自适应THP模运算因子选取方法
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摘要
以下行多用户多输入单输出(MU-MISO)系统为例,分析了在发送端采用汤姆林森-哈拉希玛预编码(THP)时,模运算因子的选取对系统性能的影响。通过考察每一步与该因子相关的数据处理过程,说明了在低信噪比(SNR)区域,为了获得较好的系统性能,应当在保证星座图不重叠的基础上,使模运算因子尽可能地小。而后通过固定SNR下的梯度调整,寻找出最佳的模运算因子。通过仿真可以看出,理论分析结果与蒙特卡洛仿真结果相吻合,随着SNR的增加,系统的最佳模运算因子趋于某个固定值,该值与经典论文在对应调制方式下的推荐值相接近。为了优化低SNR区域的系统性能,该文进一步提出了一种自适应的模运算因子选取方法。
Performance is analyzed for the Multiple User Multiple Input and Single Output(MU-MISO) system which employs Tomlinson Harashima Precoding(THP) considering various module factors. The step-to-step process related with the module factor is discussed in detail. It is indicated that in order to obtain a better system performance, the module factor should be selected as small as possible in low Signal to Noise Ratio(SNR) region, as long as the constellation does not overlap. The optimum module factor is sought out by adjusting the corresponding gradient when the SNR is fixed. Simulation results show that the theoretical analysis matches well with the Monte Carlo results. Along with the increasing of SNR, the optimum factor tends to be a fixed value which is very close to the traditional recommended value. An adaptive approach to select the module factor is proposed to improve the system performance in low SNR region.
Performance is analyzed for the Multiple User Multiple Input and Single Output(MU-MISO) system which employs Tomlinson Harashima Precoding(THP) considering various module factors. The step-to-step process related with the module factor is discussed in detail. It is indicated that in order to obtain a better system performance, the module factor should be selected as small as possible in low Signal to Noise Ratio(SNR) region, as long as the constellation does not overlap. The optimum module factor is sought out by adjusting the corresponding gradient when the SNR is fixed. Simulation results show that the theoretical analysis matches well with the Monte Carlo results. Along with the increasing of SNR, the optimum factor tends to be a fixed value which is very close to the traditional recommended value. An adaptive approach to select the module factor is proposed to improve the system performance in low SNR region.
引文
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[10]蒋攀攀,窦冬冬,杜崇,等.一种分布式MIMO信号的发射与检测方法[J].太赫兹科学与电子信息学报,2013,11(4):552 -556.(JIANG Pan-pan,DOU Dong-dong,DU Chong,et al.A transmission and detection technique for distributed MIMO signal[J].Journal of Terahertz Science and Electronic Information Technology,2013,11(4):552-556.)
[2]Windpassinger C,Fischer R F H,Vencel T,et al.Precoding in multi-antenna and multi-user communications[J].IEEE Transactions on Wireless Communications,2004,3(4):1305-1316.
[3]Joham M,Brehmer J,Utschick W.MMSE approaches to multiuser spatio-temporal Tomlinson Harashima Precoding[C]//ITG SCC 2004.Erlangen:[s.n.],2004:387-394.
[4]Kusume K,Joham M,Utschick W,et al.Cholesky Factorization with Symmetric Permutation Applied to Detecting and Precoding Spatially Multiplexed Data Streams[J].IEEE Transactions on Signal Processing,2007,55(6):3089-3103.
[5]WEI Yu,Varodayan P David,Cioffi M John.Trellis and convolutional precoding for transmitter-based interference presubtraction[J].IEEE Transactions on Communications,2005,53(7):1220-1230.
[6]Khaleghi H,Falahati A.Tomlinson-Harashima precoding with imperfect channel state information[J].IET Communication,2008,2(1):151-158.
[7]Windpassinger C,Fischer R F H,Huber J B.Lattice-reduction-aided broadcast precoding[J].IEEE Trans.Comm.,2004,52(12):2057–2060.
[8]Agrell Erik,Eriksson T,Vardy A,et al.Closest point search in lattices[J].IEEE Trans.Inform.Theory,2002,48(8):2201-2214.
[9]Taherzadeh M,Mobasher A,Khandani A K.LLL reduction achieves the receive diversity in MIMO decoding[J].IEEE Trans.Inform.Theory,2007,53(12):4801-4805.
[10]蒋攀攀,窦冬冬,杜崇,等.一种分布式MIMO信号的发射与检测方法[J].太赫兹科学与电子信息学报,2013,11(4):552 -556.(JIANG Pan-pan,DOU Dong-dong,DU Chong,et al.A transmission and detection technique for distributed MIMO signal[J].Journal of Terahertz Science and Electronic Information Technology,2013,11(4):552-556.)