A Gibbs distribution based combinatorial optimization algorithm for joint antenna splitting and user scheduling problem in full duplex massive multiple-input multiple-output (MIMO) system is proposed in this paper. The optimal solution of this problem can be determined by exhaustive search. However, the complexity of this approach becomes prohibitive in practice when the sample space is large, which is usually the case in massive MIMO systems. Our algorithm overcomes this drawback by converting the original problem into a Kullback-Leibler (KL) divergence minimization problem, and solving it through a related dynamical system via a stochastic gradient descent method. Using this approach, we improve the spectral efficiency (SE) of the system by performing joint antenna splitting and user scheduling. Additionally, numerical results show that the SE curves obtained with our proposed algorithm overlap with the curves achieved by exhaustive search for user scheduling.
翻译:Gibbs基于分布的组合优化算法,用于处理联合天线分离和用户排期问题,在本文件中建议采用全双倍大规模多投入多输出产出(MIMO)系统。这一问题的最佳解决办法可以通过彻底搜索来确定。然而,当样本空间大时,这一方法的复杂性在实践中变得令人望而却步,而大型MIMO系统通常就是这种情况。我们的算法克服了这一缺陷,将原始问题转换成Kullback-Leebler(KL)差异最小化问题,并通过一个相关动态系统,通过随机梯度梯度下降方法加以解决。我们采用这一方法,通过执行联合天线分离和用户排期,提高系统的光谱效率。此外,数字结果显示,SE曲线与我们提议的算法重叠,通过彻底搜索用户排期实现曲线。