论文标题
通过稀疏的大规模褪色处理，无细胞的节能无细胞大型MIMO
Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing
论文作者
论文摘要
无细胞的大规模多输入多输出（CF MMIMO）系统通过地理分布的接入点（AP）通过关节传输和接收来为用户设备（UES）服务。为了限制由于领先的信号传导和处理而引起的功耗，每个UE应仅由AP的一个子集提供，但是很难识别该子集。以前的作品已经通过启发式解决了这一组合问题。在本文中，我们提出了CF MMIMO的稀疏分布处理设计，其中AP-UE关联和长期信号处理系数被共同优化。我们制定了两个稀疏性诱导的于点误差（MSE）最小化问题，并通过使用有效的近端方法与区块坐标下降来解决它们。对于下行链路，更具体地说，我们使用上行链路 - 下链链接双重性开发了虚拟优化的大规模褪色预码（V-LSFP）方案。数值结果表明，提出的稀疏处理方案在上行链路和下行链路上都很好地工作。特别是，它们达到的光谱效率几乎相同，就好像所有AP都能为所有UES提供服务，而由于处理和信号的降低，能源效率高2-4倍。
Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling.
