大规模MU-MIMO的分散式坐标下降数据检测和预编码
Decentralized Coordinate-Descent Data Detection and Precoding for Massive MU-MIMO
摘要：
与小规模MIMO相比，大规模多用户（MU）多输入多输出（MIMO）有望显著提高频谱效率。典型的大规模MU-MIMO基站（BS）设计依赖于集中式线性数据检测器和预编码器，当在单个计算结构上执行时，集中式线性数据检测器和预编码器需要过高的复杂度、互连数据率和芯片输入/输出（I/O）带宽。为了解决这些复杂性和带宽瓶颈，我们提出了新的分散算法的数据检测和预编码，使用坐标下降。我们的方法在多个计算结构上并行计算，同时最大限度地减少互连和I/O带宽。提出的分散式算法实现了接近最佳的错误率性能和多Gbps的吞吐量在次1毫秒的延迟时，实现了多GPU集群与半精度浮点运算。
英文摘要：
Massive multiuser (MU) multiple-input multiple-output (MIMO) promises significant improvements in spectral efficiency compared to small-scale MIMO. Typical massive MU-MIMO base-station (BS) designs rely on centralized linear data detectors and precoders which entail excessively high complexity, interconnect data rates, and chip input/output (I/O) bandwidth when executed on a single computing fabric. To resolve these complexity and bandwidth bottlenecks, we propose new decentralized algorithms for data detection and precoding that use coordinate descent. Our methods parallelize computations across multiple computing fabrics, while minimizing interconnect and I/O bandwidth. The proposed decentralized algorithms achieve near-optimal error-rate performance and multi-Gbps throughput at sub-1 ms latency when implemented on a multi-GPU cluster with half-precision floating-point arithmetic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
