Intelligent reflecting surface (IRS) is an emerging technology for future
wireless communications including 5G and especially 6G. It consists of a large
2D array of (semi-)passive scattering elements that control the electromagnetic
properties of radio-frequency waves so that the reflected signals add
coherently at the intended receiver or destructively to reduce co-channel
interference. The promised gains of IRS-assisted communications depend on the
accuracy of the channel state information. In this paper, we address the
receiver design for an IRS-assisted multiple-input multiple-output (MIMO)
communication system via a tensor modeling approach aiming at the channel
estimation problem using supervised (pilot-assisted) methods. Considering a
structured time-domain pattern of pilots and IRS phase shifts, we present two
channel estimation methods that rely on a parallel factor (PARAFAC) tensor
modeling of the received signals. The first one has a closed-form solution
based on a Khatri-Rao factorization of the cascaded MIMO channel, by solving
rank-1 matrix approximation problems, while the second one is an iterative
alternating estimation scheme. The common feature of both methods is the
decoupling of the estimates of the involved MIMO channel matrices (base
station-IRS and IRS-user terminal), which provides performance enhancements in
comparison to competing methods that are based on unstructured LS estimates of
the cascaded channel. Design recommendations for both methods that guide the
choice of the system parameters are discussed. Numerical results show the
effectiveness of the proposed receivers, highlight the involved trade-offs, and
corroborate their superior performance compared to competing LS-based
solutions.

本文针对基于智能反射面（Intelligent reflecting surface，IRS）的多输入多输出（MIMO）通信系统，提出了两种基于并行因式分解（PARAFAC）张量建模的 IRS 辅助信道估计方法，分别采用基于 Khatri-Rao 分解和交替最小二乘方法，测试结果显示相比 LS 估计基准方案，提出的方法在通信质量和性能均有明显提升。

智能反射面辅助 MIMO 系统的通道估计：张量建模方法

Channel Estimation for Intelligent Reflecting Surface Assisted MIMO  Systems: A Tensor Modeling Approach

Channel estimation is useful in millimeter wave (mmWave) MIMO communication
systems. Channel state information allows optimized designs of precoders and
combiners under different metrics such as mutual information or
signal-to-interference-noise (SINR) ratio. At mmWave, MIMO precoders and
combiners are usually hybrid, since this architecture provides a means to
trade-off power consumption and achievable rate. Channel estimation is
challenging when using these architectures, however, since there is no direct
access to the outputs of the different antenna elements in the array. The MIMO
channel can only be observed through the analog combining network, which acts
as a compression stage of the received signal. Most of prior work on channel
estimation for hybrid architectures assumes a frequency-flat mmWave channel
model. In this paper, we consider a frequency-selective mmWave channel and
propose compressed-sensing-based strategies to estimate the channel in the
frequency domain. We evaluate different algorithms and compute their complexity
to expose trade-offs in complexity-overhead-performance as compared to those of
previous approaches.

本文研究基于压缩感知技术在频域中对毫米波 MIMO 通信中的混合预编码中的信道估计进行改进，并与之前的方法进行比较，揭示了复杂度、性能和开销之间的权衡。

面向频率选择混合毫米波 MIMO 系统的频域压缩信道估计

Frequency-domain Compressive Channel Estimation for Frequency-Selective  Hybrid mmWave MIMO Systems

MIMO communication may provide high spectral efficiency through the
deployment of a very large number of antenna elements at the base stations. The
gains from massive MIMO communication come from the use of multi-user MIMO on
the uplink and downlink, but with a large excess of antennas at the base
station compared to the number of served users. Initial work on massive MIMO
did not fully address several practical issues associated with its deployment.
This paper considers the impact of channel aging on the performance of massive
MIMO systems. The effects of channel variation are characterized as a function
of different system parameters assuming a simple model for the channel time
variations at the transmitter. Channel prediction is proposed to overcome
channel aging effects. The analytical results on aging show how capacity is
lost due to time variation in the channel. Numerical results in a multicell
network show that massive MIMO works even with some channel variation and that
channel prediction could partially overcome channel aging effects.

本研究考虑了信道老化对大规模 MIMO 系统性能的影响及其预测方法。结果表明，即使存在一定的信道变化，大规模 MIMO 系统仍能工作，并且信道预测可以部分地克服信道老化效应。