Learning-Based Adaptive IRS Control With Limited Feedback Codebooks

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design their reflection coefficients. We consider a practical setting where (i) IRS coefficients are configured by adjusting tunable elements embedded in (ii) affected incident angles incoming signals, (iii) is deployed multi-path, time-varying channels, and (iv) feedback link from base station to has low data rate. Conventional optimization-based control protocols, rely on channel estimation conveying optimized variables IRS, not applicable this due difficulty Therefore, we develop novel adaptive codebook-based limited protocol only codeword index transferred IRS. propose two solutions for codebook design, random adjacency (RA) deep neural network policy-based (DPIC), both require end-to-end compound channels. further several augmented schemes based RA DPIC. Numerical evaluations show that rate average over one coherence time improved substantially our schemes.