面向流自适应分辨率控制的前传高效无蜂窝大规模MIMO
Fronthaul-Efficient Cell-Free Massive MIMO via Stream-Adaptive Resolution Control
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中文总结 AI 辅助
针对前传受限无蜂窝大规模MIMO，提出流与AP自适应分辨率控制框架，联合优化功率与量化分辨率，采用WMMSE块坐标下降算法求解非凸问题。
中文摘要 AI 辅助
我们考虑一个前传受限的无蜂窝大规模MIMO系统的上行链路，其中单个多天线用户设备（UE）向分布式接入点（AP）传输多个空间流。由于异构信道条件和有限的前传容量，跨AP和流的均匀分辨率量化效率极低。为解决此问题，我们提出了一种流和AP自适应分辨率控制框架，该框架联合优化发射功率和每个AP每个流的量化分辨率。通过利用基于奇异值分解（SVD）处理的分布式实现，数据流在AP处解耦，从而能够在空间流和AP之间实现灵活的前传压缩。由此产生的非凸优化问题通过基于加权最小均方误差（WMMSE）的块坐标下降算法处理，该算法具有半闭式更新。
英文摘要
We consider the uplink of a fronthaul-constrained cell-free massive MIMO system with a single multi-antenna user equipment (UE) transmitting multiple spatial streams to distributed access points (APs). Due to heterogeneous channel conditions and limited fronthaul capacity, uniform-resolution quantization across APs and streams is highly inefficient. To address this, we propose a stream- and AP-adaptive resolution control framework that jointly optimizes the transmit power and per-AP per-stream quantization resolutions. By leveraging a distributed implementation of singular-value decomposition (SVD)-based processing, the data streams are decoupled at the APs, enabling flexible fronthaul compression across both spatial streams and APs. The resulting non-convex optimization problem is tackled using a weighted minimum mean-squared error (WMMSE)-based block coordinate descent algorithm with semi-closed-form updates.
