Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks
This work addresses performance optimization in IRS-assisted wireless networks, offering a hybrid solution that is incremental in nature.
The paper tackles the challenge of high computational complexity and inexact channel information in passive beamforming for IRS-assisted wireless networks by proposing an optimization-driven ML framework that combines model-based optimization efficiency with model-free ML robustness. Numerical results show that this approach improves both convergence and reward performance compared to conventional model-free learning methods.
Intelligent reflecting surface (IRS) has been recently employed to reshape the wireless channels by controlling individual scattering elements' phase shifts, namely, passive beamforming. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity and inexact channel information. In this article, we focus on machine learning (ML) approaches for performance maximization in IRS-assisted wireless networks. In general, ML approaches provide enhanced flexibility and robustness against uncertain information and imprecise modeling. Practical challenges still remain mainly due to the demand for a large dataset in offline training and slow convergence in online learning. These observations motivate us to design a novel optimization-driven ML framework for IRS-assisted wireless networks, which takes both advantages of the efficiency in model-based optimization and the robustness in model-free ML approaches. By splitting the decision variables into two parts, one part is obtained by the outer-loop ML approach, while the other part is optimized efficiently by solving an approximate problem. Numerical results verify that the optimization-driven ML approach can improve both the convergence and the reward performance compared to conventional model-free learning approaches.