Massive MIMO is currently a leading physical layer technology candidate that can dramatically enhance throughput in 5G systems, for both unicast and multicast transmission modalities. As antenna elements are becoming smaller and cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial to perform antenna selection at the transmitter, such that the available RF chains are switched to an appropriate subset of antennas. This paper considers the joint problem of multicast beamforming and antenna selection for a single multicast group in massive MIMO systems. The prior state-of-art for this problem relies on semi-definite relaxation (SDR), which cannot scale up to the massive MIMO regime. A successive convex approximation (SCA) based approach is proposed to tackle max-min fair joint multicast beamforming and antenna selection. The key idea of SCA is to successively approximate the non-convex problem by a class of non-smooth, convex optimization problems. Two fast and memory efficient first-order methods are proposed to solve each SCA subproblem. Simulations demonstrate that the proposed algorithms outperform the existing state-of-art approach in terms of solution quality and run time, in both traditional and especially in massive MIMO settings.

中文翻译：

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大规模 MIMO 中联合多播波束成形和天线选择的快速算法

大规模 MIMO 是目前领先的物理层技术候选，可以显着提高 5G 系统中单播和多播传输模式的吞吐量。由于与射频 (RF) 链相比，毫米波范围内的天线元件变得更小、更便宜，因此在发射机处执行天线选择至关重要，以便将可用的 RF 链切换到合适的天线子集。本文考虑了大规模MIMO系统中单个组播组的组播波束成形和天线选择的联合问题。该问题的现有技术依赖于半定松弛 (SDR)，它无法扩展到大规模 MIMO 机制。提出了一种基于逐次凸逼近 (SCA) 的方法来解决最大-最小公平联合多播波束成形和天线选择问题。SCA 的关键思想是通过一类非光滑凸优化问题来逐次逼近非凸问题。提出了两种快速且内存高效的一阶方法来解决每个 SCA 子问题。仿真表明，无论是在传统还是在大规模 MIMO 设置中，所提出的算法在解决方案质量和运行时间方面都优于现有的最先进方法。