How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

TL;DR

This paper introduces a novel semantic-based approach to characterize propagation environments for beam prediction, leveraging environment semantics to improve accuracy and efficiency in NLOS scenarios.

Contribution

It defines propagation environment semantics as a set of symbols for diverse tasks, validated through a PES-aided beam prediction method that enhances precision and reduces time cost.

Findings

Achieved 0.92 precision in channel quality evaluation

Achieved 0.9 precision in target scatterer detection

Reduced time cost by over 87%

Abstract

In view of the propagation environment directly determining the channel fading, the application tasks can also be solved with the aid of the environment information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, this work aims to provide a new view of the environment from the semantic level, which defines the propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS) for diverse application tasks. The PESS is extracted oriented to the tasks with channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is presented in non-line-of-sight (NLOS). The PESS of environment features and graphs are given for the semantic actions of channel quality evaluation and target scatterer detection of maximum power, which can obtain 0.92 and 0.9 precision, respectively, and save over 87% of time cost.