  Reconfigurable intelligent surface (RIS) has recently emerged as a promising
candidate to improve the energy and spectral efficiency of wireless
communication systems. However, the unit modulus constraint on the phase shift
of reflecting elements makes the design of optimal passive beamforming solution
a challenging issue. The conventional approach is to find a suboptimal solution
using the semi-definite relaxation (SDR) technique, yet the resultant
suboptimal iterative algorithm usually incurs high complexity, hence is not
amenable for real-time implementation. Motivated by this, we propose a deep
learning approach for passive beamforming design in RIS-assisted systems. In
particular, a customized deep neural network is trained offline using the
unsupervised learning mechanism, which is able to make real-time prediction
when deployed online. Simulation results show that the proposed approach
maintains most of the performance while significantly reduces computation
complexity when compared with SDR-based approach.
