Dual Factors Analysis for Energy Efficient Routing in Cognitive Radio Networks: Lightweight DRL-WsOA

Abstract

Cognitive radio networks (CRNs) has significant potential for optimizing spectrum usage, but they still face issues, such as high energy consumption, increased transmission delay, and inefficient routing due to unpredictable network performance and spectrum variance. Relatively new developments, including deep reinforcement learning (DRL), allow for simultaneous routing and resource management, but still need substantial number of trial and error interactions with the environment, which can consume energy and lead to a significant convergence time. Maintaining optimal connectivity with both primary users (PUs) and secondary users (SUs) in large scale CRNs following a homogenous Poisson process while optimally utilizing spectrum access remains a challenge. Towards advancing these problems, we propose an energy-aware, cross-layer routing design based on an apprenticeship learning framework. Our multi-stage dynamic adjustment rating (DAR) mechanism allows for effective tuning of transmit power to decrease action space (following a multi-level transition) and reduce energy consumption. The Whale Swarm Optimization Algorithm (WsOA) allows us to provide predictions of link connectivity and path probability to ensure a more reliable routing selection is presented. To ensure faster convergence and a minimal memory footprint, we incorporate Deep Q-learning with a rapidly converging local search method based on permutation-equivariant neural networks for unseen environments in the given network scenarios. The simulation results show that the suggested approach outperforms conventional algorithms like CRQ-routing, PM-DQN, and other DQL methods in terms of throughput (100-70) pt/s, routing delay (5-1) ms, packet delivery ratio (100-70) % and Percentage packet loss (5-1) %.