4): 151–153.
[177] Minn H, Al-Dhahir N. Optimal Training Signals for MIMO OFDM Channel Esti-mation [J]. IEEE Trans. Wireless Commun. 2006, 5 (5): 1158–1168.
[178] Minn H, Al-Dhahir N, Li Y. Optimal Training Signals for MIMO OFDM ChannelEstimation in the Presence of Frequency Offset and Phase Noise [J]. IEEE Trans.Commun. 2006, 54 (10): 1754–1759.
[179] Eur.Telecommun.Standard.RadioBroadcasingSystem,DigitalAudioBroadcast-ing (DAB) to Mobile, Portable, and Fixed Receiver [S]. Sophia-Antipolis, Val-bonne, France: ETS 300 401, ETSI, 1995.
[180] Eur. Telecommun. Standard. Digital Video Broadcasting (DVB-T); Frame Struc-ture, Channel Coding, Modulation for Digital Terrestrial Television [S]. Sophia-Antipolis, Valbonne, France: ETS 300 744, ETSI, 1997.
[181] Dowler A, Doufexi A, Nix A. Performance Evaluation of Channel EstimationTechniques for a Mobile Fourth Generation Wide Area OFDM System [C]. InProc. IEEE Veh. Technol. Conf. (VTC 2002). Vancouver, Canada, Sep. 2002:2036–2040.
[182] Coope I D. On Matrix Trace Inequalities and Related Topics for Products of Her-mitian Matrix [J]. J. Math. Anal. Appl. 1994, 188 (3): 999–1001.
[183] Mudumbai R, Barriac G, Madhow U. On the Feasibility of Distributed Beam-forming in Wireless Networks [J]. IEEE Trans. Wireless Commun. 2007, 6 (5):1754–1763.
[184] MudumbaiR,BrownIIIDR,PoorHV.DistributedTransmitBeamforming:Chal-lenges and Recent Progress [J]. IEEE Commun. Mag. 2009: 102–110.
[185] Mudumbai R, Hespanha J, Madhow U, et al. Distributed Transmit BeamformingUsing Feedback Control [J]. IEEE Trans. Inf. Theory. 2010, 56 (1): 411–426.
[186] Choi J, Heath R W, Jr. Interpolation Based Transmit Beamforming for MIMO-OFDM with Limited Feedback [J]. IEEE Trans. Signal Process. 2005, 53 (11):4125–4135.
[187] S′anchez-Gar′?caJ,Soriano-EquiguaL,HeathRW,Jr.QuantizedAntennaCombin-ing for Multiuser MIMO-OFDM with Limited Feedback [J]. IEEE Signal Process.Lett. 2009, 16 (12): 1027–1030.
[188] Liang Y, Schober R, Gerstacker W. Transmit Beamforming for Frequency-Selective Channels with Decision-Feedback Equalization [J]. IEEE Trans. Wire-less Commun. 2007, 6 (12): 4401–4411.
[189] van Nee R, Prasad R. OFDM for Wireless Multimedia Communications [M]. Nor-wood, MA, USA: Artech House, 2000.
[190] Ghogho M, Gil-Jimenez V P, Swami A. Multipath Diversity and Coding Gains ofCyclic-Prefixed Single Carrier Systems [C]. In Proc. IEEE Int. Conf. on Acoust.,Speech and Sig. Proc. (ICASSP 2009). Taipei, Taiwan, Apr. 2009: 2837–2840.
[191] Huang Q, Ghogho M, Wei J. Transmit Beamforming for MISO Frequency-selective Channels with Total and Per-Antenna Power Constraints [C]. In Proc.IEEE Int. Conf. on Acoust., Speech and Sig. Proc. (ICASSP 2010). Dallas, USA,Mar. 2010.
[192] Boyd S, Vandenberghe L. Convex Optimization [M]. Cambridge, UK: CambridgeUniv. Press, 2004.
[193] Shannon C E. The Zero Error Capacity of a Noisy Channel [J]. IEEE Trans. Inf.Theory. 1956, 2 (3): 8–19.
[194] Shannon C E. Channels with Side Information at the Transmitter [J]. IBM JournalResearch and Dev. 1958, 2: 289–293.
[195] Schalkwijk J. Coding Scheme for Additive Noise Channels with Feedback - PartII: Band-limited Signals [J]. IEEE Trans. Inf. Theory. 1966, 12 (2): 183–189.
[196] Hayes J. Adaptive Feedback Communications [J]. IEEE Trans. Commun. 1968,16 (1): 29–34.
[197] Linde Y, Buzo A, Gray R M. An Algorithm for Vector Quantizer Design [J]. IEEETrans. Commun. 1980, 28 (1): 85–95.
[198] Gray R M, Neuhoff D L. Quantization [J]. IEEE Trans. Inf. Theory. 1980, 44 (6):2325–2383.
[199] Gru¨nheid R, Bolinth E, Rohling H. A Blockwise Loading Algorithm for the Adap-tive Modulation Technique in OFDM Systems [C]. In Proc. IEEE Veh. Technol.Conf. Fall (VTC 2001). Atlantic city, USA, Oct. 2001: 948–951.
[200] Love D J, Heath R W, Jr, Strohmer T. Grassmannian Beamforming for Multiple-Input Multiple-Output Wireless Systems [J]. IEEE Trans. Inf. Theory. 2003,49 (10): 2735–2747.
[201] Stoica P, Arye N. MUSIC, Maximum Likelihood, and Cramer-Rao Bound [J].IEEE Trans. Acoust., Speech, Signal Process. 1989, 37 (5): 720–741.