空间相关瑞利衰落信道下多播大规模多输入多输出中的用户分组
User Subgrouping in Multicast Massive MIMO over Spatially Correlated Rayleigh Fading Channels
摘要：
大规模多输入多输出（MaMIMO）多播在过去几年中受到了极大的关注。MaMIMO是5G系统的关键推动者，可实现即将推出的服务的极高数据速率。物理层的多播是一种有效的服务多个用户，同时要求相同的服务和共享无线资源的方式。本文提出了一种基于空间信道特性的多播用户分组策略，以改进信道估计和预编码过程。我们采用最大最小公平（MMF）的功率分配策略，以最大限度地提高最低频谱效率（SE）的组播服务。此外，我们探讨了空间复用与正交（时间/频率）多址接入的组合。通过改变基站（BS）的天线数量和用户的空间分布，我们还提供了最佳的子组配置，最大化每个子组的频谱效率。最后，我们表明，服务的多播用户到两个正交的时间/频率间隔提供了更好的性能比只依赖于空间复用。
英文摘要：
Massive multiple-input-multiple-output (MaMIMO) multicasting has received significant attention over the last years. MaMIMO is a key enabler of 5G systems to achieve the extremely demanding data rates of upcoming services. Multicast in the physical layer is an efficient way of serving multiple users, simultaneously demanding the same service and sharing radio resources. This work proposes a subgrouping strategy of multicast users based on their spatial channel characteristics to improve the channel estimation and precoding processes. We employ max-min fairness (MMF) power allocation strategy to maximize the minimum spectral efficiency (SE) of the multicast service. Additionally, we explore the combination of spatial multiplexing with orthogonal (time/frequency) multiple access. By varying the number of antennas at the base station (BS) and users' spatial distribution, we also provide the optimal subgroup configuration that maximizes the spectral efficiency per subgroup. Finally, we show that serving the multicast users into two orthogonal time/frequency intervals offers better performance than only relying on spatial multiplexing.
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