Conventional spatial modulation (SM) is typically considered for transmission
in the downlink of small-scale MIMO systems, where a single one of a set of
antenna elements (AEs) is activated for implicitly conveying extra bits. By
contrast, inspired by the compelling benefits of large-scale MIMO (LS- MIMO)
systems, here we propose a LS-SM-MIMO scheme for the uplink (UL), where each
user having multiple AEs but only a single radio frequency (RF) chain invokes
SM for increasing the UL-throughput. At the same time, by relying on hundreds
of AEs but a small number of RF chains, the base station (BS) can
simultaneously serve multiple users whilst reducing the power consumption. Due
to the large number of AEs of the UL-users and the comparably small number of
RF chains at the BS, the UL multi-user signal detection becomes a challenging
large-scale under-determined problem. To solve this problem, we propose a joint
SM transmission scheme and a carefully designed structured compressive sensing
(SCS)-based multi-user detector (MUD) to be used at the users and BS,
respectively. Additionally, the cyclic- prefix single-carrier (CPSC) is used to
combat the multipath channels, and a simple receive AE selection is used for
the improved performance over correlated Rayleigh-fading MIMO channels. We
demonstrate that the aggregate SM signal consisting of SM signals of multiple
UL-users in one CPSC block appears the distributed sparsity. Moreover, due to
the joint SM transmission scheme, aggregate SM signals in the same transmission
group exhibit the group sparsity. By exploiting these intrinsically sparse
features, the proposed SCS-based MUD can reliably detect the resultant SM
signals with low complexity. Simulation results demonstrate that the proposed
SCS-based MUD achieves a better signal detection performance than its
counterparts even with higher UL-throughtput.

本文提出了一种 LS-SM-MIMO 方案用于上行通信，该方案通过聚合多个用户的 SM 信号以增加 UL 吞吐量，并使用压缩感知和多用户检测以实现效率高、低复杂度的检测性能。

基于压缩感知的大规模 SM-MIMO 上行多用户检测器

Compressive Sensing Based Multi-User Detector for the Large-Scale  SM-MIMO Uplink

This paper compares two important downlink multicell interference mitigation
techniques, namely, large-scale (LS) multiple-input multiple-output (MIMO) and
network MIMO. We consider a cooperative wireless cellular system operating in
time-division duplex (TDD) mode, wherein each cooperating cluster includes $B$
base-stations (BSs), each equipped with multiple antennas and scheduling $K$
single-antenna users. In an LS-MIMO system, each BS employs $BM$ antennas not
only to serve its scheduled users, but also to null out interference caused to
the other users within the cooperating cluster using zero-forcing (ZF)
beamforming. In a network MIMO system, each BS is equipped with only $M$
antennas, but interference cancellation is realized by data and channel state
information exchange over the backhaul links and joint downlink transmission
using ZF beamforming. Both systems are able to completely eliminate
intra-cluster interference and to provide the same number of spatial degrees of
freedom per user. Assuming the uplink-downlink channel reciprocity provided by
TDD, both systems are subject to identical channel acquisition overhead during
the uplink pilot transmission stage. Further, the available sum power at each
cluster is fixed and assumed to be equally distributed across the downlink
beams in both systems. Building upon the channel distribution functions and
using tools from stochastic ordering, this paper shows, however, that from a
performance point of view, users experience better quality of service, averaged
over small-scale fading, under an LS-MIMO system than a network MIMO system.
Numerical simulations for a multicell network reveal that this conclusion also
holds true with regularized ZF beamforming scheme. Hence, given the likely
lower cost of adding excess number of antennas at each BS, LS-MIMO could be the
preferred route toward interference mitigation in cellular networks.

本文研究了两种重要的下行多小区干扰抑制技术，即大规模 MIMO 和网络 MIMO，并通过随机排序的工具，论证了大规模 MIMO 系统在小尺度衰落平均质量服务方面优于网络 MIMO 系统，因此在降低蜂窝网络干扰方面，大规模 MIMO 是更优选择。

大规模 MIMO 与网络 MIMO 用于多小区干扰抑制的比较

Large-Scale MIMO versus Network MIMO for Multicell Interference  Mitigation

In this paper, a deterministic equivalent of ergodic sum rate and an
algorithm for evaluating the capacity-achieving input covariance matrices for
the uplink large-scale multiple-input multiple-output (MIMO) antenna channels
are proposed. We consider a large-scale MIMO system consisting of multiple
users and one base station with several distributed antenna sets. Each link
between a user and an antenna set forms a two-sided spatially correlated MIMO
channel with line-of-sight (LOS) components. Our derivations are based on novel
techniques from large dimensional random matrix theory (RMT) under the
assumption that the numbers of antennas at the terminals approach to infinity
with a fixed ratio. The deterministic equivalent results (the deterministic
equivalent of ergodic sum rate and the capacity-achieving input covariance
matrices) are easy to compute and shown to be accurate for realistic system
dimensions. In addition, they are shown to be invariant to several types of
fading distribution.

本文提出了大规模 MIMO 天线通道的确定性等效和评估容量实现输入协方差矩阵的算法。研究基于大维随机矩阵理论，在终端天线数量无限接近于无穷大的假设下做了相关推导。结果易于计算，精确性较高，并且不受多种衰落分布影响。