无线通信系统协作传输和信道互易性问题研究
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摘要
为了满足人们对数据速率和数据传输可靠性的不断提升的要求，在未来无线通信系统中，MIMO技术成为必不可少的基础技术之一。MIMO技术提供的复用增益和分集增益分别为提升数据传输速率和数据传输可靠性提供了技术保证。基于上述原因，MIMO及其相关技术得到了广泛的研究和应用，其中就包括协作传输技术和多天线发送预处理技术。
协作传输技术是MIMO技术在单天线环境下的自然推广。很多时候在廉价的小型终端上无法安装多个天线，而多个单天线终端互相借用对方的天线进行协作传输，可以构成虚拟的多天线系统以应用MIMO技术的众多研究成果。但不同于传统MIMO系统，协作传输系统中的多个分布式天线无法达到理想同步，因此导致许多可应用于传统MIMO系统的技术无法直接应用于协作传输系统中。因此，需要对异步环境下的协作传输进行研究以解决上述问题。
另一个MIMO相关技术是多天线发送预处理技术。在发送端获得信道状态信息(CSI)的情况下，使用发送预处理可明显提升系统性能。FDD系统的上下行链路工作于不同频点，因此上下行链路的信道不具有互易性，移动台(MS)需要使用专门的反馈信道把测量到的下行链路CSI发送到基站(BS)，从而浪费了系统资源，增加了复杂度；而TDD系统的上下行链路工作于同一频点，因此上下行信道互易。这样BS可以根据上行检测到的CSI进行发送预处理，而不需要使用反馈信道，这成为TDD系统的一个天然优势。但有多种因素都会对信道互易性产生影响，因此，需要对这些影响因素进行研究并补偿。
本文针对上述异步协作传输问题和信道互易性问题进行了研究，所取得的主要研究成果为：
1.对异步空时协作传输技术进行了研究。针对异步环境下无法使用正交空时分组码的问题，提出了一种基于单载波块传输的异步协作传输机制。在该机制中，中继节点将来自源节点的数据进行分段后按既定协作规则使用单载波块传输方式传输；目的节点首先对接收到的对多个传输块进行FFT，然后通过线性组合分离相互叠加的数据块，再经频域均衡和IDFT及符号判决以恢复原始数据块。通过块传输技术，所提算法克服了异步所致的码字正交性丧失及信道弥散等问题，实现了满分集增益。接下来基于上述思想，给出了一般化的适合异步环境的空时协作传输码字。码字的设计基于OSTBC码字的设计思想，通过块传输克服传统OSTBC码字无法在异步环境下直接使用的缺点，实现了满分集增益。
2.对I/Q不平衡引起的信道互易性损失问题进行了研究。存在I/Q不平衡的TDD系统中，I/Q不平衡的影响最终反映到系统的等效信道上，这样BS上行检测到的CSI已不能表征下行链路的信道状态，此时BS基于该CSI进行的发送预处理会出现偏差，进而导致系统性能的严重下降。针对此问题，提出了一种互易性补偿方法。该方法基于正式通信之前的双向信道测量来补偿I/Q不平衡的影响。BS和MS在正式通信之前，首先进行各自接收方向上的信道测量，得到上下行链路的CSI，接着MS将检测到的下行链路CSI反馈到BS，由BS计算出校准矩阵，然后BS将用于MS的校准矩阵发送给MS，这样BS和MS在发送时，首先使用校准矩阵进行预处理，使实际检测到的上下行信道的互易性得到保持。
3.对信道时变特性引起的信道互易性损失进行了研究。在TDD系统中，BS的上行信道检测和下行发送具有一定的时间间隔。此间隔内的信道时变会导致上下行信道的互易性丧失。针对此问题，提出了一种基于变换域显著分量检测和预测的互易性补偿算法。首先将初步的LS信道估计所得到的导频信道CFR序列进行DFT转换到变换域，接着在变换域中使用MDL准则进行显著分量检测，再对每个显著分量使用基于修正协方差的AR模型进行预测以补偿由信道时变特性所致的信道互易性损失。所提算法有效地降低了信道估计和预测误差，弥补了由时变引起的信道非互易性所带来的系统容量损失，同时与频域直接预测方法相比，大大降低了系统复杂度。
4.对信道估计误差引起的信道互易性丧失进行了研究。在存在虚载波和非严格等距导频的TDD-MIMO-OFDM系统中，把初步LS估计得到的导频信道CFR序列通过IFFT转换到时域时，出现了严重的时域CIR能量泄漏，从而使得FFT插值算法等基于时域处理的算法不能使用，无法实现估计误差的抑制。针对此问题，提出了一种低复杂度的信道估计算法以对抗由信道估计误差导致的互易性损失。该算法首先通过频域线性插值对虚载波位置的CFR进行添加，然后重新选取严格等距子载波作为导频，并对此新的导频CFR序列进行IFFT变换到时域，然后对所得的时域CIR序列进行基于噪声门限的去噪，从而有效地抑制了信道估计误差，使信道互易性得到保持，弥补了由信道非互易所致的系统容量损失。
对以上方法都在理论分析的基础上通过仿真实验验证了其正确性和可行性。
In order to meet the increasing demands on higher rate and reliability of datatransmission，Multiple Input Multiple Output (MIMO) technique has been chosen asone of the indispensably basic techniques used in future wireless communicationsystems. The multiplexing gains and diversity gains obtained by MIMO techniqueprovide technical guarantee for higher data rate and reliability respectively. For thereason above, MIMO technique as well as some related techniques have been studiedand applied widely, including cooperative transmission and multi-antenna transmitpreprocessing.
Cooperative transmission is the natural extend of MIMO technique in single-antennaenvironment. Usually multi-antennas are not available for a cheap and small-sized userterminal, whereas several single-antenna user terminals can share their antennas to forma virtual MIMO system to reap the various benefits of MIMO technique. However, it isdifferent from the conventional MIMO systems that the distributed antennas in acooperative transmission system can’t achieve perfect synchronization, so that manytechniques which are suitable for conventional MIMO systems can’t be directlyemployed. Therefore, it’s necessary to study the cooperative transmission inasynchronous environment so as to solve this problem.
Another technique related to MIMO is multi-antennas transmit preprocessing. Withchannel state information (CSI) at transmitter, transmit preprocessing can significantlyimprove system performance. In FDD systems, the uplink and downlink use differentfrequency bands, which means no reciprocity between the two channels can be tookadvantage of, so that a mobile station (MS) can’t help sending CSI to base station (BS)via a feedback channel. It is not only a waste of system resource but also an increasingin system complexity. However, the uplink and downlink channels in a TDD systemshare the same frequency band, which means that there exists reciprocity between theproperties of the two channels. Thus, BS can perform transmit preprocessing accordingto the CSI obtained by means of uplink channel estimation rather than via a feedbackchannel, which is believed to be an inherent superiority of TDD systems. Unfortunately,various factors can damage the channel reciprocity; so that it’s a vital issue how tocompensate the influence resulted from such factors.
This dissertation mainly researches on the problems of asynchronous cooperativetransmission and channel reciprocity mentioned above. The author’s main contributionsare as follows.
1. Considering non-feasibility of OSTBC in asynchronous environment, the dissertationproposes an asynchronous cooperative transmission scheme based on single-carrierblock transmission. In the scheme, the relay nodes segment the signal received from thesource node into several blocks and retransmit them based on single-carrier blocktransmission mode according to the predefined cooperation rules. For the multipleblocks of received signal, the destination node performs FFT respectively, separates theoverlapped ones among them by means of linear combination, and then performsfrequency domain equalization, IFFT and symbol decision to recover the original data.The proposed scheme can solves the problems of orthogonality damage and channeldispersion resulted from the asynchronization, and can achieve full diversity gains.Based on this idea, a generalized space-time cooperative transmission code withmodified OSTBC which is suitable for asynchronous environment is presented. It cansolve the problem of non-feasibility of OSTBC in asynchronous environment andachieve full diversity gains.
2. In TDD systems with I/Q imbalance, the negative effect of I/Q imbalance will resultin the non-reciprocity between uplink and downlink channels, so as to degrade thesystem performance. Aiming at this problem, a compensation scheme is proposed,which is based on bidirectional channel estimations. Before starting formal datatransmission, BS and MS perform channel estimations to get the CSI of uplink anddownlink respectively, then MS send the downlink CSI to BS which is responsible forcalculating the calibration matrices used for BS and MS, and at last BS send MS’scalibration matrix to MS. In this way, BS and MS can use calibration matrices fortransmit preprocessing to maintain the channel reciprocity.
3. Time-variance property of wireless channel will lead to differences between uplinkand downlink CSI of TDD systems to damage the channel reciprocity. Aiming at thisproblem, a compensation method based on transform-domain detection and prediction isproposed. Firstly the channel frequency response (CFR) sequence of pilot channelsobtained by least square estimation is transformed into transform-domain by DFT, andthen significant components in transform-domain is picked up with minimumdescription length (MDL) criterion, and at last prediction is made for each component tocompensate channel time-variances. It is through transform domain processing that theproposed method effectively reduces the estimation and prediction errors, and henceremedies the system capacity loses. Meanwhile the proposed method greatly lowers thesystem complexity compared with the frequency domain prediction method.
4. Channel estimation error is another causation of channel non-reciprocity. InOFDM systems with virtual subcarriers and non-strictly equidistant pilots, seriousenergy leakage of channel impulse response (CIR) emerges when the pilots’ CFRsequence obtained by LS criterion is transformed into time domain using IFFT, so thatthe algorithms based on time domain processing including FFT interpolation can’t beused to suppress estimation error. Aiming at this problem, a low complexity channelestimation method is proposed to compensate the channel non-reciprocity caused byestimation error. Firstly the CFRs of virtual subcarriers are added by linear interpolation,then strictly equidistant subcarriers are chosen as new pilot channels for CIR recoveryby IFFT, and at last a threshold-based denoising is performed to the recovered CIRsequence to suppress the estimation error and compensate the channel non-reciprocity.
All the proposed methods mentioned above are validated for their correctness andfeasibility by means of theoretical analysis and simulation experiments.
协作传输技术是MIMO技术在单天线环境下的自然推广。很多时候在廉价的小型终端上无法安装多个天线，而多个单天线终端互相借用对方的天线进行协作传输，可以构成虚拟的多天线系统以应用MIMO技术的众多研究成果。但不同于传统MIMO系统，协作传输系统中的多个分布式天线无法达到理想同步，因此导致许多可应用于传统MIMO系统的技术无法直接应用于协作传输系统中。因此，需要对异步环境下的协作传输进行研究以解决上述问题。
另一个MIMO相关技术是多天线发送预处理技术。在发送端获得信道状态信息(CSI)的情况下，使用发送预处理可明显提升系统性能。FDD系统的上下行链路工作于不同频点，因此上下行链路的信道不具有互易性，移动台(MS)需要使用专门的反馈信道把测量到的下行链路CSI发送到基站(BS)，从而浪费了系统资源，增加了复杂度；而TDD系统的上下行链路工作于同一频点，因此上下行信道互易。这样BS可以根据上行检测到的CSI进行发送预处理，而不需要使用反馈信道，这成为TDD系统的一个天然优势。但有多种因素都会对信道互易性产生影响，因此，需要对这些影响因素进行研究并补偿。
本文针对上述异步协作传输问题和信道互易性问题进行了研究，所取得的主要研究成果为：
1.对异步空时协作传输技术进行了研究。针对异步环境下无法使用正交空时分组码的问题，提出了一种基于单载波块传输的异步协作传输机制。在该机制中，中继节点将来自源节点的数据进行分段后按既定协作规则使用单载波块传输方式传输；目的节点首先对接收到的对多个传输块进行FFT，然后通过线性组合分离相互叠加的数据块，再经频域均衡和IDFT及符号判决以恢复原始数据块。通过块传输技术，所提算法克服了异步所致的码字正交性丧失及信道弥散等问题，实现了满分集增益。接下来基于上述思想，给出了一般化的适合异步环境的空时协作传输码字。码字的设计基于OSTBC码字的设计思想，通过块传输克服传统OSTBC码字无法在异步环境下直接使用的缺点，实现了满分集增益。
2.对I/Q不平衡引起的信道互易性损失问题进行了研究。存在I/Q不平衡的TDD系统中，I/Q不平衡的影响最终反映到系统的等效信道上，这样BS上行检测到的CSI已不能表征下行链路的信道状态，此时BS基于该CSI进行的发送预处理会出现偏差，进而导致系统性能的严重下降。针对此问题，提出了一种互易性补偿方法。该方法基于正式通信之前的双向信道测量来补偿I/Q不平衡的影响。BS和MS在正式通信之前，首先进行各自接收方向上的信道测量，得到上下行链路的CSI，接着MS将检测到的下行链路CSI反馈到BS，由BS计算出校准矩阵，然后BS将用于MS的校准矩阵发送给MS，这样BS和MS在发送时，首先使用校准矩阵进行预处理，使实际检测到的上下行信道的互易性得到保持。
3.对信道时变特性引起的信道互易性损失进行了研究。在TDD系统中，BS的上行信道检测和下行发送具有一定的时间间隔。此间隔内的信道时变会导致上下行信道的互易性丧失。针对此问题，提出了一种基于变换域显著分量检测和预测的互易性补偿算法。首先将初步的LS信道估计所得到的导频信道CFR序列进行DFT转换到变换域，接着在变换域中使用MDL准则进行显著分量检测，再对每个显著分量使用基于修正协方差的AR模型进行预测以补偿由信道时变特性所致的信道互易性损失。所提算法有效地降低了信道估计和预测误差，弥补了由时变引起的信道非互易性所带来的系统容量损失，同时与频域直接预测方法相比，大大降低了系统复杂度。
4.对信道估计误差引起的信道互易性丧失进行了研究。在存在虚载波和非严格等距导频的TDD-MIMO-OFDM系统中，把初步LS估计得到的导频信道CFR序列通过IFFT转换到时域时，出现了严重的时域CIR能量泄漏，从而使得FFT插值算法等基于时域处理的算法不能使用，无法实现估计误差的抑制。针对此问题，提出了一种低复杂度的信道估计算法以对抗由信道估计误差导致的互易性损失。该算法首先通过频域线性插值对虚载波位置的CFR进行添加，然后重新选取严格等距子载波作为导频，并对此新的导频CFR序列进行IFFT变换到时域，然后对所得的时域CIR序列进行基于噪声门限的去噪，从而有效地抑制了信道估计误差，使信道互易性得到保持，弥补了由信道非互易所致的系统容量损失。
对以上方法都在理论分析的基础上通过仿真实验验证了其正确性和可行性。
In order to meet the increasing demands on higher rate and reliability of datatransmission，Multiple Input Multiple Output (MIMO) technique has been chosen asone of the indispensably basic techniques used in future wireless communicationsystems. The multiplexing gains and diversity gains obtained by MIMO techniqueprovide technical guarantee for higher data rate and reliability respectively. For thereason above, MIMO technique as well as some related techniques have been studiedand applied widely, including cooperative transmission and multi-antenna transmitpreprocessing.
Cooperative transmission is the natural extend of MIMO technique in single-antennaenvironment. Usually multi-antennas are not available for a cheap and small-sized userterminal, whereas several single-antenna user terminals can share their antennas to forma virtual MIMO system to reap the various benefits of MIMO technique. However, it isdifferent from the conventional MIMO systems that the distributed antennas in acooperative transmission system can’t achieve perfect synchronization, so that manytechniques which are suitable for conventional MIMO systems can’t be directlyemployed. Therefore, it’s necessary to study the cooperative transmission inasynchronous environment so as to solve this problem.
Another technique related to MIMO is multi-antennas transmit preprocessing. Withchannel state information (CSI) at transmitter, transmit preprocessing can significantlyimprove system performance. In FDD systems, the uplink and downlink use differentfrequency bands, which means no reciprocity between the two channels can be tookadvantage of, so that a mobile station (MS) can’t help sending CSI to base station (BS)via a feedback channel. It is not only a waste of system resource but also an increasingin system complexity. However, the uplink and downlink channels in a TDD systemshare the same frequency band, which means that there exists reciprocity between theproperties of the two channels. Thus, BS can perform transmit preprocessing accordingto the CSI obtained by means of uplink channel estimation rather than via a feedbackchannel, which is believed to be an inherent superiority of TDD systems. Unfortunately,various factors can damage the channel reciprocity; so that it’s a vital issue how tocompensate the influence resulted from such factors.
This dissertation mainly researches on the problems of asynchronous cooperativetransmission and channel reciprocity mentioned above. The author’s main contributionsare as follows.
1. Considering non-feasibility of OSTBC in asynchronous environment, the dissertationproposes an asynchronous cooperative transmission scheme based on single-carrierblock transmission. In the scheme, the relay nodes segment the signal received from thesource node into several blocks and retransmit them based on single-carrier blocktransmission mode according to the predefined cooperation rules. For the multipleblocks of received signal, the destination node performs FFT respectively, separates theoverlapped ones among them by means of linear combination, and then performsfrequency domain equalization, IFFT and symbol decision to recover the original data.The proposed scheme can solves the problems of orthogonality damage and channeldispersion resulted from the asynchronization, and can achieve full diversity gains.Based on this idea, a generalized space-time cooperative transmission code withmodified OSTBC which is suitable for asynchronous environment is presented. It cansolve the problem of non-feasibility of OSTBC in asynchronous environment andachieve full diversity gains.
2. In TDD systems with I/Q imbalance, the negative effect of I/Q imbalance will resultin the non-reciprocity between uplink and downlink channels, so as to degrade thesystem performance. Aiming at this problem, a compensation scheme is proposed,which is based on bidirectional channel estimations. Before starting formal datatransmission, BS and MS perform channel estimations to get the CSI of uplink anddownlink respectively, then MS send the downlink CSI to BS which is responsible forcalculating the calibration matrices used for BS and MS, and at last BS send MS’scalibration matrix to MS. In this way, BS and MS can use calibration matrices fortransmit preprocessing to maintain the channel reciprocity.
3. Time-variance property of wireless channel will lead to differences between uplinkand downlink CSI of TDD systems to damage the channel reciprocity. Aiming at thisproblem, a compensation method based on transform-domain detection and prediction isproposed. Firstly the channel frequency response (CFR) sequence of pilot channelsobtained by least square estimation is transformed into transform-domain by DFT, andthen significant components in transform-domain is picked up with minimumdescription length (MDL) criterion, and at last prediction is made for each component tocompensate channel time-variances. It is through transform domain processing that theproposed method effectively reduces the estimation and prediction errors, and henceremedies the system capacity loses. Meanwhile the proposed method greatly lowers thesystem complexity compared with the frequency domain prediction method.
4. Channel estimation error is another causation of channel non-reciprocity. InOFDM systems with virtual subcarriers and non-strictly equidistant pilots, seriousenergy leakage of channel impulse response (CIR) emerges when the pilots’ CFRsequence obtained by LS criterion is transformed into time domain using IFFT, so thatthe algorithms based on time domain processing including FFT interpolation can’t beused to suppress estimation error. Aiming at this problem, a low complexity channelestimation method is proposed to compensate the channel non-reciprocity caused byestimation error. Firstly the CFRs of virtual subcarriers are added by linear interpolation,then strictly equidistant subcarriers are chosen as new pilot channels for CIR recoveryby IFFT, and at last a threshold-based denoising is performed to the recovered CIRsequence to suppress the estimation error and compensate the channel non-reciprocity.
All the proposed methods mentioned above are validated for their correctness andfeasibility by means of theoretical analysis and simulation experiments.
引文
[1]李建东,杨家玮.个人通信[M].北京:人民邮电出版社,1998.
[2]张辉,曹丽娜.现代通信原理与技术[M].西安:西安电子科技大学出版社,2002.
[3] Global mobile Suppliers Association(GSA). GSM/3G and LTE Market Update March3,2011.http://www.gsacom.com/downloads/pdf/GSM_3G_Market_Update.php4
[4]3rd Generation Partnership Project. Technical Specification Group Radio Access Network;Physical layer aspects for evolved Universal Terrestrial Radio Access (UTRA),3GPP Std. TR25.814v.7.0.0,2006.
[5] IEEE. Air Interface for Fixed and Mobile Broadband Wireless Access Systems, IEEE Std.802.16e-2005, Feb.2006.
[6] Telatar E. Capacity of Multi-antenna Gaussian Channels [J]. Europe Transaction onTelecommunications (ETT),1999,10(6):585-595.
[7] Foschini G, Gans M. On Limits of Wireless Communications in a Fading Environment WhenUsing Multiple Antennas [J]. Wireless Personal Communications,1998, Vol.6:311-335.
[8] Cimini L Jr. Analysis and Simulation of a Digital Mobile Channel Using OrthogonalFrequency Division Multiplexing [J]. IEEE Transactions on Communications,1985,33(7):665-675.
[9] Tarokh V, Jafarkhani H, Calderbank A R. Space-Time Block Codes From Orthogonal Designs[J]. IEEE Transaction on Information Theory,1999,45(5):1456-1467.
[10] Vu M, Paulraj A. MIMO Wireless Linear Precoding Using CSIT to Improve LinkPerformance [J]. IEEE Signal Processing Magazine,2007,24(9):86-105.
[11] Glenn S. Smith. A Direct Derivation of a Single-antenna Reciprocity Relation for The TimeDomain [J]. IEEE Transactions on Antennas and Propagation,2004,52(6):1568-1577.
[12] Liu C. Impacts of I/Q Imbalance on QPSK-OFDM QAM Detection [J]. IEEE Transactions onConsumer Electronics,1998,44(3):984-989.
[13] Parsons J D. The Mobile Radio Propagation Channel [M].2ndedition. West Sussex: JohnWiley&Sons Ltd,2000.
[14] Lee W C Y. Mobile Communications Engineering [M]. New York, USA: McGraw HillPublications,1985.
[15] Cover T, Gamal A E. Capacity Theorems for the Relay Channel [J]. IEEE Transactions onInformation Theory,1979.25(5):572-584.
[16] Sendonaris A, Erkip E, Aazhang B. User Cooperation Diversity Part I: System Description [J].IEEE Transaction on Communications,2003,51(11):1927-1938.
[17] Laneman J N, Wornell G W, Tse D N C. Cooperative Diversity in Wireless Networks:Efficient Protocols and Outage Behavior. IEEE Transaction on Information Theory,2004,50(12):3062-3080
[18] Hunter T E, Nosratinia A. Diversity through Coded Cooperation [J]. IEEE Transactions onWireless Communications,2006,5(2):283-289.
[19] H st-Madsen A, Zhang J. Capacity Bounds and Power Allocation for Wireless RelayChannels [J]. IEEE Transactions on Information Theory,2005,51(6):2020-2040.
[20] Yang S, Belfiore J C. Towards the Optimal Amplify-and-Forward Cooperative DiversityScheme [J]. IEEE Transactions on Information Theory,2007,53(9):3114-3126.
[21] Sneesens H H, Vandendorpe L. Soft Decode and Forward Improves CooperativeCommunications [C]//IEEE International Workshop on Computational Advances inMulti-Sensor Adaptive Processing. IEEE,2005:157-160.
[22] Bao X K, Li J. Decode-Amplify-Forward (DAF): A New Class of Forwarding Strategy forWireless Relay Channels [C]//IEEE6th Workshop on Signal Processing Advances in WirelessCommunications. IEEE,2005:816-820.
[23] Bouanen S, Boujemaa H, Ajib W. Threshold-based Adaptive Decode-Amplify-ForwardRelaying Protocol for Cooperative Systems[C]//7th International Wireless Communicationsand Mobile Computing Conference. IEEE,2011:725-730.
[24] Rankov B, Wittneben A. Spectral Efficient Protocol for Half-Duplex Fading Relay Channel[J]. IEEE Journal on Selected Areas in Communications,2007,25(2):379-389.
[25] Popovski P, Yomo H. Wireless Network Coding by Amplify-And-Forward for Bi-directionalTraffic Flow [J]. IEEE Communication Letters,2007,11(1):16-18.
[26] Bletsas A, Khisti A, Reed D P, et al. A Simple Cooperative Diversity Method Based onNetwork Path Selection [J]. IEEE Journal on Selected Areas in Communications,2006,24(3):659-672.
[27] Bletsas A, Hyundong S, Win M Z. Cooperative Communications withOutage-Optimal Opportunistic Relaying [J]. IEEE Transactions on Wireless Communications,2007,6(9):3450-3460.
[28] Bletsas A, Khisti A, Win M Z. Opportunistic Cooperative Diversity with Feedbackand Cheap Radios [J]. IEEE Transactions on Wireless Communications,2008,7(5):1823-1827.
[29] Ikki S S, Ahmed M H. On the Performance if Cooperative-Diversity Networks with the NthBest-Relay Selection Scheme [J]. IEEE Transactions on Communications,2010,58(11):3062-3069.
[30] Jing Y D, Jafarkhani H. Single and Multiple Relay Selection Schemes and Their AchievableDiversity Orders [J]. IEEE Transactions on Wireless Communications,2009,8(3):1414-1423.
[31] Li Y, Yin Q, Xu W, et al. On the Design of Relay Selection Strategies in RegenerativeCooperative Networks with Outdated CSI [J]. IEEE Transactions on WirelessCommunications,2011,10(9):3086-3097.
[32] Caleb K L, Sriram V, Robert W H. Relay Subset Selection in Wireless Networks Using PartialDecode-and-Forward Transmission [J]. IEEE Transactions on Vehicular Technology,2009,58(2):692-704.
[33] Gayan A, Masoud A, Chintha T. Output-Threshold Multiple-Relay-Selection Scheme forCooperative Wireless Networks [J]. IEEE Transactions on Vehicular Technology,2010,59(6):3091-3097
[34] Luo J, Blum R S, Cimini L, et al. Decode-and-Forward Cooperative Diversity with PowerAllocation in Wireless Networks [J]. IEEE Transactions on Wireless Communications,2007,6(3):793-799.
[35] Blum R S, Cimini L, Greenstein L, et al. Power Allocation in a Transmit Diversity Systemwith Mean Channel Gain Information [J]. IEEE Communications Letters,2005,9(7):616-618.
[36] Ren S, Letaief K. Maximizing the Effective Capacity for Wireless Cooperative RelayNetworks with QoS Guarantees [J]. IEEE Transactions on Communications,2009,57(7):2148-2159.
[37] Kim S, Wang X, Madihian M. Optimal Resource Allocation in Multi-hop OFDMA WirelessNetworks with Cooperative Relay [J]. IEEE Transactions on Wireless Communications,2008,7(5):1833-1838.
[38] Shim W, Han Y, Kim S. Fairness-Aware Resource Allocation in a Cooperative OFDMAUplink System [J]. IEEE Transactions on Vehicular Technology,2010,59(2):932-939.
[39] Yang N, Tian H, Chen S, et al. An Adaptive Frame Resource Allocation Strategy forTDMA-Based Cooperative Transmission [J]. IEEE Communications Letters,2007,11(5):417-419.
[40] Edmund M Y, Randall A B. Throughput Optimal Control of Cooperative Relay Networks [J].IEEE Transactions on Information Theory,2007,53(10):3827-3833.
[41] Chen W, Dai L, Letaief K B, et al. A Unified Cross-Layer Framework for Resource Allocationin Cooperative Networks [J]. IEEE Transactions on Wireless Communications,2008,7(8):3000-3012.
[42] Huang J W, Han Z, Chiang M, et al. Auction-Based Resource Allocation for CooperativeCommunications [J]. IEEE Journal on Selected Areas in Communications,2008,26(7):1226-1237.
[43] Wang B, Han Z, Liu K J R. Distributed Relay Selection and Power Control for MultiuserCooperative Communication Networks Using Stackelberg Game [J]. IEEE Transactions onMobile Computing,2009,8(7):975-990.
[44] Laneman J N, Wornell G W. Distributed Space-Time-Coded Protocols for ExploitingCooperative Diversity in Wireless Networks [J]. IEEE Transactions on Information Theory,2003,49(10):2415-2425.
[45] Nabar R U, Helmut B, Kneubühler F W. Fading Relay Channels: Performance Limits andSpace-Time Signal Design [J]. IEEE Journal on Selected Areas in Communications,2004,22(6):1099-1109.
[46] Gamal H E, Hammons A R, Stefanov A. Space-Time Overlays for Convolutionally CodedSystems [J]. IEEE Transactions on Communications,2003,51(9):1603-1612.
[47] Anghel P A, Kaveh M. On the Performance of Distributed Space-Time Coding Systems withOne and Two Non-Regenerative Relays [J]. IEEE Transactions on Wireless Communications,2006,5(3):682-692.
[48] Chen S, Wang W, Zhang X, et al. Performance Analysis of OSTBC Transmission inAmplify-and-Forward Cooperative Relay Networks [J]. IEEE Transactions on VehicularTechnology,2010,59(1):105-113.
[49] Sirkeci M B, Scaglione A. Randomized Space-Time Coding for Distributed CooperativeCommunication [J]. IEEE Transactions on Signal Processing,2007,55(10):5003-5017.
[50] Vajapeyam M, Mitra U. Performance Analysis of Distributed Space-Time Coded Protocols forWireless Multi-Hop Communications [J]. IEEE Transactions on Wireless Communications,2010,9(1):122-133.
[51] Lai H, Liu K. Space-Time Network Coding [J]. IEEE Transactions On Signal Processing,2011,59(4):1706-1718.
[52] Wei S, Goeckel D, Valenti M. Asynchronous Cooperative Diversity [J]. IEEE Transactions onWireless Communications,2006,5(6):1547-1557.
[53] Wei S. Diversity-Multiplexing Tradeoff of Asynchronous Cooperative Diversity in WirelessNetworks [J]. IEEE Transactions on Information Theory,2007,53(11):4150-4172.
[54] Li X. Space-Time Coded Multi-Transmission among Distributed Transmitters without PerfectSynchronization [J]. IEEE Signal Processing Letters,2004,11(12):948-951.
[55] Guo X, Xia X. Distributed Linear Convolutive Space-Time Codes for AsynchronousCooperative Communication Networks [J]. IEEE Transactions on Wireless Communications,2008,7(5):1857-1861.
[56] Li Y, Xia X. A Family of Distributed Space-Time Trellis Codes with AsynchronousCooperative Diversity [J]. IEEE Transactions on Communications,2007,55(4):790-800.
[57] Shang Y, Xia X. Shift-full-rank matrices and applications in space-time trellis codes for relaynetworks with asynchronous cooperative diversity [J]. IEEE Transactions on InformationTheory,2006,52(7):3153-3167.
[58] Li Z, Xia X. A Simple Alamouti Space-Time Transmission Scheme for AsynchronousCooperative Systems [J]. IEEE Signal Processing Letters,2007,14(11):804-807.
[59] Li Z, Xia X. A Distributed Differentially Encoded OFDM Scheme for AsynchronousCooperative Systems with Low Probability of Interception [J]. IEEE Transactions on WirelessCommunications,2009,8(7):3372-3379.
[60] Wu N, Gharavi H. Asynchronous Cooperative MIMO Systems Using a Linear DispersionStructure [J]. IEEE Transactions on Vehicular Technology,2010,59(2):779-787.
[61] Fritz Gfeller F, Hirt W. A Robust Wireless Infrared System with Channel Reciprocity [J].IEEE Communications Magazine,1998,36(12):100-106.
[62] Medard M. The Effect upon Channel Capacity in Wireless Communications of Perfect andImperfect Knowledge of the Channel [J]. IEEE Transactions on Information Theory,2000,46(3):933-946.
[63] Choi J. Performance Analysis for Transmit Antenna Diversity with/without ChannelInformation [J]. IEEE Transactions on Vehicular Technology,2002,51(1):101-113.
[64] Paris J F, Aguayo T M C, Entrambasaguas J T, Impact of Imperfect Channel Estimation onAdaptive Modulation Performance in Flat Fading [J]. IEEE Transactions on Communications,2004,52(5):716-720.
[65] Wang C, Ross D M. Adaptive Downlink Multi-User MIMO Wireless Systems for CorrelatedChannels with Imperfect CSI [J]. IEEE Transactions on Wireless Communications,2006,5(9):2435-2446.
[66] Helstrom C W. Distribution of the Envelope of a Sum of Random Sine Waves and GaussianNoise [J]. IEEE Transactions on Aerospace and Electronic Systems,2002,35(2):594-601.
[67] Chen M, Ekman T, Viberg M. New Approaches for Channel Prediction Based on SinusoidalModeling [J]. EURASIP Journal on Advances in Signal Processing, Vol.2007, Article ID:49393.
[68] Kim K T, Seo D K, Kim H T. Efficient Radar Target Recognition Using the MUSICAlgorithm and Invariant Features [J]. IEEE Transactions on Antennas and Propagation,2002,50(3):325-337.
[69] Abeida H, Delmas J P. MUSIC-like Estimation of Direction of Arrival for NoncircularSources [J]. IEEE Transactions on Signal Processing,2006,54(7):2678-2690.
[70] Yuen N, Friedlander B. Asymptotic Performance Analysis of ESPRIT, Higher Order ESPRIT,and Virtual ESPRIT Algorithms [J]. IEEE Transactions on Signal Processing,1996,44(10):2537-2550.
[71] Shahbazpanahi S, Valaee S, Bastani M H. Distributed Source Localization Using ESPRITAlgorithm [J]. IEEE Transactions on Signal Processing,2001,49(10):2169-2178.
[72] Jakobsson A, Swindlehurst A, Stoica P. Subspace-based Estimation of Time Delays andDoppler Shifts [J]. IEEE Transactions on Signal Processing,1998,46(9):2472-248.
[73] Yang B, Letaief K B, Cheng R, et al. Channel Estimation for OFDM Transmission inMultipath Fading Channels Based on Parametric Channel Modeling [J]. IEEE Transactions onCommunications,2001,49(3):467-479.
[74] Thoma R S, Hampicke D, Richter A, et al. Identification of Time-Variant Directional MobileRadio Channels [J]. IEEE Transactions on Instrumentation and Measurement,2000,49(2):357-364.
[75] Zwick T, Hampicke D, Richter A, et al. A Novel Antenna Concept for Double-DirectionalChannel Measurements [J]. IEEE Transactions on Vehicular Technology,2004,53(2):527-537.
[76] Picheral J, Spagnolini U. Angle and Delay Estimation of Space-Time Channels forTD-CDMA Systems [J]. IEEE Transactions on Wireless Communications,2004,3(3):758-769.
[77] Wong I C, Evans B L. Sinusoidal Modeling and Adaptive Channel Prediction in MobileOFDM Systems [J]. IEEE Transactions on Signal Processing,2008,56(4):1601-1615.
[78] Tan S and Hirose A. Low-Calculation-Cost Fading Channel Prediction Using ChirpZ-Transform [J]. Electronics Letters,2009,45(8):418-420.
[79] Heidari A, Khandani A K, McAvoy D. Adaptive Modeling and Long-Range Prediction ofMobile Fading Channels [J]. IET Communications,2010,4(1):39-50.
[80] Falahati S, Svensson A, Ekman T, et al. Adaptive Modulation Systems for Predicted WirelessChannels [J]. IEEE Transactions on Communications,2004,52(2):307-316.
[81] Zhou S, Giannakis G B. How Accurate Channel Prediction Needs to be forTransmit-Beamforming With Adaptive Modulation Over Rayleigh MIMO Channels?[J].IEEE Transactions on Wireless Communications,2004,3(4):1285-1294.
[82] Schafhuber D, Matz G. MMSE and Adaptive Prediction of Time-Varying Channels for OFDMSystems [J]. IEEE Transactions on Wireless Communications,2005,4(2):593-602.
[83] Akhtman J, Hanzo L. Channel Impulse Response Tap Prediction for Time-Varying WirelessChannels [J]. IEEE Transactions on Vehicular Technology,2007,56(5):2767-2769.
[84] Heo J, Wang Y P, Chang K H. A Novel Two-Step Channel-Prediction Technique forSupporting Adaptive Transmission in OFDM/FDD System [J]. IEEE Transactions onVehicular Technology,2008,57(1):188-193.
[85] Khrwat A S, Sharif B S, Tsimenidis C C, et al. Channel Prediction for Precoded SpatialMultiplexing Multiple-Input Multiple-Output Systems in Time-Varying Fading Channels [J].IET Signal Processing,2009,3(6):459-466.
[86] Pham V H, Wang X, Nadeau J. Long Term Cluster-Based Channel Envelope and PhasePrediction for Dynamic Link Adaptation [J]. IEEE Communications Letters,2011,15(7):713-715.
[87] Oien G E, Holm H, Hole K J. Impact of Channel Prediction on Adaptive Coded ModulationPerformance in Rayleigh Fading [J]. IEEE Transactions on Vehicular Technology,2004,53(3):758-769.
[88] Sun J, Zhang T, Liu F. Nonlinear Prediction of Mobile-Radio Fading Channel UsingRecurrent Least Squares Support Vector Machines and Embedding Phase Space [C]//2004International Conference on Communications, Circuits and Systems (ICCCAS2004). IEEE,2004:282-286.
[89] Fernández M, Cumplido M, García J, et al. SVM Multiregression for Nonlinear ChannelEstimation in Multiple-Input Multiple-Output Systems [J]. IEEE Transactions on SignalProcessing,2004,52(8):2298-2307.
[90] Liu C. Impacts of I/Q Imbalance on QPSK-OFDM QAM Detection [J]. IEEE Transactions onConsumer Electronics,1998,44(3):984-989.
[91] Valkama M, Renfors M, Koivunen V. Advanced Methods for I/Q Imbalance Compensation inCommunication Receivers [J]. IEEE Transactions on Signal Processing,2001,49(10):2335-2344.
[92] Abidi A A. Direct-Conversion Radio Transceivers for Digital Communications [J]. IEEEJournal of Solid-State Circuits,1995,30(12):1399-410.
[93] Tubbax J, Come B, Van der Perre L, et al. Compensation of IQ Imbalance and Phase Noise inOFDM Systems [J]. IEEE Transactions on Wireless Communications,2005,4(3):872-877.
[94] Tarighat A, Bagheri R, Sayed A H. Compensation Schemes and Performance Analysis of IQImbalances in OFDM Receivers [J]. IEEE Transactions on Signal Processing,2005,53(8):3257-3268.
[95] Tarighat A, Sayed A H. MIMO OFDM Receivers for Systems with IQ Imbalances [J]. IEEETransactions on Signal Processing,2005,53(9):3582-3596.
[96] Barhumi I, Moonen M. IQ-Imbalance Compensation for OFDM in the Presence of IBI andCarrier-Frequency Offset [J]. IEEE Transactions on Signal Processing,2007,55(1):256-266.
[97] Horlin F, Bourdoux A, Van der Perre L. Low-Complexity EM-based Joint Acquisition of theCarrier Frequency Offset and IQ Imbalance [J]. IEEE Transactions on WirelessCommunications,2008,7(6):2212-2220.
[98] He L, Ma S, Wu Y, et al. Pilot-Aided IQ Imbalance Compensation for OFDM SystemsOperating Over Doubly Selective Channels [J]. IEEE Transactions on Signal Processing,2011,59(5):2223-2233.
[99] Mattera D, Paura L, Sterle F. MMSE WL Equalizer in Presence of Receiver IQ Imbalance [J].IEEE Transactions on Signal Processing,2008,56(4):1735-1740.
[100] Yoshida Y, Hayashi K, Sakai H, et al. Analysis and Compensation of Transmitter IQImbalances in OFDMA and SC-FDMA Systems [J]. IEEE Transactions on Signal Processing,2009,57(8):3119-3129.
[101] Inamori M, Bostamam A M, Sanada Y, et al. IQ Imbalance Compensation Scheme in thePresence of Frequency Offset and Dynamic DC Offset for a Direct ConversionReceiver[J].IEEE Transactions on Wireless Communications,2009,8(5):2214-2220.
[102] Zou Q, Tarighat A, Sayed A H. Joint Compensation of IQ Imbalance and Phase Noise inOFDM Wireless Systems [J]. IEEE Transactions on Communications,2009,57(2):404-414.
[103] Chiu Y J, Hung S P. Estimation Scheme of the Receiver IQ Imbalance under CarrierFrequency Offset in Communication System [J]. IET Communications,2010,4(11):1381-1388.
[104] Gu C F, Law C L, Wu W. Time Domain IQ Imbalance Compensation for Wideband WirelessSystems[J]. IEEE Communications Letters,2010,14(6):539-541.
[105] Tarighat A, Sayed A H. Joint Compensation of Transmitter and Receiver Impairments inOFDM Systems [J]. IEEE Transactions on Wireless Communications,2007,6(1):240-247.
[106] Schenk T C W. Performance Analysis of Zero-IF MIMO OFDM Transceivers with IQImbalance [J]. Journal of Communications,2007,2(7):9-19.
[107] Tandur D, Moonen M. Joint Adaptive Compensation of Transmitter and Receiver IQImbalance under Carrier Frequency Offset in OFDM-Based Systems [J]. IEEE Transactionson Signal Processing,2007,55(11):5246-5252.
[108] Zou Y, Valkama M, Renfors M. Analysis and Compensation of Transmitter and Receiver I/QImbalances in Space-Time Coded Multiantenna OFDM Systems [J]. EURASIP JournalonWireless Communications and Networking, Vol.2008, Article ID:391025.
[109] Morelli M, Mengali U. A Comparison of Pilot-Aided Channel Estimation Methods for OFDMSystems [J]. IEEE Transactions on Signal Processing,2001,49(12):3065-3073.
[110] Song S, Singer A C. Pilot-Aided OFDM Channel Estimation in the Presence of the GuardBand [J]. IEEE Transactions on Communications,2007,55(8):1459-1465.
[111] Shin C, Heath R W, Powers E J. Blind Channel Estimation for MIMO-OFDM Systems [J].IEEE Transactions on Vehicular Technology,2007,56(2):670-685.
[112] Beres E, Adve R. Blind Channel Estimation for Orthogonal STBC in MISO Systems, IEEETransactions on Vehicular Technology,2007,56(4):2042-2050.
[113] Han B, Gao X, You X, et al. An Iterative Joint Channel Estimation and Symbol DetectionAlgorithm Applied in OFDM System with High Data to Pilot Power Ratio[C]//IEEEInternational conference of Communications. IEEE,2003:2076-2080.
[114] IEEE. IEEE Standard for Information technology--Telecommunications and InformationExchange between Systems--Local and Metropolitan Area Networks--Specific RequirementsPart11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)Specifications Amendment5: Enhancements for Higher Throughput[S]. IEEE,2009.
[115] Hsieh M, We C. Channel Estimation for OFDM Systems based on Comb-Type PilotArrangement in Frequency Selective Fading Channels [J]. IEEE Transactions on ConsumerElectronics,1998,44(1):217-225.
[116] Coleri S, Ergen M, Puri A, et al. Channel Estimation Techniques Based on Pilot Arrangementin OFDM Systems [J]. IEEE Transactions on Broadcasting,2002,48(3):223-229.
[117] Yang H. A Road to Future Broadband Wireless Access: MIMO-OFDM-Based Air Interface [J].IEEE Communications Magazine,2005,43(1):53-60.
[118] Hoeher P, Kaiser S, Robertson I. Two-Dimensional Pilot-Symbol-Aided Channel Estimationby Wiener Filtering [C]//International Conference of Acoustics Speech and Signal Processing(ICASSP). IEEE,1997:1845-1848.
[119] Qiao Y, Yu S, Su P, et al. Research on an Iterative Algorithm of LS Channel Estimation inMIMO OFDM Systems [J]. IEEE Transactions on Broadcasting,2005,51(1):149-153.
[120] Lim C H, Han D S. Robust LS Channel Estimation with Phase Rotation for Single FrequencyNetwork in OFDM [J]. IEEE Transactions on Consumer Electronics,2006,52(4):1173-1178.
[121] Negi R, Cioffi J. Pilot Tone Selection for Channel Estimation in a Mobile OFDM System [J].IEEE Transactions on Consumer Electronics,1998,44(3):1122-1128.
[122] Barhumi I, Leus G, Moonen M. Optimal Training Design for MIMO OFDM Systems inMobile Wireless Channels [J]. IEEE Transactions on Signal Processing,2003,51(6):1615-1624.
[123] Edfors O, Sandell M, Van de Beek J J, et al. Analysis of DFT-Based Channel Estimators forOFDM [J]. Wireless Personal Communications,2000,12(1):55-70.
[124] Larsson E G, Liu G, Li J, et al. Joint Symbol Timing and Channel Estimation for OFDMBased WLANs [J]. IEEE Communications Letters,2001,5(8):325-327.
[125] Cheng Z, Dahlhaus D. Time Versus Frequency Domain Channel Estimation for OFDMSystems with Antenna Arrays[C]//6th International Conference on Signal Processing. IEEE,2002:1340-1343.
[126] Kang S G, Ha Y M, Joo E K. A Comparative Investigation on Channel Estimation Algorithmsfor OFDM in Mobile Communications [J]. IEEE Transactions on Broadcasting,2003,49(2):142-149.
[127] Morelli M, Mengali U. A Comparison of Pilot-Aided Channel Estimation Methods for OFDMSystems [J]. IEEE Transactions on Signal Processing,2001,49(12):3065-3073.
[128] Edfors O, Sandell M, Van de Beek J J, et al. OFDM Channel Estimation by Singular ValueDecomposition [J]. IEEE Transactions on Communications,1998,46(7):931-939.
[129] Gong Y, Letaief K B. Low Complexity Channel Estimation for Space-Time Coded WidebandOFDM Systems [J]. IEEE Transactions on Wireless Communications,2003,2(5):876-882.
[130] Ozdemir M K, Arslan H, Arvas E. Towards Real-Time Adaptive Low-Rank LMMSE ChannelEstimation of MIMO-OFDM Systems [J]. IEEE Transactions on Wireless Communications,2006,5(10):2675-2678.
[131] Li Y, Cimini L J, Sollenberger N R. Robust Channel Estimation for OFDM Systems withRapid Dispersive Fading Channels [J]. IEEE Transactions on Communications,1998,46(7):902-915.
[132] Tolochko I, Faulkner M. Real Time LMMSE Channel Estimation for Wireless OFDMSystems with Transmitter Diversity [C]//IEEE56th Conference of Vehicular Technology.IEEE,2002:1555-1559.
[133] Zhao Y, Huang A. A Novel Channel Estimation Method for OFDM Mobile CommunicationSystems Based on Pilot Signals and Transform-Domain Processing[C]//IEEE47th VehicularTechnology Conference. IEEE,1997:2089-2093.
[134] Yeh Y H, Chen S G. DCT-Based Channel Estimation for OFDM Systems [C]//2004International Conference of Communications. IEEE,2004:2442-2446.
[135] Feng S, Wu W. DCT-based Channel Estimation Method for MIMO-OFDM Systems UsingPhase Shifted Pilot Sequences [C]//Wireless Communications and Networking Conference,2007(WCNC2007). IEEE,2007:12-15.
[136] Li Y, Sollenberger N R. Clustered OFDM with Channel Estimation for High Rate WirelessData [J]. IEEE Transactions on Communications,2001,49(12):2071-2076.
[137]郑宝玉,张继东.基于小波去噪的OFDM信道估计新方法[J].电子与信息学报,2006,28(3):415-418.
[138] Lee Y S, Shin H C, H N. Channel Estimation Based on a Time-Domain Threshold for OFDMSystems [J]. IEEE Transactions on Broadcasting,2009,55(3):656-662.
[139] Kwak K, Lee S, Kim J, et al. A New DFT-Based Channel Estimation Approach for OFDMwith Virtual Subcarriers by Leakage Estimation [J]. IEEE Transactions on WirelessCommunications,2008,7(6):2004-2008.
[140] Sun W, Li L. A Time Domain Iteration-based Channel Estimation Method in OFDM Systemswith Null Subcarriers [C]//2010IEEE71stVehicular Technology Conference (VTC2010-Spring). IEEE,2010:1-5
[141] Seo J, Jang S, Yang J, et al. Analysis of Pilot-Aided Channel Estimation with OptimalLeakage Suppression for OFDM Systems [J]. IEEE Communications Letters,2010,14(9):809-811.
[142] Carbonelli C, Vedantam S, Mitra U. Sparse Channel Estimation with Zero Tap Detection [J].IEEE Transactions on Wireless Communications,2007,6(5):1743:1753.
[143] Li W, Preisig J C.Estimation of Rapidly Time-Varying Sparse Channels [J]. IEEE Journal ofOceanic Engineering,2007,32(4):927-939.
[144] Donoho D. Compressed Sensing [J]. IEEE Transactions on Information Theory2006,52(4):1289-1306.
[145] Khojastepour M A, Gomadam K, Wang X. Pilot-Assisted Channel Estimation for MIMOOFDM Systems Using Theory of Sparse Signal Recovery[C]//IEEE International Conferenceon Acoustics, Speech and Signal Processing. IEEE,2009:2693-2696.
[146] Huang J, Huang J, Berger C R. Iterative Sparse Channel Estimation and Decoding forUnderwater MIMO-OFDM [J]. EURASIP Journal on Advances in Signal Processing, Volume2010, Article ID:460379,11pages.
[147] Soltanolkotabi M, Amini A, Marvasti F. OFDM Channel Estimation Based on AdaptiveThresholding for Sparse Signal Detection[C]//17th European Signal Processing Conference.EUSIPCO,2009:1685-1689.
[148] Maechler P, Greisen P, Felber N, et al. Matching Pursuit: Evaluation and Implementation forLTE Channel Estimation[C]//Proceedings of2010IEEE International Symposium on Circuitsand Systems.IEEE,2010:589-592.
[149] Cepeda R, Fitton M, Nix A. The Performance of Robust Adaptive Modulation over WirelessChannels with Non Reciprocal Interference[C]//IEEE55th Vehicular Technology Conference.IEEE,2002:1497-1501.
[150] Tolli A, Cndreanu M. Compensation of Interference Non-Reciprocity in Adaptive TDDMIMO-OFDM Systems[C]//15th IEEE International Symposium on Personal, Indoor andMobile Radio Communications. IEEE,2004:859-863.
[151] Yomo H, Reynisson R V. A Power Control Method for Channel Non-ReciprocityCompensation in Hybrid Duplexing [C]//IEEE17th International Symposium on Personal,Indoor and Mobile Radio Communications. IEEE,2006:1-5.
[152] Tolli A, Codreanu M, Juntti M. Suppression of Non-reciprocal Interference in AdaptiveCellular Systems [C]//IEEE61stVehicular Technology Conference. IEEE,2005:1072-1076.
[153] Tolli A, Codreanu M, Juntti M. Compensation of Non-Reciprocal Interference in AdaptiveMIMO-OFDM Cellular Systems.[J]. IEEE Transactions on Wireless Communications,2007,6(2):545-555.
[154] Stoica P, Lindskog E. Space-Time Block Coding for Channels With Intersymbol Interference
[C]//Proceedings of the35thAsilomar Conference on Signal, Systems and Computers. IEEE,2001:252-256.
[155] Clark M V. Adaptive Frequency-Domain Equalization and Diversity Combining for BroadbandWireless Communications [J]. IEEE Journal on elected Areas in Communications,1998,16(8):1385-1395.
[156] Falconer D, Ariyavisitakul S L, Benyamin-Seeyar A, et al. Frequency Domain Equalization forSingle-Carrier Broadband Wireless Systems [J]. IEEE Communication Magazine,2002,40(4):58-66.
[157] Pancaldi F, Vitetta G M. Block Channel Equalization in the Frequency Domain [J]. IEEETransactions on Communications,2005,53(3):463-471.
[158] Wang Z, Ma X, Giannakis G B. OFDM or Single-Carrier Block Transmissions?[J]. IEEETransactions on Communications,2004,52(3):380-394.
[159] Ohno S. Performance of Single-Carrier Block Transmissions over Multipath Fading Channelswith Linear Equalization [J]. IEEE Transactions on Signal Processing,2006,54(10):3678:3687.
[160] Dhahir N A. Single-Carrier Frequency-Domain Equalization for Space-Time Block-CodedTransmissions over Frequency-Selective Fading Channels [J]. IEEE Communications Letters,2001,5(7):304-306.
[161] Zheng L, Tse D N C. Diversity and Multiplexing: A Fundamental Tradeoff inMultiple-Antenna Channels [J]. IEEE Transactions on Information Theory,2003,49(5):1073:1096.
[162] Willink T J. Efficient adaptive SVD Algorithm for MIMO Applications [J]. IEEETransactions on Signal Processing,2008,56(2):615-622.
[163] Andersen J B. Array Gain and Capacity for Known Random Channels with MultipleElements Arrays at Both Ends [J]. IEEE Journal on Selected Areas in Communications,2000,18(11):2172-2178.
[164] Cover T M, Thomas J A. Elements of Information Theory [M]. New York: Wiley,1991.
[165] Lebrun G, Gao J, Faulker M. MIMO Transmission Over A Time-Varying Channel UsingSVD [J]. IEEE Transaction on Wireless Communications,2005,4(2):757-764.
[166] Yen C P. Advanced Receiver Signal Processing Functions for Next Generation WirelessCommunication Systems [D]. Polytechnic University, New York,2007.
[167] Jakes W C. Microwave Mobile Communications [M]. Virginia: Wiley-IEEE Press,1994.
[168] Duel-Hallen A, Hu S, Hallen, H. Long-Range Prediction of Fading Signals [J]. IEEE SignalProcessing Magazine,2000,17(3):62-75.
[169] Matthias Patzold. Mobile Fading Channels [M]. West Sussex: John Wiley&Sons Ltd,2002.
[170]3GPP.3GPP TR25.996V6.1.0. Spatial Channel model for Multiple Input Multiple Output(MIMO) Simulations (Release6)[S].3GPP,2003.
[171] Semmelrodt S, Kattenbach R. Inversigation of Different Fading Forecast Schemes for FlatFading Radio Channels[C]//IEEE2edInternational Conference of Vehicular Technology.IEEE,2003:149-153.
[172]石峰,胡登鹏,王晨,等. OFDM系统中基于补零DFT信道插值算法的研究[J].国防科技大学学报,2010,32(5):98-104.
[173] Wax M, Kailath T. Detection of Signals by Information Theoretic Criteria [J]. IEEE Trans onAcoustics, Speech, and Signal Processing,1985,33(2):387-389.
[174] Nuttal A H. Spectral Analysis of a Univariate Process with Bad data Points, via MaximumEntropy and Linear Predictive Techniques. Tech. Rep. TR-5303, Naval Underwater SystemCenter, New London, Conn., Mar.26,1976.
[175] Konstantinidis S, Freear S. Performance Analysis of Tikhonov Regularized LS ChannelEstimation for MIMO OFDM Systems with Virtual Carriers [J]. Wireless PersonalCommunications, December17,2010, published on line.
[176] Zyren J. Overview of the3GPP Long Term Evolution Physical Layer. White Paper, FreescaleSemiconductor, Inc.,2007.
[177] Kang Y, Kim K, Park H. Effficient DFT-based Channel Estimation for OFDM System onMultipath Channels [J]. IET Communications,2007,1(2):197-202.
[2]张辉,曹丽娜.现代通信原理与技术[M].西安:西安电子科技大学出版社,2002.
[3] Global mobile Suppliers Association(GSA). GSM/3G and LTE Market Update March3,2011.http://www.gsacom.com/downloads/pdf/GSM_3G_Market_Update.php4
[4]3rd Generation Partnership Project. Technical Specification Group Radio Access Network;Physical layer aspects for evolved Universal Terrestrial Radio Access (UTRA),3GPP Std. TR25.814v.7.0.0,2006.
[5] IEEE. Air Interface for Fixed and Mobile Broadband Wireless Access Systems, IEEE Std.802.16e-2005, Feb.2006.
[6] Telatar E. Capacity of Multi-antenna Gaussian Channels [J]. Europe Transaction onTelecommunications (ETT),1999,10(6):585-595.
[7] Foschini G, Gans M. On Limits of Wireless Communications in a Fading Environment WhenUsing Multiple Antennas [J]. Wireless Personal Communications,1998, Vol.6:311-335.
[8] Cimini L Jr. Analysis and Simulation of a Digital Mobile Channel Using OrthogonalFrequency Division Multiplexing [J]. IEEE Transactions on Communications,1985,33(7):665-675.
[9] Tarokh V, Jafarkhani H, Calderbank A R. Space-Time Block Codes From Orthogonal Designs[J]. IEEE Transaction on Information Theory,1999,45(5):1456-1467.
[10] Vu M, Paulraj A. MIMO Wireless Linear Precoding Using CSIT to Improve LinkPerformance [J]. IEEE Signal Processing Magazine,2007,24(9):86-105.
[11] Glenn S. Smith. A Direct Derivation of a Single-antenna Reciprocity Relation for The TimeDomain [J]. IEEE Transactions on Antennas and Propagation,2004,52(6):1568-1577.
[12] Liu C. Impacts of I/Q Imbalance on QPSK-OFDM QAM Detection [J]. IEEE Transactions onConsumer Electronics,1998,44(3):984-989.
[13] Parsons J D. The Mobile Radio Propagation Channel [M].2ndedition. West Sussex: JohnWiley&Sons Ltd,2000.
[14] Lee W C Y. Mobile Communications Engineering [M]. New York, USA: McGraw HillPublications,1985.
[15] Cover T, Gamal A E. Capacity Theorems for the Relay Channel [J]. IEEE Transactions onInformation Theory,1979.25(5):572-584.
[16] Sendonaris A, Erkip E, Aazhang B. User Cooperation Diversity Part I: System Description [J].IEEE Transaction on Communications,2003,51(11):1927-1938.
[17] Laneman J N, Wornell G W, Tse D N C. Cooperative Diversity in Wireless Networks:Efficient Protocols and Outage Behavior. IEEE Transaction on Information Theory,2004,50(12):3062-3080
[18] Hunter T E, Nosratinia A. Diversity through Coded Cooperation [J]. IEEE Transactions onWireless Communications,2006,5(2):283-289.
[19] H st-Madsen A, Zhang J. Capacity Bounds and Power Allocation for Wireless RelayChannels [J]. IEEE Transactions on Information Theory,2005,51(6):2020-2040.
[20] Yang S, Belfiore J C. Towards the Optimal Amplify-and-Forward Cooperative DiversityScheme [J]. IEEE Transactions on Information Theory,2007,53(9):3114-3126.
[21] Sneesens H H, Vandendorpe L. Soft Decode and Forward Improves CooperativeCommunications [C]//IEEE International Workshop on Computational Advances inMulti-Sensor Adaptive Processing. IEEE,2005:157-160.
[22] Bao X K, Li J. Decode-Amplify-Forward (DAF): A New Class of Forwarding Strategy forWireless Relay Channels [C]//IEEE6th Workshop on Signal Processing Advances in WirelessCommunications. IEEE,2005:816-820.
[23] Bouanen S, Boujemaa H, Ajib W. Threshold-based Adaptive Decode-Amplify-ForwardRelaying Protocol for Cooperative Systems[C]//7th International Wireless Communicationsand Mobile Computing Conference. IEEE,2011:725-730.
[24] Rankov B, Wittneben A. Spectral Efficient Protocol for Half-Duplex Fading Relay Channel[J]. IEEE Journal on Selected Areas in Communications,2007,25(2):379-389.
[25] Popovski P, Yomo H. Wireless Network Coding by Amplify-And-Forward for Bi-directionalTraffic Flow [J]. IEEE Communication Letters,2007,11(1):16-18.
[26] Bletsas A, Khisti A, Reed D P, et al. A Simple Cooperative Diversity Method Based onNetwork Path Selection [J]. IEEE Journal on Selected Areas in Communications,2006,24(3):659-672.
[27] Bletsas A, Hyundong S, Win M Z. Cooperative Communications withOutage-Optimal Opportunistic Relaying [J]. IEEE Transactions on Wireless Communications,2007,6(9):3450-3460.
[28] Bletsas A, Khisti A, Win M Z. Opportunistic Cooperative Diversity with Feedbackand Cheap Radios [J]. IEEE Transactions on Wireless Communications,2008,7(5):1823-1827.
[29] Ikki S S, Ahmed M H. On the Performance if Cooperative-Diversity Networks with the NthBest-Relay Selection Scheme [J]. IEEE Transactions on Communications,2010,58(11):3062-3069.
[30] Jing Y D, Jafarkhani H. Single and Multiple Relay Selection Schemes and Their AchievableDiversity Orders [J]. IEEE Transactions on Wireless Communications,2009,8(3):1414-1423.
[31] Li Y, Yin Q, Xu W, et al. On the Design of Relay Selection Strategies in RegenerativeCooperative Networks with Outdated CSI [J]. IEEE Transactions on WirelessCommunications,2011,10(9):3086-3097.
[32] Caleb K L, Sriram V, Robert W H. Relay Subset Selection in Wireless Networks Using PartialDecode-and-Forward Transmission [J]. IEEE Transactions on Vehicular Technology,2009,58(2):692-704.
[33] Gayan A, Masoud A, Chintha T. Output-Threshold Multiple-Relay-Selection Scheme forCooperative Wireless Networks [J]. IEEE Transactions on Vehicular Technology,2010,59(6):3091-3097
[34] Luo J, Blum R S, Cimini L, et al. Decode-and-Forward Cooperative Diversity with PowerAllocation in Wireless Networks [J]. IEEE Transactions on Wireless Communications,2007,6(3):793-799.
[35] Blum R S, Cimini L, Greenstein L, et al. Power Allocation in a Transmit Diversity Systemwith Mean Channel Gain Information [J]. IEEE Communications Letters,2005,9(7):616-618.
[36] Ren S, Letaief K. Maximizing the Effective Capacity for Wireless Cooperative RelayNetworks with QoS Guarantees [J]. IEEE Transactions on Communications,2009,57(7):2148-2159.
[37] Kim S, Wang X, Madihian M. Optimal Resource Allocation in Multi-hop OFDMA WirelessNetworks with Cooperative Relay [J]. IEEE Transactions on Wireless Communications,2008,7(5):1833-1838.
[38] Shim W, Han Y, Kim S. Fairness-Aware Resource Allocation in a Cooperative OFDMAUplink System [J]. IEEE Transactions on Vehicular Technology,2010,59(2):932-939.
[39] Yang N, Tian H, Chen S, et al. An Adaptive Frame Resource Allocation Strategy forTDMA-Based Cooperative Transmission [J]. IEEE Communications Letters,2007,11(5):417-419.
[40] Edmund M Y, Randall A B. Throughput Optimal Control of Cooperative Relay Networks [J].IEEE Transactions on Information Theory,2007,53(10):3827-3833.
[41] Chen W, Dai L, Letaief K B, et al. A Unified Cross-Layer Framework for Resource Allocationin Cooperative Networks [J]. IEEE Transactions on Wireless Communications,2008,7(8):3000-3012.
[42] Huang J W, Han Z, Chiang M, et al. Auction-Based Resource Allocation for CooperativeCommunications [J]. IEEE Journal on Selected Areas in Communications,2008,26(7):1226-1237.
[43] Wang B, Han Z, Liu K J R. Distributed Relay Selection and Power Control for MultiuserCooperative Communication Networks Using Stackelberg Game [J]. IEEE Transactions onMobile Computing,2009,8(7):975-990.
[44] Laneman J N, Wornell G W. Distributed Space-Time-Coded Protocols for ExploitingCooperative Diversity in Wireless Networks [J]. IEEE Transactions on Information Theory,2003,49(10):2415-2425.
[45] Nabar R U, Helmut B, Kneubühler F W. Fading Relay Channels: Performance Limits andSpace-Time Signal Design [J]. IEEE Journal on Selected Areas in Communications,2004,22(6):1099-1109.
[46] Gamal H E, Hammons A R, Stefanov A. Space-Time Overlays for Convolutionally CodedSystems [J]. IEEE Transactions on Communications,2003,51(9):1603-1612.
[47] Anghel P A, Kaveh M. On the Performance of Distributed Space-Time Coding Systems withOne and Two Non-Regenerative Relays [J]. IEEE Transactions on Wireless Communications,2006,5(3):682-692.
[48] Chen S, Wang W, Zhang X, et al. Performance Analysis of OSTBC Transmission inAmplify-and-Forward Cooperative Relay Networks [J]. IEEE Transactions on VehicularTechnology,2010,59(1):105-113.
[49] Sirkeci M B, Scaglione A. Randomized Space-Time Coding for Distributed CooperativeCommunication [J]. IEEE Transactions on Signal Processing,2007,55(10):5003-5017.
[50] Vajapeyam M, Mitra U. Performance Analysis of Distributed Space-Time Coded Protocols forWireless Multi-Hop Communications [J]. IEEE Transactions on Wireless Communications,2010,9(1):122-133.
[51] Lai H, Liu K. Space-Time Network Coding [J]. IEEE Transactions On Signal Processing,2011,59(4):1706-1718.
[52] Wei S, Goeckel D, Valenti M. Asynchronous Cooperative Diversity [J]. IEEE Transactions onWireless Communications,2006,5(6):1547-1557.
[53] Wei S. Diversity-Multiplexing Tradeoff of Asynchronous Cooperative Diversity in WirelessNetworks [J]. IEEE Transactions on Information Theory,2007,53(11):4150-4172.
[54] Li X. Space-Time Coded Multi-Transmission among Distributed Transmitters without PerfectSynchronization [J]. IEEE Signal Processing Letters,2004,11(12):948-951.
[55] Guo X, Xia X. Distributed Linear Convolutive Space-Time Codes for AsynchronousCooperative Communication Networks [J]. IEEE Transactions on Wireless Communications,2008,7(5):1857-1861.
[56] Li Y, Xia X. A Family of Distributed Space-Time Trellis Codes with AsynchronousCooperative Diversity [J]. IEEE Transactions on Communications,2007,55(4):790-800.
[57] Shang Y, Xia X. Shift-full-rank matrices and applications in space-time trellis codes for relaynetworks with asynchronous cooperative diversity [J]. IEEE Transactions on InformationTheory,2006,52(7):3153-3167.
[58] Li Z, Xia X. A Simple Alamouti Space-Time Transmission Scheme for AsynchronousCooperative Systems [J]. IEEE Signal Processing Letters,2007,14(11):804-807.
[59] Li Z, Xia X. A Distributed Differentially Encoded OFDM Scheme for AsynchronousCooperative Systems with Low Probability of Interception [J]. IEEE Transactions on WirelessCommunications,2009,8(7):3372-3379.
[60] Wu N, Gharavi H. Asynchronous Cooperative MIMO Systems Using a Linear DispersionStructure [J]. IEEE Transactions on Vehicular Technology,2010,59(2):779-787.
[61] Fritz Gfeller F, Hirt W. A Robust Wireless Infrared System with Channel Reciprocity [J].IEEE Communications Magazine,1998,36(12):100-106.
[62] Medard M. The Effect upon Channel Capacity in Wireless Communications of Perfect andImperfect Knowledge of the Channel [J]. IEEE Transactions on Information Theory,2000,46(3):933-946.
[63] Choi J. Performance Analysis for Transmit Antenna Diversity with/without ChannelInformation [J]. IEEE Transactions on Vehicular Technology,2002,51(1):101-113.
[64] Paris J F, Aguayo T M C, Entrambasaguas J T, Impact of Imperfect Channel Estimation onAdaptive Modulation Performance in Flat Fading [J]. IEEE Transactions on Communications,2004,52(5):716-720.
[65] Wang C, Ross D M. Adaptive Downlink Multi-User MIMO Wireless Systems for CorrelatedChannels with Imperfect CSI [J]. IEEE Transactions on Wireless Communications,2006,5(9):2435-2446.
[66] Helstrom C W. Distribution of the Envelope of a Sum of Random Sine Waves and GaussianNoise [J]. IEEE Transactions on Aerospace and Electronic Systems,2002,35(2):594-601.
[67] Chen M, Ekman T, Viberg M. New Approaches for Channel Prediction Based on SinusoidalModeling [J]. EURASIP Journal on Advances in Signal Processing, Vol.2007, Article ID:49393.
[68] Kim K T, Seo D K, Kim H T. Efficient Radar Target Recognition Using the MUSICAlgorithm and Invariant Features [J]. IEEE Transactions on Antennas and Propagation,2002,50(3):325-337.
[69] Abeida H, Delmas J P. MUSIC-like Estimation of Direction of Arrival for NoncircularSources [J]. IEEE Transactions on Signal Processing,2006,54(7):2678-2690.
[70] Yuen N, Friedlander B. Asymptotic Performance Analysis of ESPRIT, Higher Order ESPRIT,and Virtual ESPRIT Algorithms [J]. IEEE Transactions on Signal Processing,1996,44(10):2537-2550.
[71] Shahbazpanahi S, Valaee S, Bastani M H. Distributed Source Localization Using ESPRITAlgorithm [J]. IEEE Transactions on Signal Processing,2001,49(10):2169-2178.
[72] Jakobsson A, Swindlehurst A, Stoica P. Subspace-based Estimation of Time Delays andDoppler Shifts [J]. IEEE Transactions on Signal Processing,1998,46(9):2472-248.
[73] Yang B, Letaief K B, Cheng R, et al. Channel Estimation for OFDM Transmission inMultipath Fading Channels Based on Parametric Channel Modeling [J]. IEEE Transactions onCommunications,2001,49(3):467-479.
[74] Thoma R S, Hampicke D, Richter A, et al. Identification of Time-Variant Directional MobileRadio Channels [J]. IEEE Transactions on Instrumentation and Measurement,2000,49(2):357-364.
[75] Zwick T, Hampicke D, Richter A, et al. A Novel Antenna Concept for Double-DirectionalChannel Measurements [J]. IEEE Transactions on Vehicular Technology,2004,53(2):527-537.
[76] Picheral J, Spagnolini U. Angle and Delay Estimation of Space-Time Channels forTD-CDMA Systems [J]. IEEE Transactions on Wireless Communications,2004,3(3):758-769.
[77] Wong I C, Evans B L. Sinusoidal Modeling and Adaptive Channel Prediction in MobileOFDM Systems [J]. IEEE Transactions on Signal Processing,2008,56(4):1601-1615.
[78] Tan S and Hirose A. Low-Calculation-Cost Fading Channel Prediction Using ChirpZ-Transform [J]. Electronics Letters,2009,45(8):418-420.
[79] Heidari A, Khandani A K, McAvoy D. Adaptive Modeling and Long-Range Prediction ofMobile Fading Channels [J]. IET Communications,2010,4(1):39-50.
[80] Falahati S, Svensson A, Ekman T, et al. Adaptive Modulation Systems for Predicted WirelessChannels [J]. IEEE Transactions on Communications,2004,52(2):307-316.
[81] Zhou S, Giannakis G B. How Accurate Channel Prediction Needs to be forTransmit-Beamforming With Adaptive Modulation Over Rayleigh MIMO Channels?[J].IEEE Transactions on Wireless Communications,2004,3(4):1285-1294.
[82] Schafhuber D, Matz G. MMSE and Adaptive Prediction of Time-Varying Channels for OFDMSystems [J]. IEEE Transactions on Wireless Communications,2005,4(2):593-602.
[83] Akhtman J, Hanzo L. Channel Impulse Response Tap Prediction for Time-Varying WirelessChannels [J]. IEEE Transactions on Vehicular Technology,2007,56(5):2767-2769.
[84] Heo J, Wang Y P, Chang K H. A Novel Two-Step Channel-Prediction Technique forSupporting Adaptive Transmission in OFDM/FDD System [J]. IEEE Transactions onVehicular Technology,2008,57(1):188-193.
[85] Khrwat A S, Sharif B S, Tsimenidis C C, et al. Channel Prediction for Precoded SpatialMultiplexing Multiple-Input Multiple-Output Systems in Time-Varying Fading Channels [J].IET Signal Processing,2009,3(6):459-466.
[86] Pham V H, Wang X, Nadeau J. Long Term Cluster-Based Channel Envelope and PhasePrediction for Dynamic Link Adaptation [J]. IEEE Communications Letters,2011,15(7):713-715.
[87] Oien G E, Holm H, Hole K J. Impact of Channel Prediction on Adaptive Coded ModulationPerformance in Rayleigh Fading [J]. IEEE Transactions on Vehicular Technology,2004,53(3):758-769.
[88] Sun J, Zhang T, Liu F. Nonlinear Prediction of Mobile-Radio Fading Channel UsingRecurrent Least Squares Support Vector Machines and Embedding Phase Space [C]//2004International Conference on Communications, Circuits and Systems (ICCCAS2004). IEEE,2004:282-286.
[89] Fernández M, Cumplido M, García J, et al. SVM Multiregression for Nonlinear ChannelEstimation in Multiple-Input Multiple-Output Systems [J]. IEEE Transactions on SignalProcessing,2004,52(8):2298-2307.
[90] Liu C. Impacts of I/Q Imbalance on QPSK-OFDM QAM Detection [J]. IEEE Transactions onConsumer Electronics,1998,44(3):984-989.
[91] Valkama M, Renfors M, Koivunen V. Advanced Methods for I/Q Imbalance Compensation inCommunication Receivers [J]. IEEE Transactions on Signal Processing,2001,49(10):2335-2344.
[92] Abidi A A. Direct-Conversion Radio Transceivers for Digital Communications [J]. IEEEJournal of Solid-State Circuits,1995,30(12):1399-410.
[93] Tubbax J, Come B, Van der Perre L, et al. Compensation of IQ Imbalance and Phase Noise inOFDM Systems [J]. IEEE Transactions on Wireless Communications,2005,4(3):872-877.
[94] Tarighat A, Bagheri R, Sayed A H. Compensation Schemes and Performance Analysis of IQImbalances in OFDM Receivers [J]. IEEE Transactions on Signal Processing,2005,53(8):3257-3268.
[95] Tarighat A, Sayed A H. MIMO OFDM Receivers for Systems with IQ Imbalances [J]. IEEETransactions on Signal Processing,2005,53(9):3582-3596.
[96] Barhumi I, Moonen M. IQ-Imbalance Compensation for OFDM in the Presence of IBI andCarrier-Frequency Offset [J]. IEEE Transactions on Signal Processing,2007,55(1):256-266.
[97] Horlin F, Bourdoux A, Van der Perre L. Low-Complexity EM-based Joint Acquisition of theCarrier Frequency Offset and IQ Imbalance [J]. IEEE Transactions on WirelessCommunications,2008,7(6):2212-2220.
[98] He L, Ma S, Wu Y, et al. Pilot-Aided IQ Imbalance Compensation for OFDM SystemsOperating Over Doubly Selective Channels [J]. IEEE Transactions on Signal Processing,2011,59(5):2223-2233.
[99] Mattera D, Paura L, Sterle F. MMSE WL Equalizer in Presence of Receiver IQ Imbalance [J].IEEE Transactions on Signal Processing,2008,56(4):1735-1740.
[100] Yoshida Y, Hayashi K, Sakai H, et al. Analysis and Compensation of Transmitter IQImbalances in OFDMA and SC-FDMA Systems [J]. IEEE Transactions on Signal Processing,2009,57(8):3119-3129.
[101] Inamori M, Bostamam A M, Sanada Y, et al. IQ Imbalance Compensation Scheme in thePresence of Frequency Offset and Dynamic DC Offset for a Direct ConversionReceiver[J].IEEE Transactions on Wireless Communications,2009,8(5):2214-2220.
[102] Zou Q, Tarighat A, Sayed A H. Joint Compensation of IQ Imbalance and Phase Noise inOFDM Wireless Systems [J]. IEEE Transactions on Communications,2009,57(2):404-414.
[103] Chiu Y J, Hung S P. Estimation Scheme of the Receiver IQ Imbalance under CarrierFrequency Offset in Communication System [J]. IET Communications,2010,4(11):1381-1388.
[104] Gu C F, Law C L, Wu W. Time Domain IQ Imbalance Compensation for Wideband WirelessSystems[J]. IEEE Communications Letters,2010,14(6):539-541.
[105] Tarighat A, Sayed A H. Joint Compensation of Transmitter and Receiver Impairments inOFDM Systems [J]. IEEE Transactions on Wireless Communications,2007,6(1):240-247.
[106] Schenk T C W. Performance Analysis of Zero-IF MIMO OFDM Transceivers with IQImbalance [J]. Journal of Communications,2007,2(7):9-19.
[107] Tandur D, Moonen M. Joint Adaptive Compensation of Transmitter and Receiver IQImbalance under Carrier Frequency Offset in OFDM-Based Systems [J]. IEEE Transactionson Signal Processing,2007,55(11):5246-5252.
[108] Zou Y, Valkama M, Renfors M. Analysis and Compensation of Transmitter and Receiver I/QImbalances in Space-Time Coded Multiantenna OFDM Systems [J]. EURASIP JournalonWireless Communications and Networking, Vol.2008, Article ID:391025.
[109] Morelli M, Mengali U. A Comparison of Pilot-Aided Channel Estimation Methods for OFDMSystems [J]. IEEE Transactions on Signal Processing,2001,49(12):3065-3073.
[110] Song S, Singer A C. Pilot-Aided OFDM Channel Estimation in the Presence of the GuardBand [J]. IEEE Transactions on Communications,2007,55(8):1459-1465.
[111] Shin C, Heath R W, Powers E J. Blind Channel Estimation for MIMO-OFDM Systems [J].IEEE Transactions on Vehicular Technology,2007,56(2):670-685.
[112] Beres E, Adve R. Blind Channel Estimation for Orthogonal STBC in MISO Systems, IEEETransactions on Vehicular Technology,2007,56(4):2042-2050.
[113] Han B, Gao X, You X, et al. An Iterative Joint Channel Estimation and Symbol DetectionAlgorithm Applied in OFDM System with High Data to Pilot Power Ratio[C]//IEEEInternational conference of Communications. IEEE,2003:2076-2080.
[114] IEEE. IEEE Standard for Information technology--Telecommunications and InformationExchange between Systems--Local and Metropolitan Area Networks--Specific RequirementsPart11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY)Specifications Amendment5: Enhancements for Higher Throughput[S]. IEEE,2009.
[115] Hsieh M, We C. Channel Estimation for OFDM Systems based on Comb-Type PilotArrangement in Frequency Selective Fading Channels [J]. IEEE Transactions on ConsumerElectronics,1998,44(1):217-225.
[116] Coleri S, Ergen M, Puri A, et al. Channel Estimation Techniques Based on Pilot Arrangementin OFDM Systems [J]. IEEE Transactions on Broadcasting,2002,48(3):223-229.
[117] Yang H. A Road to Future Broadband Wireless Access: MIMO-OFDM-Based Air Interface [J].IEEE Communications Magazine,2005,43(1):53-60.
[118] Hoeher P, Kaiser S, Robertson I. Two-Dimensional Pilot-Symbol-Aided Channel Estimationby Wiener Filtering [C]//International Conference of Acoustics Speech and Signal Processing(ICASSP). IEEE,1997:1845-1848.
[119] Qiao Y, Yu S, Su P, et al. Research on an Iterative Algorithm of LS Channel Estimation inMIMO OFDM Systems [J]. IEEE Transactions on Broadcasting,2005,51(1):149-153.
[120] Lim C H, Han D S. Robust LS Channel Estimation with Phase Rotation for Single FrequencyNetwork in OFDM [J]. IEEE Transactions on Consumer Electronics,2006,52(4):1173-1178.
[121] Negi R, Cioffi J. Pilot Tone Selection for Channel Estimation in a Mobile OFDM System [J].IEEE Transactions on Consumer Electronics,1998,44(3):1122-1128.
[122] Barhumi I, Leus G, Moonen M. Optimal Training Design for MIMO OFDM Systems inMobile Wireless Channels [J]. IEEE Transactions on Signal Processing,2003,51(6):1615-1624.
[123] Edfors O, Sandell M, Van de Beek J J, et al. Analysis of DFT-Based Channel Estimators forOFDM [J]. Wireless Personal Communications,2000,12(1):55-70.
[124] Larsson E G, Liu G, Li J, et al. Joint Symbol Timing and Channel Estimation for OFDMBased WLANs [J]. IEEE Communications Letters,2001,5(8):325-327.
[125] Cheng Z, Dahlhaus D. Time Versus Frequency Domain Channel Estimation for OFDMSystems with Antenna Arrays[C]//6th International Conference on Signal Processing. IEEE,2002:1340-1343.
[126] Kang S G, Ha Y M, Joo E K. A Comparative Investigation on Channel Estimation Algorithmsfor OFDM in Mobile Communications [J]. IEEE Transactions on Broadcasting,2003,49(2):142-149.
[127] Morelli M, Mengali U. A Comparison of Pilot-Aided Channel Estimation Methods for OFDMSystems [J]. IEEE Transactions on Signal Processing,2001,49(12):3065-3073.
[128] Edfors O, Sandell M, Van de Beek J J, et al. OFDM Channel Estimation by Singular ValueDecomposition [J]. IEEE Transactions on Communications,1998,46(7):931-939.
[129] Gong Y, Letaief K B. Low Complexity Channel Estimation for Space-Time Coded WidebandOFDM Systems [J]. IEEE Transactions on Wireless Communications,2003,2(5):876-882.
[130] Ozdemir M K, Arslan H, Arvas E. Towards Real-Time Adaptive Low-Rank LMMSE ChannelEstimation of MIMO-OFDM Systems [J]. IEEE Transactions on Wireless Communications,2006,5(10):2675-2678.
[131] Li Y, Cimini L J, Sollenberger N R. Robust Channel Estimation for OFDM Systems withRapid Dispersive Fading Channels [J]. IEEE Transactions on Communications,1998,46(7):902-915.
[132] Tolochko I, Faulkner M. Real Time LMMSE Channel Estimation for Wireless OFDMSystems with Transmitter Diversity [C]//IEEE56th Conference of Vehicular Technology.IEEE,2002:1555-1559.
[133] Zhao Y, Huang A. A Novel Channel Estimation Method for OFDM Mobile CommunicationSystems Based on Pilot Signals and Transform-Domain Processing[C]//IEEE47th VehicularTechnology Conference. IEEE,1997:2089-2093.
[134] Yeh Y H, Chen S G. DCT-Based Channel Estimation for OFDM Systems [C]//2004International Conference of Communications. IEEE,2004:2442-2446.
[135] Feng S, Wu W. DCT-based Channel Estimation Method for MIMO-OFDM Systems UsingPhase Shifted Pilot Sequences [C]//Wireless Communications and Networking Conference,2007(WCNC2007). IEEE,2007:12-15.
[136] Li Y, Sollenberger N R. Clustered OFDM with Channel Estimation for High Rate WirelessData [J]. IEEE Transactions on Communications,2001,49(12):2071-2076.
[137]郑宝玉,张继东.基于小波去噪的OFDM信道估计新方法[J].电子与信息学报,2006,28(3):415-418.
[138] Lee Y S, Shin H C, H N. Channel Estimation Based on a Time-Domain Threshold for OFDMSystems [J]. IEEE Transactions on Broadcasting,2009,55(3):656-662.
[139] Kwak K, Lee S, Kim J, et al. A New DFT-Based Channel Estimation Approach for OFDMwith Virtual Subcarriers by Leakage Estimation [J]. IEEE Transactions on WirelessCommunications,2008,7(6):2004-2008.
[140] Sun W, Li L. A Time Domain Iteration-based Channel Estimation Method in OFDM Systemswith Null Subcarriers [C]//2010IEEE71stVehicular Technology Conference (VTC2010-Spring). IEEE,2010:1-5
[141] Seo J, Jang S, Yang J, et al. Analysis of Pilot-Aided Channel Estimation with OptimalLeakage Suppression for OFDM Systems [J]. IEEE Communications Letters,2010,14(9):809-811.
[142] Carbonelli C, Vedantam S, Mitra U. Sparse Channel Estimation with Zero Tap Detection [J].IEEE Transactions on Wireless Communications,2007,6(5):1743:1753.
[143] Li W, Preisig J C.Estimation of Rapidly Time-Varying Sparse Channels [J]. IEEE Journal ofOceanic Engineering,2007,32(4):927-939.
[144] Donoho D. Compressed Sensing [J]. IEEE Transactions on Information Theory2006,52(4):1289-1306.
[145] Khojastepour M A, Gomadam K, Wang X. Pilot-Assisted Channel Estimation for MIMOOFDM Systems Using Theory of Sparse Signal Recovery[C]//IEEE International Conferenceon Acoustics, Speech and Signal Processing. IEEE,2009:2693-2696.
[146] Huang J, Huang J, Berger C R. Iterative Sparse Channel Estimation and Decoding forUnderwater MIMO-OFDM [J]. EURASIP Journal on Advances in Signal Processing, Volume2010, Article ID:460379,11pages.
[147] Soltanolkotabi M, Amini A, Marvasti F. OFDM Channel Estimation Based on AdaptiveThresholding for Sparse Signal Detection[C]//17th European Signal Processing Conference.EUSIPCO,2009:1685-1689.
[148] Maechler P, Greisen P, Felber N, et al. Matching Pursuit: Evaluation and Implementation forLTE Channel Estimation[C]//Proceedings of2010IEEE International Symposium on Circuitsand Systems.IEEE,2010:589-592.
[149] Cepeda R, Fitton M, Nix A. The Performance of Robust Adaptive Modulation over WirelessChannels with Non Reciprocal Interference[C]//IEEE55th Vehicular Technology Conference.IEEE,2002:1497-1501.
[150] Tolli A, Cndreanu M. Compensation of Interference Non-Reciprocity in Adaptive TDDMIMO-OFDM Systems[C]//15th IEEE International Symposium on Personal, Indoor andMobile Radio Communications. IEEE,2004:859-863.
[151] Yomo H, Reynisson R V. A Power Control Method for Channel Non-ReciprocityCompensation in Hybrid Duplexing [C]//IEEE17th International Symposium on Personal,Indoor and Mobile Radio Communications. IEEE,2006:1-5.
[152] Tolli A, Codreanu M, Juntti M. Suppression of Non-reciprocal Interference in AdaptiveCellular Systems [C]//IEEE61stVehicular Technology Conference. IEEE,2005:1072-1076.
[153] Tolli A, Codreanu M, Juntti M. Compensation of Non-Reciprocal Interference in AdaptiveMIMO-OFDM Cellular Systems.[J]. IEEE Transactions on Wireless Communications,2007,6(2):545-555.
[154] Stoica P, Lindskog E. Space-Time Block Coding for Channels With Intersymbol Interference
[C]//Proceedings of the35thAsilomar Conference on Signal, Systems and Computers. IEEE,2001:252-256.
[155] Clark M V. Adaptive Frequency-Domain Equalization and Diversity Combining for BroadbandWireless Communications [J]. IEEE Journal on elected Areas in Communications,1998,16(8):1385-1395.
[156] Falconer D, Ariyavisitakul S L, Benyamin-Seeyar A, et al. Frequency Domain Equalization forSingle-Carrier Broadband Wireless Systems [J]. IEEE Communication Magazine,2002,40(4):58-66.
[157] Pancaldi F, Vitetta G M. Block Channel Equalization in the Frequency Domain [J]. IEEETransactions on Communications,2005,53(3):463-471.
[158] Wang Z, Ma X, Giannakis G B. OFDM or Single-Carrier Block Transmissions?[J]. IEEETransactions on Communications,2004,52(3):380-394.
[159] Ohno S. Performance of Single-Carrier Block Transmissions over Multipath Fading Channelswith Linear Equalization [J]. IEEE Transactions on Signal Processing,2006,54(10):3678:3687.
[160] Dhahir N A. Single-Carrier Frequency-Domain Equalization for Space-Time Block-CodedTransmissions over Frequency-Selective Fading Channels [J]. IEEE Communications Letters,2001,5(7):304-306.
[161] Zheng L, Tse D N C. Diversity and Multiplexing: A Fundamental Tradeoff inMultiple-Antenna Channels [J]. IEEE Transactions on Information Theory,2003,49(5):1073:1096.
[162] Willink T J. Efficient adaptive SVD Algorithm for MIMO Applications [J]. IEEETransactions on Signal Processing,2008,56(2):615-622.
[163] Andersen J B. Array Gain and Capacity for Known Random Channels with MultipleElements Arrays at Both Ends [J]. IEEE Journal on Selected Areas in Communications,2000,18(11):2172-2178.
[164] Cover T M, Thomas J A. Elements of Information Theory [M]. New York: Wiley,1991.
[165] Lebrun G, Gao J, Faulker M. MIMO Transmission Over A Time-Varying Channel UsingSVD [J]. IEEE Transaction on Wireless Communications,2005,4(2):757-764.
[166] Yen C P. Advanced Receiver Signal Processing Functions for Next Generation WirelessCommunication Systems [D]. Polytechnic University, New York,2007.
[167] Jakes W C. Microwave Mobile Communications [M]. Virginia: Wiley-IEEE Press,1994.
[168] Duel-Hallen A, Hu S, Hallen, H. Long-Range Prediction of Fading Signals [J]. IEEE SignalProcessing Magazine,2000,17(3):62-75.
[169] Matthias Patzold. Mobile Fading Channels [M]. West Sussex: John Wiley&Sons Ltd,2002.
[170]3GPP.3GPP TR25.996V6.1.0. Spatial Channel model for Multiple Input Multiple Output(MIMO) Simulations (Release6)[S].3GPP,2003.
[171] Semmelrodt S, Kattenbach R. Inversigation of Different Fading Forecast Schemes for FlatFading Radio Channels[C]//IEEE2edInternational Conference of Vehicular Technology.IEEE,2003:149-153.
[172]石峰,胡登鹏,王晨,等. OFDM系统中基于补零DFT信道插值算法的研究[J].国防科技大学学报,2010,32(5):98-104.
[173] Wax M, Kailath T. Detection of Signals by Information Theoretic Criteria [J]. IEEE Trans onAcoustics, Speech, and Signal Processing,1985,33(2):387-389.
[174] Nuttal A H. Spectral Analysis of a Univariate Process with Bad data Points, via MaximumEntropy and Linear Predictive Techniques. Tech. Rep. TR-5303, Naval Underwater SystemCenter, New London, Conn., Mar.26,1976.
[175] Konstantinidis S, Freear S. Performance Analysis of Tikhonov Regularized LS ChannelEstimation for MIMO OFDM Systems with Virtual Carriers [J]. Wireless PersonalCommunications, December17,2010, published on line.
[176] Zyren J. Overview of the3GPP Long Term Evolution Physical Layer. White Paper, FreescaleSemiconductor, Inc.,2007.
[177] Kang Y, Kim K, Park H. Effficient DFT-based Channel Estimation for OFDM System onMultipath Channels [J]. IET Communications,2007,1(2):197-202.