多输入多输出无线通信系统中预编码技术研究
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摘要
多输入多输出(Multiple Input Multiple Output, MIMO)技术作为下一代移动通信技术的主流技术之一,可以在不增加系统带宽和天线发射功率条件下大幅提高频谱利用率。然而在多输入多输出系统中不可避免的会出现各用户之间以及用户内各数据流之间共信道干扰的现象,预编码技术正是这样一种利用发射端已知信道状态信息,在发射端对信号进行预处理,从而消除接收信号中共信道干扰的技术。预编码技术的应用使得多输入多输出系统性能得到显著提升的同时,还大大降低了接收机处理的复杂度,因此,在最近几年受到了国内外研究人员的广泛关注。
本文针对单用户和多用户多输入多输出系统的预编码算法进行了较为深入的研究,主要工作如下：
1.深入了解多输入多输出系统相关知识,对单用户和多用户多输入多输出系统进行建模和分析；
2.针对线性预均衡算法、基于正交三角分解的模代数预编码算法和基于几何均值分解的模代数预编码算法等单用户预编码算法进行了分析,并将基于正交三角分解的模代数预编码算法扩展到四种系统框图进行实现,同时给出了各模型下相关处理矩阵的表达式,并进行了系统仿真和应用分析；
3.考虑多用户多输入多输出系统下行广播信道模型,针对基于信漏噪比最优化准则的线性预编码算法及其相关改进算法进行了分析和实现,并基于此思想提出了一种SLNR-GMD-THP预编码算法。该算法通过引入脏纸编码思想来消除已知信号干扰,增大每用户的信漏噪比,改善系统性能。从另一方面来讲,该算法成功将单用户多输入多输出系统当中的基于几何均值分解的模代数预编码算法扩展到多用户多输入多输出系统当中,几何均值分解思想的引入均衡了各数据流的等效信道性能,避免了最差等效子信道的出现,经仿真验证该算法获得了较好的误码率性能和系统总速率性能。此算法还可以视为一种较新颖的线性预编码算法与非线性预编码算法相结合的预编码算法；
4.研究了基于块对角化思想的多用户预编码算法并基于块对角化几何均值分解模代数预编码算法提出了一种次优贪婪用户排序算法,在保持与最优用户排序相近的误码率性能的条件下,大大降低了算法复杂度。同时为了减小等效信道增益较差用户对系统误码率的影响,将流减少技术与原算法进行结合,并引入流控制因子实现系统对不同用户的差异性服务。
As we all know, multiple input multiple output (MIMO) technology as one of the key technologies can improve the spectral efficiency immensely without increasing system bandwith or transmitting power. However, one of the unavoidable problems in these systems is the co-channel interference among different users and different data streams within the same user.Precoding is just the technology taking full advantage of the channel state information at transimitter to cancel or suppress this co-channel interference. Precoding can improve MIMO systems'performance further and decrease the complexity of the receiver.All in all, precoding technology has already focused worldwide.
In this paper, the precoding technology for the single user and multi-user MIMO systems is mainly investigated. The main contents of the paper are as follows:
Ⅰ. Do deeply into the related knowledge of MIMO systems. Research on the modeling of single user and multi-user MIMO systems.
Ⅱ. Three typical precoding techniques for single user MIMO precoding algorithms, including linear zero-forcing percoding, Tomlinson-Harashima precoding (THP) with Orthogonal Triangular Decomposition (QRD) and Geometric Mean Decomposition (GMD), have been sumed up and simulated with the perfect channel state information at the transmitter and receiver. Moreover, the QRD-THP algorithm has been extended to four kinds of block diagram.
Ⅲ. Based on the deep analysis and research on the leakage based precoding algorithm and its improved algorithm, a new successive SLNR precoding algorithm named SLNR-GMD-THP is proposed in the multi-user MIMO broadcast channel (BC) systems. In the proposed algorithm, the signal-to-leakage-plus-noise ratio (SLNR) is also selected as the optimization criterion. Different from the previous algorithms, dirty paper coding (DPC) is applied to cancel the known interferences. Therefore, owing to the neglect of part of the leakages in this algorithm, the value of SLNR per user is increased significantly. In addition, this algorithm succeeds in introducing the GMD-THP algorithm for the SU-MIMO system to the MU-MIMO system. Meanwhile, because of the introduction of GMD, the proposed algorithm avoids the apparence of the worst equivalent sub-channels leading to a better performance of the system. This new algorithm can be seen as a combination of the linear precoding and non-linear precoding algorithms.
IV. The paper has done the research on precoding techniques for multi-user MIMO precoding algorithms, such as linear block diagonal algorithm. Most importantly, a near-optimal greedy user ordering for the MIMO broadcast channel model based on the BD-GMD-THP algorithm has been proposed. Theoretical analysis and computer simulations have illustrated its low computation complexity relative to the optimal user ordering with little bit error ratio (BER) lose. Moreover, in order to mitigate the impact of users with smaller sub-channel gains to the whole system's BER performance, a joint pre-processing scheme design of adaptive data streams reduction and greedy user ordering is proposed. By means of choosing different values for the controlling factor, we can obtain different system sum-rate and BER performance to satisfy different quality-of-service (QoS) requirements.
本文针对单用户和多用户多输入多输出系统的预编码算法进行了较为深入的研究,主要工作如下：
1.深入了解多输入多输出系统相关知识,对单用户和多用户多输入多输出系统进行建模和分析；
2.针对线性预均衡算法、基于正交三角分解的模代数预编码算法和基于几何均值分解的模代数预编码算法等单用户预编码算法进行了分析,并将基于正交三角分解的模代数预编码算法扩展到四种系统框图进行实现,同时给出了各模型下相关处理矩阵的表达式,并进行了系统仿真和应用分析；
3.考虑多用户多输入多输出系统下行广播信道模型,针对基于信漏噪比最优化准则的线性预编码算法及其相关改进算法进行了分析和实现,并基于此思想提出了一种SLNR-GMD-THP预编码算法。该算法通过引入脏纸编码思想来消除已知信号干扰,增大每用户的信漏噪比,改善系统性能。从另一方面来讲,该算法成功将单用户多输入多输出系统当中的基于几何均值分解的模代数预编码算法扩展到多用户多输入多输出系统当中,几何均值分解思想的引入均衡了各数据流的等效信道性能,避免了最差等效子信道的出现,经仿真验证该算法获得了较好的误码率性能和系统总速率性能。此算法还可以视为一种较新颖的线性预编码算法与非线性预编码算法相结合的预编码算法；
4.研究了基于块对角化思想的多用户预编码算法并基于块对角化几何均值分解模代数预编码算法提出了一种次优贪婪用户排序算法,在保持与最优用户排序相近的误码率性能的条件下,大大降低了算法复杂度。同时为了减小等效信道增益较差用户对系统误码率的影响,将流减少技术与原算法进行结合,并引入流控制因子实现系统对不同用户的差异性服务。
As we all know, multiple input multiple output (MIMO) technology as one of the key technologies can improve the spectral efficiency immensely without increasing system bandwith or transmitting power. However, one of the unavoidable problems in these systems is the co-channel interference among different users and different data streams within the same user.Precoding is just the technology taking full advantage of the channel state information at transimitter to cancel or suppress this co-channel interference. Precoding can improve MIMO systems'performance further and decrease the complexity of the receiver.All in all, precoding technology has already focused worldwide.
In this paper, the precoding technology for the single user and multi-user MIMO systems is mainly investigated. The main contents of the paper are as follows:
Ⅰ. Do deeply into the related knowledge of MIMO systems. Research on the modeling of single user and multi-user MIMO systems.
Ⅱ. Three typical precoding techniques for single user MIMO precoding algorithms, including linear zero-forcing percoding, Tomlinson-Harashima precoding (THP) with Orthogonal Triangular Decomposition (QRD) and Geometric Mean Decomposition (GMD), have been sumed up and simulated with the perfect channel state information at the transmitter and receiver. Moreover, the QRD-THP algorithm has been extended to four kinds of block diagram.
Ⅲ. Based on the deep analysis and research on the leakage based precoding algorithm and its improved algorithm, a new successive SLNR precoding algorithm named SLNR-GMD-THP is proposed in the multi-user MIMO broadcast channel (BC) systems. In the proposed algorithm, the signal-to-leakage-plus-noise ratio (SLNR) is also selected as the optimization criterion. Different from the previous algorithms, dirty paper coding (DPC) is applied to cancel the known interferences. Therefore, owing to the neglect of part of the leakages in this algorithm, the value of SLNR per user is increased significantly. In addition, this algorithm succeeds in introducing the GMD-THP algorithm for the SU-MIMO system to the MU-MIMO system. Meanwhile, because of the introduction of GMD, the proposed algorithm avoids the apparence of the worst equivalent sub-channels leading to a better performance of the system. This new algorithm can be seen as a combination of the linear precoding and non-linear precoding algorithms.
IV. The paper has done the research on precoding techniques for multi-user MIMO precoding algorithms, such as linear block diagonal algorithm. Most importantly, a near-optimal greedy user ordering for the MIMO broadcast channel model based on the BD-GMD-THP algorithm has been proposed. Theoretical analysis and computer simulations have illustrated its low computation complexity relative to the optimal user ordering with little bit error ratio (BER) lose. Moreover, in order to mitigate the impact of users with smaller sub-channel gains to the whole system's BER performance, a joint pre-processing scheme design of adaptive data streams reduction and greedy user ordering is proposed. By means of choosing different values for the controlling factor, we can obtain different system sum-rate and BER performance to satisfy different quality-of-service (QoS) requirements.
引文
[1]Spencer Q.H., Swindlehurst A.L., Haardt M., Zero-forcing methods for downlink spatial multiplexing in muitiuser MIMO channels. IEEE Transactions on Signal Processing,52(2),2004, pp.461-471.
[2]Sampath H., Stoica P., Paulraj A., Genralized linear precoder and decoder design for MIMO channels using the weighted MMSE criterion. IEEE Transactions on Communications,49(12),2001, pp.2198-2206.
[3]Scaglione A., Stoica P., Barbarossa S., Optimal designs for space-time linear precoders and equalizers. IEEE Transaction on Signal Processing,50(5),2002, pp.1051-1064.
[4]Costa M., Writing on dirty paper. IEEE Trans on Information Theory,29(3),1983, pp.439-441.
[5]Tomlinson M., New automatic equaliser employing modulo arithmetic. Electronics Letters,7(5),1971, pp.138-139.
[6]Harashima H., Miyakawa H., Matched-transmission technique for channels with inter-symbol interference. IEEE Transaction on Communications,20(4),1972, pp.774-780.
[7]Windpassinger C., Fischer R.F.H., Vencel T., Precoding in multiantenna and multiuser communications. IEEE Transaction on Wireless Communications,3(4), 2004,pp.1305-1315.
[8]Joham M., MMSE approaches to multiuser spatio-temporal Tomlinson-Harashima precoding. in Proc.5th ITG Conf. Source and Channel Coding, Erlangen, Germany, 2004, pp.387-394.
[9]Kusume K., Joham M., Utschick W, Efficient Tomlinson- Harashima precoding for spatial multiplexing on flat MIMO channel. in Proc. ICC,2005, pp.2021-2025.
[10]Kusume K., Joham M., Utschick W., Cholesky factorization with symmetric permutation applied to detecting and precoding spatially multiplexed data streams. IEEE Transacions on Signal Processing,55(6),2007, pp.3089-3103.
[11]Spencer Q.H., Haardt M., Capacity and downlink transmission algorithms for a multiuser MIMO channel. Conference on Signals, Systems and Computers,2002, pp.1384-1388.
[12]Sadek M., Tarighat A., Sayed A., A leakage based precoding scheme for downlink multi-user MIMO channels. IEEE Transactions on Wireless Communications,6(5),2007, pp.1711-1721.
[13]Jia Liu, Witold A., Krzymien A., Novel nonlinear joint transmitter-receiver processing algorithm for the downlink of multi-user MIMO systems. in Proc. IEEE Globecom'06, San Francisco, USA,2006, pp.1-6.
[14]Yang Yang, Li Qiang, Fang Shu, Joint scheduling and precoding scheme in MU-MIMO system with limited feedback, in WiCOM2010, Chengdu,China,2010, pp.1-4.
[15]Stankovic V., Haardt M., Successive optimization Tomlinson-Harashima precoding (SO THP) for multi-user MIMO systems. in Acoustics, Speech, and Signal Processing, vol 3, Germany,2005, pp.1117-1120.
[16]Jiang Y., Hager W., Li J., The geometric mean decomposition. Linear Algebra and Its Applications,2005, pp.373-384.
[17]Jiang Y, Li J., Uniform channel decomposition for MIMO communications. IEEE Transactionsons on Signal Processing,53(11),2005, pp.4253-4294.
[18]王连英.[学位论文]多用户MIMO预编码技术研究.西安,西安科技大学,2008,pp.6-17.
[19]Windpassinger C., Detection and precoding for multiple input multiple output channels. Erlangen,2004, pp.81-127.
[20]Bandemer B., Haardt M., Linear MMSE multi-user MIMO downlink precoding for users with multiple antennas. in The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'06), Espoo, pp.1-5.
[21]奚家熹.[学位论文]无线通信中的预编码技术.上海,复旦大学,2007,pp.40-41.
[22]Harashima H., Miyakawa H., Matched-transmission technique for channels with intersymbol interference. IEEE Transactions on Communications,20(4),1972, pp.774-780.
[23]贾蓉.[学位论文]MIMO系统中的非线性预编码技术研究.成都,成都电子科技大学,2009,pp.15-19.
[24]Cheng Peng, Tao Meixia, Zhang Wenjun, A new SLNR-based linear precoding for downlink multi-user multi-stream MIMO systems. IEEE Communications, 14(11),2010, pp.1008-1010.
[25]Jiang Yi, Li Jian, Hager W.W., Joint transceiver design for MIMO communications using Geometric Mean Decomposition. IEEE Transactions on Signal Processing,53(10),2005, pp.3791-3803.
[26]Lin Shaowei, Ho W.W.L., Liang Ying-chang, Block diagonal geometric mean decomposition (BD-GMD) for MIMO broadcast channels. IEEE Transactions on Wireless Communications,7(7),2008, pp.2778-2789.
[27]Ho W.W.L., Liang Ying-chang, User ordering and subchannel selection for power minimization in MIMO broadcast channels using BD-GMD. in VTC 2008, Singapore,2008, pp.1-5.
[2]Sampath H., Stoica P., Paulraj A., Genralized linear precoder and decoder design for MIMO channels using the weighted MMSE criterion. IEEE Transactions on Communications,49(12),2001, pp.2198-2206.
[3]Scaglione A., Stoica P., Barbarossa S., Optimal designs for space-time linear precoders and equalizers. IEEE Transaction on Signal Processing,50(5),2002, pp.1051-1064.
[4]Costa M., Writing on dirty paper. IEEE Trans on Information Theory,29(3),1983, pp.439-441.
[5]Tomlinson M., New automatic equaliser employing modulo arithmetic. Electronics Letters,7(5),1971, pp.138-139.
[6]Harashima H., Miyakawa H., Matched-transmission technique for channels with inter-symbol interference. IEEE Transaction on Communications,20(4),1972, pp.774-780.
[7]Windpassinger C., Fischer R.F.H., Vencel T., Precoding in multiantenna and multiuser communications. IEEE Transaction on Wireless Communications,3(4), 2004,pp.1305-1315.
[8]Joham M., MMSE approaches to multiuser spatio-temporal Tomlinson-Harashima precoding. in Proc.5th ITG Conf. Source and Channel Coding, Erlangen, Germany, 2004, pp.387-394.
[9]Kusume K., Joham M., Utschick W, Efficient Tomlinson- Harashima precoding for spatial multiplexing on flat MIMO channel. in Proc. ICC,2005, pp.2021-2025.
[10]Kusume K., Joham M., Utschick W., Cholesky factorization with symmetric permutation applied to detecting and precoding spatially multiplexed data streams. IEEE Transacions on Signal Processing,55(6),2007, pp.3089-3103.
[11]Spencer Q.H., Haardt M., Capacity and downlink transmission algorithms for a multiuser MIMO channel. Conference on Signals, Systems and Computers,2002, pp.1384-1388.
[12]Sadek M., Tarighat A., Sayed A., A leakage based precoding scheme for downlink multi-user MIMO channels. IEEE Transactions on Wireless Communications,6(5),2007, pp.1711-1721.
[13]Jia Liu, Witold A., Krzymien A., Novel nonlinear joint transmitter-receiver processing algorithm for the downlink of multi-user MIMO systems. in Proc. IEEE Globecom'06, San Francisco, USA,2006, pp.1-6.
[14]Yang Yang, Li Qiang, Fang Shu, Joint scheduling and precoding scheme in MU-MIMO system with limited feedback, in WiCOM2010, Chengdu,China,2010, pp.1-4.
[15]Stankovic V., Haardt M., Successive optimization Tomlinson-Harashima precoding (SO THP) for multi-user MIMO systems. in Acoustics, Speech, and Signal Processing, vol 3, Germany,2005, pp.1117-1120.
[16]Jiang Y., Hager W., Li J., The geometric mean decomposition. Linear Algebra and Its Applications,2005, pp.373-384.
[17]Jiang Y, Li J., Uniform channel decomposition for MIMO communications. IEEE Transactionsons on Signal Processing,53(11),2005, pp.4253-4294.
[18]王连英.[学位论文]多用户MIMO预编码技术研究.西安,西安科技大学,2008,pp.6-17.
[19]Windpassinger C., Detection and precoding for multiple input multiple output channels. Erlangen,2004, pp.81-127.
[20]Bandemer B., Haardt M., Linear MMSE multi-user MIMO downlink precoding for users with multiple antennas. in The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC'06), Espoo, pp.1-5.
[21]奚家熹.[学位论文]无线通信中的预编码技术.上海,复旦大学,2007,pp.40-41.
[22]Harashima H., Miyakawa H., Matched-transmission technique for channels with intersymbol interference. IEEE Transactions on Communications,20(4),1972, pp.774-780.
[23]贾蓉.[学位论文]MIMO系统中的非线性预编码技术研究.成都,成都电子科技大学,2009,pp.15-19.
[24]Cheng Peng, Tao Meixia, Zhang Wenjun, A new SLNR-based linear precoding for downlink multi-user multi-stream MIMO systems. IEEE Communications, 14(11),2010, pp.1008-1010.
[25]Jiang Yi, Li Jian, Hager W.W., Joint transceiver design for MIMO communications using Geometric Mean Decomposition. IEEE Transactions on Signal Processing,53(10),2005, pp.3791-3803.
[26]Lin Shaowei, Ho W.W.L., Liang Ying-chang, Block diagonal geometric mean decomposition (BD-GMD) for MIMO broadcast channels. IEEE Transactions on Wireless Communications,7(7),2008, pp.2778-2789.
[27]Ho W.W.L., Liang Ying-chang, User ordering and subchannel selection for power minimization in MIMO broadcast channels using BD-GMD. in VTC 2008, Singapore,2008, pp.1-5.