发表机构
University of Electronic Science and Technology of China（电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模MIMO系统粗量化导致的限幅失真问题，本文提出硬件友好的固定步长轻量PCG均衡器，通过算法-硬件协同设计实现低复杂度与接近高分辨率检测器的性能，可节省大规模MIMO上行链路功耗。
AI 中文摘要
大规模多输入多输出（MIMO）系统中的粗量化可降低功耗，但会导致限幅失真。贝叶斯期望最大化（BEM）算法可恢复限幅信号，但其矩阵求逆与动态步长评估是硬件瓶颈。本文提出一种硬件友好的单步校正方案，采用初始雅可比预条件共轭梯度（PCG）方向与固定松弛参数，所得符号级更新具备超轻量的O(U)前馈数据通路，在评估的大规模MIMO场景中接近高分辨率参考检测器。有限维分析确立了精确的单步下降规律，证明雅可比归一化可抵消原始乘性远近缩放，同时将加载系统依赖限制在有界衰减因子内，并给出基于归一化信道相干性的固定步长下降可验证充分条件。系统级结果显示，该方案有望为高能效大规模MIMO上行链路节省功耗。
英文摘要
Coarse quantization in massive multiple-input multiple-output (MIMO) systems reduces power but causes clipping distortions. The Bayesian Expectation-Maximization (BEM) algorithm can recover clipped signals, but its matrix inversion and dynamic step-size evaluation are hardware bottlenecks. We propose a hardware-friendly one-step correction that uses the initial Jacobi-preconditioned Conjugate Gradient (PCG) direction with a fixed relaxation parameter. The resulting symbol-level update has an ultra-lightweight $\mathcal{O}(U)$ feed-forward datapath and approaches high-resolution reference detectors in the evaluated massive-MIMO setting. Our finite-dimensional analysis establishes the exact one-step descent law, proves that Jacobi normalization cancels the raw multiplicative near-far scaling while confining the loaded-system dependence to bounded attenuation factors, and gives verifiable sufficient conditions for fixed-step descent in terms of normalized channel coherence. System-level results indicate projected power savings for energy-efficient massive MIMO uplinks.
Comments6 pages, 3 figures, 2 tables
