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This article introduces a neural network-based signal processing frameworkfor intelligent reflecting surface (IRS) aided wireless communications systems.By modeling radio-frequency (RF) impairments inside the "meta-atoms" of IRS(including nonlinearity and memory effects), we present an approach thatgeneralizes the entire IRS-aided system as a reservoir computing (RC) system,an efficient recurrent neural network (RNN) operating in a state near the "edgeof chaos". This framework enables us to take advantage of the nonlinearity ofthis "fabricated" wireless environment to overcome link degradation due tomodel mismatch. Accordingly, the randomness of the wireless channel and RFimperfections are naturally embedded into the RC framework, enabling theinternal RC dynamics lying on the edge of chaos. Furthermore, several practicalissues, such as channel state information acquisition, passive beamformingdesign, and physical layer reference signal design, are discussed.

Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing