When Optimization Meets Machine Learning: The Case of IRS-Assisted Wireless Networks
Performance optimization of wireless networks is typically complicated because of high computational complexity and dynamic channel conditions. Considering a specific case, the recent introduction of intelligent reflecting surface (IRS) can reshape the wireless channels by controlling the scattering elements' phase shifts, namely, passive beamforming. However, due to the large size of scattering elements, the IRS's beamforming optimization becomes intractable. In this article, we focus on machine learning (ML) approaches for complex optimization problems in wireless networks. ML approaches can provide flexibility and robustness against uncertain and dynamic systems. However, practical challenges still remain due to slow convergence in offline training or online learning. This motivated us to design a novel optimization-driven ML framework that exploits the efficiency of model-based optimization and the robustness of model-free ML approaches. Splitting the control variables into two parts allows one part to be updated by the outer loop ML approach while the other part is solved by the inner loop optimization. The case study in IRS-assisted wireless networks confirms that the optimization-driven ML framework can improve learning efficiency and the reward performance significantly compared to conventional model-free ML approaches. © 1986-2012 IEEE.
Artificial intelligence, Beamforming, Complex networks, Efficiency, Learning systems, Channel conditions, Dynamic channels, Machine learning approaches, Machine-learning, Model free, Optimisations, Performance optimizations, Reflecting surface, Scattering elements, Wireless channel, Wireless networks
S. Gong, J. Lin, B. Ding, D. Niyato, D. I. Kim and M. Guizani, "When Optimization Meets Machine Learning: The Case of IRS-Assisted Wireless Networks," in IEEE Network, vol. 36, no. 2, pp. 190-198, March/April 2022, doi: 10.1109/MNET.211.2100386.