In this paper, we consider an intelligent reflecting surface (IRS)-aided
cell-free massive multiple-input multiple-output system, where the beamforming
at access points and the phase shifts at IRSs are jointly optimized to maximize
energy efficiency (EE). To solve EE maximization problem, we propose an
iterative optimization algorithm by using quadratic transform and Lagrangian
dual transform to find the optimum beamforming and phase shifts. However, the
proposed algorithm suffers from high computational complexity, which hinders
its application in some practical scenarios. Responding to this, we further
propose a deep learning based approach for joint beamforming and phase shifts
design. Specifically, a two-stage deep neural network is trained offline using
the unsupervised learning manner, which is then deployed online for the
predictions of beamforming and phase shifts. Simulation results show that
compared with the iterative optimization algorithm and the genetic algorithm,
the unsupervised learning based approach has higher EE performance and lower
running time.Comment: 6 pages, 4 figure