论文标题
在多中心Mu-Mimo中协调的总和最大化，深度展开
Coordinated Sum-Rate Maximization in Multicell MU-MIMO with Deep Unrolling
论文作者
论文摘要
在基础站内和互嵌物干扰和局部通道状态的多中心MIMO网络中，协调的加权总和率最大化被认为是一个重要但困难的问题。通过加权的最小平方误差（WMMSE）算法获得了经典的局部最佳解决方案，该算法促进了Multicell网络中的分布式实现。但是，它通常会遭受缓慢的收敛性，因此沟通开销较大。为了获得更实际的解决方案，传统迭代算法的展开/展开引起了人们的重大关注。在这项工作中，我们演示了使用本地通道状态信息的多核MIMO-MIMO干扰通道中的WMMSE算法进行收发器设计的完整展开。所谓的GCN-WMMSE所产生的体系结构应用了图形信号处理的想法，并且不可知到对不同的无线网络拓扑，同时表现出较少数量的可训练参数和高效率W.R.T.培训数据。它大大减少了所需迭代的数量，同时达到类似于WMMSE算法的性能，从而减轻了分布式部署的开销。此外，我们会根据展开WMMSE算法的展开来回顾以前的体系结构，并将它们与特定适用域中的GCN-WMMSE进行比较。
Coordinated weighted sum-rate maximization in multicell MIMO networks with intra- and intercell interference and local channel state at the base stations is recognized as an important yet difficult problem. A classical, locally optimal solution is obtained by the weighted minimum mean squared error (WMMSE) algorithm which facilitates a distributed implementation in multicell networks. However, it often suffers from slow convergence and therefore large communication overhead. To obtain more practical solutions, the unrolling/unfolding of traditional iterative algorithms gained significant attention. In this work, we demonstrate a complete unfolding of the WMMSE algorithm for transceiver design in multicell MU-MIMO interference channels with local channel state information. The resulting architecture termed GCN-WMMSE applies ideas from graph signal processing and is agnostic to different wireless network topologies, while exhibiting a low number of trainable parameters and high efficiency w.r.t. training data. It significantly reduces the number of required iterations while achieving performance similar to the WMMSE algorithm, alleviating the overhead in a distributed deployment. Additionally, we review previous architectures based on unrolling the WMMSE algorithm and compare them to GCN-WMMSE in their specific applicable domains.
