Minimum Sum-Mean-Square-Error Frequency-Domain Pre-Coding for Downlink Multi-User MIMO System in the Frequency-Selective Fading Channel
Minimum Sum-Mean-Square-Error Frequency-Domain Pre-Coding for Downlink Multi-User MIMO System in the Frequency-Selective Fading Channel
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DOI：
10.1109/twc.2017.2684808
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发表时间：
2017-03
影响因子：
10.4
通讯作者：
Qiyue Yu;Ji-chong Guo;Shu-Ying Gao;Wei-Xiao Meng;W. Xiang
中科院分区：
文献类型：
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作者：
Qiyue Yu;Ji-chong Guo;Shu-Ying Gao;Wei-Xiao Meng;W. Xiang
针对频率选择性衰落信道下每个用户配备多个天线的下行多用户MIMO系统，提出了一种新的频域预编码算法。该算法基于最小均方误差和（Min-SMSE）准则，能够同时消除多用户和多天线干扰，从而提高系统的误码率（BER）性能。与传统的最优Min-SMSE算法相比，该算法通过简化迭代和功率分配过程，降低了计算复杂度。并详细推导了理论误码率和各态历经和速率。此外，我们定义和分析的预编码算法的复杂度。仿真结果表明，由于功率分配近似相等，该算法在遍历和速率方面优于最优Min-SMSE算法和传统的块对角化（BD）算法.无论是在非相关信道还是在相关信道中，该算法的误码率性能都接近最优误码率性能，且明显优于BD算法。此外，次优Min-SMSE算法的计算复杂度显着低于其最佳对应，特别是在低信噪比区域。
This paper proposes a new frequency-domain pre-coding algorithm for the downlink multi-user MIMO system in a frequency-selective fading channel, where each user is equipped with multiple antennas. The proposed algorithm is based on the minimum sum-mean-square-error (Min-SMSE) criterion, which is able to eliminate both the multi-user and multi-antenna interferences, resulting in an improved bit error rate (BER) performance. Compared with the traditional optimal Min-SMSE algorithm, the proposed algorithm can reduce the computational complexity through simplifying the iterative and power allocation processes. Furthermore, the theoretical BER and ergodic sum-rate are derived in detail. In addition, we define and analyze the complexity of the pre-coding algorithms. Simulation results show that the proposed algorithm compares favorably with the optimal Min-SMSE and traditional block diagonalization (BD) algorithms in the sense of the ergodic sum-rate due to the approximately equal power allocation. The BER performance of the proposed algorithm is close to that of the optimal one and much better than that of the BD algorithm in both the uncorrelated and correlated channels. Moreover, the computational complexity of the sub-optimal Min-SMSE algorithm is significantly less than that of its optimal counterpart, especially in the low SNR region.