Interference modeling and adaptive suppression algorithm for HPLC and 920MHz wireless fusion communication in complex building environments

Abstract

The interference coupling of high-speed power line carrier (HPLC) communication exacerbates the frequency-domain tail between subcarriers, reduces the sensitivity of suppression methods, and results in poor communication quality. This article focuses on the interference problem of HPLC and 920MHz-925MHz wireless communication fusion system in typical urban building environments in Hong Kong, and conducts research on interference modeling and adaptive suppression algorithms. Firstly, in response to the signal instability caused by multipath fading and occlusion, this paper constructs a composite interference recognition model based on short-time Fourier transform (STFT) and deep learning (DL). This model takes the time-frequency domain information obtained from STFT as input and uses the YOLOv5 (You Only Look Once) algorithm to achieve spatial localization and classification of interference signals, while identifying interference types and signal-to-noise ratios, improving the perception ability of composite interference. On this basis, the anti-interference problem of multi-node communication is modeled as a Markov game, and a fast anti-interference algorithm based on transfer reinforcement learning (TrRL) is proposed. Combining multi-agent Q-learning and value function transfer mechanism, adaptive spectrum access and power control in dynamic environments are achieved. Simulation experiments show that the proposed method significantly improves communication success rate and system robustness in complex building environments.