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Massive MIMO obtains the multiuser performance gain based on the favorable propagation (FP) assumption, defined as the mutual orthogonality of different users’ channel vectors. Until now, most of the theoretical analyses of FP are based on uniform angular distributions and only consider the horizontal dimension. However, the real propagation channel contains full dimensions, and the spatial angle varies with the environment. Thus, it remains unknown whether the FP condition holds in real deployment scenarios and how it impacts the massive MIMO system performance. In this paper, we analyze the FP condition theoretically based on a cluster-based three-dimensional (3D) MIMO channel with generalized angle distributions. Firstly, the FP condition’s unified mathematical expectation and variance expressions with full-dimensional angular integral are given. Since the closed-form expressions are hard to derive, we decompose generalized angle distributions, i.e., wrapped Gaussian (WG), Von Mises (VM), and truncated Laplacian (TL) into the functions of Bessel and Cosine basis by introducing Jacobi-Anger expansions and Fourier series. Thus the closed-form expressions of the FP condition are derived. Based on the above, we theoretically analyze the asymptotically FP condition under generalized angle distributions and then compare the impact of angular spreads on the FP performance. Furtherly, the FP condition is also investigated by numerical simulations and practical measurements. It is observed that environments with larger angle spreads and larger antenna spacing are more likely to realize FP. This paper provides valuable insights for the theoretical analysis of the practical application of massive MIMO systems.

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Due to its significant efficiency, semantic communication emerges as a promising technique for sixth-generation (6G) networks. The wireless propagation channel plays a crucial role in system design, as it directly impacts transmission performance and capability. Given the increasingly complex communication scenarios, the channel exhibits high dynamism and poses challenges in acquisition. In such cases, sensing-based methods have drawn significant attention. To enhance system robustness, we propose a predictive channel-based semantic communication (PC-SC) system tailored for sensing scenarios. The PC-SC system is designed with an orientation toward applications by directly taking semantic targets into account. It comprises three modules: transmitter, predictive channel, and receiver. Firstly, at the transmitter, instead of employing global semantic coding, the scheme emphasizes preserving semantic information through target-based semantic extraction. Secondly, the channel prediction module predicts the dynamic wireless channel by utilizing the extracted target-based semantic information. Finally, at the receiver, the target-based semantic information can be utilized to meet specific application requirements. Alternatively, pre-captured background and semantic targets can be composited to fulfill complete image reconstruction needs. We evaluate the proposed approach by using a sensing image transmission scenario as a case study. Experimental results demonstrate the superiority of the PC-SC system in terms of image reconstruction performance and cost savings of bit. We employ beam prediction as a channel prediction task and find that the targets-based method outperforms the complete image-based approach in terms of efficiency and robustness, which can provide 32% time-saving.