Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

no code implementations • 7 May 2022 • JungHoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum, Taejoon Kim, David J. Love, Christopher G. Brinton

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients.

reinforcement-learning Reinforcement Learning (RL)

Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

no code implementations • 3 Dec 2021 • JungHoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum, Taejoon Kim, David J. Love, Christopher G. Brinton

We consider a practical setting where (i) the IRS reflection coefficients are achieved by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate.