标题： 基于多用户媒体的调制用于大规模MIMO系统 显示英文标题

标题： Multiuser Media-based Modulation for Massive MIMO Systems

摘要： 在本文中，我们考虑了{\em 基于媒体的调制（MBM）}，这是一种最近受到越来越多研究关注的吸引人的调制方案，用于大规模MIMO系统的上行链路。每个用户配备一个发射天线，附近有多个射频（RF）镜面（寄生元件）。基站（BS）配备数十到数百个接收天线。在多径信道上，具有$m_{rf}$个RF镜面和$n_r$个接收天线的MBM已被证明在$m_{rf}\rightarrow \infty$趋于无穷大时渐近地达到$n_r$个并行AWGN信道的容量。这表明MBM可以吸引人用于大规模MIMO系统，这些系统通常在基站使用大量接收天线。在本文中，我们研究了在大规模MIMO环境中多用户MBM（MU-MBM）的潜在性能优势。我们的结果表明，多用户MBM（MU-MBM）可以显著优于其他调制方案。例如，在使用传统调制的大规模MIMO系统中，使用基站的500个接收天线实现的比特错误性能，仅使用128个天线即可通过MU-MBM实现。即使是在相同的大规模MIMO环境下，多用户空间调制和广义空间调制也需要超过200个天线才能实现相同的比特错误性能。此外，认识到MU-MBM信号向量本质上是稀疏的，我们提出了一种高效的MU-MBM信号检测方案，该方案使用基于压缩感知的重建算法，如正交匹配追踪（OMP）、压缩采样匹配追踪（CoSaMP）和子空间追踪（SP）。 显示英文摘要

摘要： In this paper, we consider {\em media-based modulation (MBM)}, an attractive modulation scheme which is getting increased research attention recently, for the uplink of a massive MIMO system. Each user is equipped with one transmit antenna with multiple radio frequency (RF) mirrors (parasitic elements) placed near it. The base station (BS) is equipped with tens to hundreds of receive antennas. MBM with $m_{rf}$ RF mirrors and $n_r$ receive antennas over a multipath channel has been shown to asymptotically (as $m_{rf}\rightarrow \infty$) achieve the capacity of $n_r$ parallel AWGN channels. This suggests that MBM can be attractive for use in massive MIMO systems which typically employ a large number of receive antennas at the BS. In this paper, we investigate the potential performance advantage of multiuser MBM (MU-MBM) in a massive MIMO setting. Our results show that multiuser MBM (MU-MBM) can significantly outperform other modulation schemes. For example, a bit error performance achieved using 500 receive antennas at the BS in a massive MIMO system using conventional modulation can be achieved using just 128 antennas using MU-MBM. Even multiuser spatial modulation, and generalized spatial modulation in the same massive MIMO settings require more than 200 antennas to achieve the same bit error performance. Also, recognizing that the MU-MBM signal vectors are inherently sparse, we propose an efficient MU-MBM signal detection scheme that uses compressive sensing based reconstruction algorithms like orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), and subspace pursuit (SP).