Multimodal Learning Optimization for Enhancing Channel Usage in Wireless Communication

Abstract

Cooperative spectrum processing is a key factor for improving channel utilization in wireless communication. Optimal utilization and allocation control of the available channels improve the rate of communication reliability. This manuscript introduces a multimodal learning optimization (MLO) technique for ensuring optimal channel usage (CU) in wireless communications. MLO adapts cooperative communications to leverage the throughput of wireless users. The overlapping problem that causes deterioration of wireless throughput is resolved through feature-based channel assignment. The proposed technique focuses on the CU limitation problem caused due to multi-hop communication and interference. Interference and multi-hop constraint is addressed as a dynamic optimization problem (DOP) for selecting precise channels to improve throughput and channel access rate. The analysis of varying communication parameters in DOP is modeled as a bipartite graph in MLO to classify the channel selection features. Channel selection by suppressing interference and joint constraints is independently identified for both single- and multi-hop communications based on the extracted features. The performance of MLO-CU is assessed based on the metrics: throughput, outage, channel utilization and peak transmission factor.