Joint Transceiver, Data Streams, and User Ordering Optimization for Nonlinear Multiuser MIMO Systems
Joint Transceiver, Data Streams, and User Ordering Optimization for Nonlinear Multiuser MIMO Systems
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DOI：
10.1109/tcomm.2015.2469280
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发表时间：
2015-08
影响因子：
8.3
通讯作者：
Liang Sun;Jiaheng Wang;Victor C. M. Leung
中科院分区：
文献类型：
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作者：
Liang Sun;Jiaheng Wang;Victor C. M. Leung
我们考虑下行链路多输入多输出（MIMO）系统与多天线用户的非线性信号处理算法。设计目标是在给定的总发射功率下，通过联合优化收发器、数据流和用户排序来提高误比特率（BER）性能。我们考虑一个通用的全局目标函数，其元素是每个用户的均方误差（MSE）的Schur凸函数。与非线性Tomlinson-Harashima预编码相结合的块连续迫零预编码在发送端，我们表明，最佳的非线性收发器导致有利的对角和并行结构的所有用户与一般的全球性能指标。允许每个用户的数据流的数量是不大于有效信道的秩的任意数量。然后，我们提供的收发器矩阵和每个用户的最佳功率分配的封闭形式的表达式，并解析表征的最佳数量的数据流的每个用户的最小最大和平均BER指标。用户排序也被优化，以进一步提高系统的性能。我们还分析了信道空间相关性对我们方案性能的影响，并说明了为什么我们提出的自适应策略可以减轻信道空间相关性造成的性能下降。我们所提出的框架的优越性，通过数值结果证明。
We consider nonlinear signal processing algorithms for downlink multiple-input multiple-output (MIMO) systems with multiple-antenna users. The design goal is to improve bit error rate (BER) performance by jointly optimizing the transceiver, data streams, and user ordering, under given total transmit power. We consider a general global objective function, whose elements are Schur-convex functions of the mean square error (MSE) of each user. With nonlinear Tomlinson-Harashima precoding combined with block successive zero-forcing precoding at the transmitter, we show that the optimal nonlinear transceiver leads to favorable diagonal and parallel structures for all users with the general global performance metric. The number of data streams of each user is allowed to be an arbitrary number no more than the rank of the effective channel. We then provide closed-form expressions of the transceiver matrices and optimal power allocation of each user, and analytically characterize the optimal number of data streams of each user for the minimax and average BER metrics. The user ordering is also optimized to further improve the system performance. We also analytically investigate the impact of channel spatial correlation on the performance of our scheme and illustrate why our proposed adaptive strategy can mitigate the performance degradation caused by channel spatial correlation. The superiority of our proposed framework is demonstrated through numerical results.