Title: Optimizing Fronthaul Quantization for Flexible User Load in Cell-Free Massive MIMO Show Chinese title

Title: 优化无蜂窝大规模MIMO中灵活用户负载的前传量化

Abstract: We investigate the physical layer (PHY) spectral efficiency and fronthaul network load of a scalable user-centric cell-free massive MIMO system. Each user-centric cluster processor responsible for cluster-level signal processing is located at one of multiple decentralized units (DUs). Thus, the radio units in the cluster must exchange data with the corresponding DU over the fronthaul. Because the fronthaul links have limited capacity, this data must be quantized before it is sent over the fronthaul. We consider a routed fronthaul network, where the cluster processor placement and fronthaul traffic routing are jointly optimized with a mixed-integer linear program. For different numbers of users in the network, we investigate the effect of fronthaul quantization rates, a system parameter computed based on rate-distortion theory. Our results show that with optimized quantization rates, the fronthaul load is quite stable for a wide range of user loads without significant PHY performance loss. This demonstrates that the cell-free massive MIMO PHY and fronthaul network are resilient to varying user densities. Show Chinese abstract

Abstract: 我们研究了可扩展的以用户为中心的无蜂窝大规模MIMO系统的物理层（PHY）频谱效率和前传网络负载。每个负责集群级信号处理的以用户为中心的集群处理器位于多个分布式单元（DUs）中的一个。因此，集群中的射频单元必须通过前传网络与相应的DU交换数据。由于前传链路容量有限，此数据在发送到前传网络之前必须进行量化。我们考虑一种路由前传网络，其中集群处理器的位置和前传流量路由通过混合整数线性规划进行联合优化。对于网络中不同数量的用户，我们研究了前传量化速率的影响，这是一个基于率失真理论计算的系统参数。我们的结果表明，通过优化的量化速率，前传负载在广泛的用户负载范围内相当稳定，且不会导致显著的PHY性能损失。这表明，无蜂窝大规模MIMO的PHY和前传网络对用户密度的变化具有鲁棒性。