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Minimum BER Linear Transceivers for MIMO Channels via Primal Decomposition

机译:通过原始分解的MIMO通道的最小BER线性收发器

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摘要

This paper considers the employment of linear transceivers for communication through multiple-input multiple-output (MIMO) channels with channel state information (CSI) at both sides of the link. The design of linear MIMO transceivers has been studied since the 1970s by optimizing simple measures of the quality of the system, such as the trace of the mean-square error matrix, subject to a power constraint. Recent results showed how to solve the problem in an optimal way for the family of Schur-concave and Schur-convex cost functions. In particular, when the constellations used on the different transmit dimensions are equal, the bit-error rate (BER) averaged over these dimensions happens to be a Schur-convex function, and therefore, it can be optimally solved. In a more general case, however, when different constellations are used, the average BER is not a Schur-convex function, and the optimal design in terms of minimum BER is an open problem. This paper solves the minimum BER problem with arbitrary constellations by first reformulating the problem in convex form and then proposing two solutions. One is a heuristic and suboptimal solution, which performs remarkably well in practice. The other one is the optimal solution obtained by decomposing the convex problem into several subproblems controlled by a master problem (a technique borrowed from optimization theory), for which extremely simple algorithms exist. Thus, the minimum BER problem can be optimally solved in practice with very simple algorithms.
机译:本文考虑了使用线性收发器通过链路两端的信道状态信息(CSI)通过多输入多输出(MIMO)信道进行通信的问题。自1970年代以来,一直在研究线性MIMO收发器的设计,方法是优化系统质量的简单度量,例如根据功率限制对均方误差矩阵进行跟踪。最近的结果表明,对于Schur-凹面和Schur-凸面成本函数族,如何以最佳方式解决问题。特别地,当在不同的发送维度上使用的星座相等时,在这些维度上平均的误码率(BER)恰好是舒尔凸函数,因此可以最佳地解决。然而,在更一般的情况下,当使用不同的星座图时,平均BER不是舒尔凸函数,而就最小BER而言的最佳设计是一个未解决的问题。本文首先通过将凸问题重新形成为凸形式,然后提出两个解决方案,以解决任意星座的最小BER问题。一种是启发式且次优的解决方案,在实践中表现出色。另一个是通过将凸问题分解成由主问题(从优化理论中借用的一种技术)控制的几个子问题而获得的最优解,为此,它们存在极其简单的算法。因此,最小的BER问题可以通过非常简单的算法在实践中得到最佳解决。

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