面向前传受限无小区大规模MIMO的联合量化预编码与比特分配
Joint Quantized Precoding and Bit Allocation for Fronthaul-Constrained Cell-Free Massive MIMO
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中文总结 AI 辅助
针对前传受限的无小区大规模MIMO下行链路，提出基于Bussgang分解的量化感知联合预编码与比特分配算法，在功率和比特约束下迭代优化，显著优于传统方案。
中文摘要 AI 辅助
我们研究了具有有限分辨率前传的无小区大规模MIMO系统下行链路中的量化感知预编码。在此类系统中，集中设计的预编码器在传送到分布式接入点（AP）之前必须经过量化，这导致预编码器设计与前传压缩之间产生强耦合。为捕捉这一效应，我们基于Bussgang分解开发了一个端到端信号模型，其中量化失真显式依赖于预编码器系数。基于该模型，我们在每AP功率约束和总前传比特预算下，构建了一个联合预编码与比特分配问题。我们提出了一种高效的块坐标下降算法，该算法迭代更新预编码器、接收缩放和失真水平。数值结果表明，所提方法显著优于均匀比特分配和未考虑量化的预编码方案，尤其是在前传受限场景中。
英文摘要
We study quantization-aware precoding for the downlink of cell-free massive MIMO systems with limited-resolution fronthaul. In such systems, the centrally designed precoder must be quantized before being conveyed to distributed access points (APs), creating a strong coupling between precoder design and fronthaul compression. To capture this effect, we develop an end-to-end signal model based on Bussgang decomposition, where the quantization distortion depends explicitly on the precoder coefficients. Building on this model, we formulate a joint precoding and bit allocation problem under per-AP power constraints and a total fronthaul bit budget. We propose an efficient block coordinate descent algorithm that iteratively updates the precoder, receive scaling, and distortion levels. Numerical results demonstrate that the proposed method significantly outperforms uniform bit allocation and quantization-unaware precoding schemes, particularly in fronthaul-limited regimes.
