Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks

Abstract

The efficient integration of communication and computation in the internet of things (IoT) presents new opportunities for enhancing system performance but still faces challenges such as interference management, resource allocation and task scheduling. To address these issues, this paper proposes a semantic-aware intelligent optimization framework that combines unmanned aerial vehicles (UAVs) and intelligent reflecting surface (IRS) with mobile edge computing (MEC) to enhance communication quality and semantic awareness in wideband cognitive radio networks. The proposed semantic-aware optimization framework incorporates semantic information to achieve more efficient task scheduling and resource allocation. Particularly, the proposed optimization framework jointly optimizes UAV trajectories, subcarrier allocation, IRS reflection coefficients, task offloading ratios, task priorities and contextual relevance to maximize semantic utility and system energy efficiency while dynamically ensuring task demands. Furthermore, to tackle the non-convexity caused by highly coupled optimization variables, we employ a deep reinforcement learning algorithm based on double deep Q-network and twin delayed deep deterministic policy gradient (DDQN-TD3). Simulation results demonstrate that the proposed approach significantly outperforms baseline schemes by better aligning with user priorities, task requirements, and contextual awareness, leading to improved task completion rates and semantic utility, providing an innovative optimization solution for wideband cognitive radio networks.