Beam Interference Identification and Sinr Prediction in Millimeter Wave Systems

Millimeter wave (mmWave) has emerged as a key technology for next-generation communications to address spectrum scarcity in traditional low-frequency wireless systems. However, its dynamic channel, high propagation losses and blocking susceptibility impose higher demands on resource allocation in complex interference environments, especially in factory scenarios. Efficient resource allocation in mmWave systems relies on accurately identifying and modeling interference, but current prevalent research remains constrained by idealized channel state information (CSI) assumptions and statistical modeling approaches that focus on stable CSI and consistent interference and noise. These conventional methods are inadequate for transient systems with instantaneous channel variations. To overcome these challenges, this paper proposes an interference identification model based on directional beams, which integrates the cluster scattering characteristics of mmWave signals and formulates the received power as the sum of the directional beam gain-weighted multipath powers. Using intelligent algorithms, we train this model to precisely identify interference and match arbitrary input resource relationships to achieve accurate signal-to-interference-plus-noise ratio (SINR) prediction for supporting the subsequent resource allocation. Numerical results demonstrate that the proposed algorithm improves the prediction accuracy by 4.7% − 35.1% compared to the benchmark scheme.