论文标题
随机混合动力组合设计，用于量化大型MIMO系统
Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems
论文作者
论文摘要
通过在接收器上使用低分辨率类似物对数字转换器（LADC），可以大大降低大量多输入多输出（MMIMO）系统的功率散落和成本。但是，LADC的粗量化和不准确的瞬时通道状态信息（ICSI）都会降低量化MMIMO系统的性能。为了克服这些挑战，我们提出了一种新型的随机混合类似物 - 数字组合制剂（SHC）方案，用于将混合组合制剂适应通道状态信息（SCSI）的长期统计数据。我们试图通过共同优化SHC受到平均速率约束来最大程度地减少发射功率。为了解决所得的非凸随机优化问题，我们开发了宽松的随机连续凸近似（RSSCA）算法。进行模拟以确认我们提出的计划对基准测试器的好处。
Both the power-dissipation and cost of massive multiple-input multiple-output (mMIMO) systems may be substantially reduced by using low-resolution analog-to-digital converters (LADCs) at the receivers. However, both the coarse quantization of LADCs and the inaccurate instantaneous channel state information (ICSI) degrade the performance of quantized mMIMO systems. To overcome these challenges, we propose a novel stochastic hybrid analog-digital combiner (SHC) scheme for adapting the hybrid combiner to the long-term statistics of the channel state information (SCSI). We seek to minimize the transmit power by jointly optimizing the SHC subject to average rate constraints. For the sake of solving the resultant nonconvex stochastic optimization problem, we develop a relaxed stochastic successive convex approximation (RSSCA) algorithm. Simulations are carried out to confirm the benefits of our proposed scheme over the benchmarkers.
