面向6G的无线通信信道特性分析与建模

针对6G全覆盖、全频谱、全应用的发展愿景,对面向6G的全频谱全场景无线信道测量、信道特性与信道模型方面的进展进行了全面概述,侧重于毫米波、太赫兹、光波段、卫星、无人机、海洋、水声、高铁、车对车、大规模/超大规模天线、轨道角动量以及工业物联网等通信信道,并展示了6G信道的相关测量与建模结果。最后,指出了6G无线信道测量与建模研究的未来挑战。

AMP Dual-Turbo Iterative Detection and Decoding for LDPC Coded Multibeam MSC Uplink

The uplink of mobile satellite communication(MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output(MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receiver, which exchanges soft extrinsic information between a soft-in soft-out(SISO) detector and an SISO decoder in an iterative fashion, is an efficient method to reduce the uplink inter-beam-interference(IBI),and so the receiving bit error rate(BER).We propose to replace the linear SISO detector of traditional dual-turbo iterative detection and decoding with the AMP detector for the low-density parity-check(LDPC) coded multibeam MSC uplink. This improvement can reduce the computational complexity and achieve much lower BER.

LoS MIMO Transmission for LEO Satellite Communication Systems

To provide global service with low latency, the broadband low earth orbits (LEO) satellite constellation based communication systems have become one of the focuses in academic and industry. To allow for wideband access for user links, the feeder link of LEO satellite is correspondingly required to support high throughput data communications. To this end, we propose to apply line-of-sight (LoS) multiple-input multiple-output (MIMO) transmission for the feeder link to achieve spatial multiplexing by optimizing the antenna arrangement. Unlike the LoS MIMO applications for static scenarios, the movement of LEO satellites make it impractical to adjust the optimal antenna separation for all possible satellite positions. To address this issue, we propose to design the antenna placement to maximize the ergodic channel capacity during the visible region of the ground station. We first derive the closed-form probability distribution of the satellite trajectory in visible region. Based on which the ergodic channel capacity can be then calculated numerically. The antenna placement can be further optimized to maximize the ergodic channel capacity. Numerical results verify the derived probability distribution of the satellite trajectory, and show that the proposed LoS MIMO scheme can significantly increase the ergodic channel capacity compared with the existing SISO one.

Deep Learning-Based AMP for Massive MIMO Detection

Low-complexity detectors play an essential role in massive multiple-input multiple-output (MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate message passing (AMP) algorithm to the detection of massive MIMO transmission. To this end, we need to efficiently reduce the divergence occurrence in AMP iterations and bridge the performance gap that AMP has from the optimum detector while making use of its advantage of low computational load. Our solution is to build a neural network to learn and optimize AMP detection with four groups of specifically designed learnable coefficients such that divergence rate and detection mean squared error (MSE) can be significantly reduced. Moreover, the proposed deep learning-based AMP has a much faster converging rate, and thus a much lower computational complexity than conventional AMP, providing an alternative solution for the massive MIMO detection. Extensive simulation experiments are provided to validate the advantages of the proposed deep learning-based AMP.

Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance propagation.In satellite communication systems,channel acquisition and energy consumption are two critical issues.To this end,we investigate the tradeoff between the total energy efficiency(TEE)and minimum EE(MEE)for robust multigroup multicast satellite communication systems in this paper.Specifically,under the total power constraint,we investigate the robust beamforming aimed at balancing the TEE-MEE,so as to achieve the balance between the fairness and total performance on the system EE.For this optimization problem,we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user rate.Then,the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex procedure.In addition,depending on the solution rank value,Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible solutions.Finally,simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE,thus realizing a tradeoff between fairness and system EE performance.It is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.