Conventionally statistical path loss models are high-dimensional data-based without utilizing specific environment features. In this letter, a novel environment features-based model (EFBM) for path loss prediction is presented. We connect the propagation environment and channel by representing the environment with low-dimensional features: distance, deviation, volume, and blockage. The features are propagation-related, which can predict path loss directly by utilizing the Random Forest (RF) method. Compared with the data-based method, the proposed method can reduce the Root Mean Squared Error (RMSE) by 0.33 and 0.89 dB at 6 and 28 GHz and provide closer results to the Ray-Tracing (RT)-based ground-truth values.

In view of the propagation environment directly determining the channel properties, the applications can also be solved with the aid of environmental information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, we aim to provide a new view of the environment from the semantic level. This letter defines propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS). The PESS is extracted, which is oriented to the application tasks with concerned channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is investigated and implemented for non-line-of-sight (NLOS) scenarios. Environment features and graph representations are constructed for actions of channel quality evaluation and target scatterer detection with maximum power. It can obtain 0.92 and 0.9 precision, respectively, and save over 87% of the time cost.

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.