Massive overloaded MIMO signal detection via convex optimization with proximal splitting
Massive overloaded MIMO signal detection via convex optimization with proximal splitting
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DOI：
10.1109/eusipco.2016.7760475
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Ryo Hayakawa;K. Hayashi;Hampei Sasahara;M. Nagahara
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文献类型：
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作者：
Ryo Hayakawa;K. Hayashi;Hampei Sasahara;M. Nagahara
在本文中，我们提出了接收天线数量少于发射流数量的大规模过载多输入多输出（MIMO）系统的信号检测方案。利用绝对值和优化的思想，将信号检测描述为一个凸优化问题，并通过基于Douglas-Rachford分裂的快速算法求解。为了提高性能，我们还提出了一种迭代方法来解决成本函数中权重参数更新的优化问题。仿真结果表明，该方案具有较好的误码率性能，特别是在大规模过载MIMO系统中。
In this paper, we propose signal detection schemes for massive overloaded multiple-input multiple-output (MIMO) systems, where the number of receive antennas is less than that of transmitted streams. Using the idea of the sum-of-absolute-value (SOAV) optimization, we formulate the signal detection as a convex optimization problem, which can be solved via a fast algorithm based on Douglas-Rachford splitting. To improve the performance, we also propose an iterative approach to solve the optimization problem with weighting parameters update in a cost function. Simulation results show that the proposed scheme can achieve much better bit error rate (BER) performance than conventional schemes, especially in large-scale overloaded MIMO systems.