Collaborative Transmission and Resource Management in IRS-Aided Wireless-Powered Mobile Edge Computing Systems

The evolution of computing paradigms has been significantly influenced by the emergence of wireless-powered mobile edge computing (WP-MEC), fundamentally transforming resource-efficient processing at the network edges. Intelligent reflecting surfaces (IRSs) integrated with WP-MEC offer new opportunities by enhancing the channel quality while addressing the complex resource management challenges. To address this, a collaborative transmission and resource management scheme for communication, energy, and computation is provided in this article. In particular, a novel performance evaluation index, named energy cost utility is presented first to capture the IRS-aided coupling performances thoroughly. Subsequently, a collaborative transmission mechanism addressing communication, energy transmission, and edge computing issues is developed, leveraging adaptive IRS association. Furthermore, to enhance the system performance, a hierarchical optimization framework for the resource management with limited computation capability is proposed, which includes the upper-layer optimization-based IRS association and resource allocation, along with the lower-layer deep reinforcement learning-based active and passive beamforming, aimed at maximizing the energy cost utility. Compared to the other benchmark schemes, our proposed collaborative transmission and resource management approach demonstrates the ability to learn from the environment and improve behavior gradually and exhibits superiority in enhancing transmission quality and reducing energy consumption. Also, appropriate neural network parameters will significantly improve the performance and convergence rate of the proposed algorithm. Finally, the advantages of the IRS association regarding quantity and configuration for enhancing the energy cost utility are explored, highlighting its potential to shape the future of the Internet of Things.