Semantic communications offer promising prospects for enhancing data
transmission efficiency. However, existing schemes have predominantly
concentrated on point-to-point transmissions. In this paper, we aim to
investigate the validity of this claim in interference scenarios compared to
baseline approaches. Specifically, our focus is on general multiple-input
multiple-output (MIMO) interference channels, where we propose an
interference-robust semantic communication (IRSC) scheme. This scheme involves
the development of transceivers based on neural networks (NNs), which integrate
channel state information (CSI) either solely at the receiver or at both
transmitter and receiver ends. Moreover, we establish a composite loss function
for training IRSC transceivers, along with a dynamic mechanism for updating the
weights of various components in the loss function to enhance system fairness
among users. Experimental results demonstrate that the proposed IRSC scheme
effectively learns to mitigate interference and outperforms baseline
approaches, particularly in low signal-to-noise (SNR) regimes.

语义通信在增强数据传输效率方面具有很大潜力。本文针对干扰情景与基准方案进行了比较研究，提出了一种抗干扰的语义通信方案。该方案基于神经网络（NNs）开发了收发机，它可以单独在接收端或发送端和接收端都集成信道状态信息（CSI）。我们还建立了一个综合损失函数来训练 IRSC 收发机，并采用动态机制来更新损失函数中各个部分的权重，以提高系统对用户的公平性。实验结果表明，所提出的 IRSC 方案能有效地学习抑制干扰，在低信噪比（SNR）环境中优于基准方案。

基于 MIMO 干扰信道的图像传输语义通信的基准测试

Benchmarking Semantic Communications for Image Transmission Over MIMO  Interference Channels

Radar as a remote sensing technology has been used to analyze human activity
for decades. Despite all the great features such as motion sensitivity, privacy
preservation, penetrability, and more, radar has limited spatial degrees of
freedom compared to optical sensors and thus makes it challenging to sense
crowded environments without prior information. In this paper, we develop a
novel dual-sensing system, in which a vision sensor is leveraged to guide
digital beamforming in a multiple-input multiple-output (MIMO) radar. Also, we
develop a calibration algorithm to align the two types of sensors and show that
the calibrated dual system achieves about two centimeters precision in
three-dimensional space within a field of view of $75^\circ$ by $65^\circ$ and
for a range of two meters. Finally, we show that the proposed approach is
capable of detecting the vital signs simultaneously for a group of closely
spaced subjects, sitting and standing, in a cluttered environment, which
highlights a promising direction for vital signs detection in realistic
environments.

本文介绍一种新型的双传感技术，其中视觉传感器用于引导 MIMO 雷达的数字波束成形，并提出一种校准算法将两种类型的传感器对齐，最后证明该方法能够在复杂环境中同时检测到坐立不同的一组人的生命体征。

面向拥挤环境的基于视觉引导的 MIMO 雷达波束成形增强生命迹象检测

Vision Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection  in Crowds

Given a multiple-input multiple-output (MIMO) channel, feedback from the
receiver can be used to specify a transmit precoding matrix, which selectively
activates the strongest channel modes. Here we analyze the performance of
Random Vector Quantization (RVQ), in which the precoding matrix is selected
from a random codebook containing independent, isotropically distributed
entries. We assume that channel elements are i.i.d. and known to the receiver,
which relays the optimal (rate-maximizing) precoder codebook index to the
transmitter using B bits. We first derive the large system capacity of
beamforming (rank-one precoding matrix) as a function of B, where large system
refers to the limit as B and the number of transmit and receive antennas all go
to infinity with fixed ratios. With beamforming RVQ is asymptotically optimal,
i.e., no other quantization scheme can achieve a larger asymptotic rate. The
performance of RVQ is also compared with that of a simpler reduced-rank scalar
quantization scheme in which the beamformer is constrained to lie in a random
subspace. We subsequently consider a precoding matrix with arbitrary rank, and
approximate the asymptotic RVQ performance with optimal and linear receivers
(matched filter and Minimum Mean Squared Error (MMSE)). Numerical examples show
that these approximations accurately predict the performance of finite-size
systems of interest. Given a target spectral efficiency, numerical examples
show that the amount of feedback required by the linear MMSE receiver is only
slightly more than that required by the optimal receiver, whereas the matched
filter can require significantly more feedback.

分析了随机向量量化在多输入多输出信道中的应用，探讨了随机码本选择预编码矩阵的性能表现，比较了此方法和约束在一个随机子空间内的降低秩标量量化方法的差异，结果表明这种方法具有良好的性能表现，只需略高于最优接收机所需的反馈量。