Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing
This work addresses the problem of overcoming link degradation in IRS-aided wireless communication systems for wireless communication engineers, offering an incremental approach by integrating existing RC methods.
This paper proposes a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communication systems. By modeling RF impairments within IRS meta-atoms, the authors generalize the entire IRS-aided system as a reservoir computing (RC) system, leveraging the nonlinearity of the wireless environment to mitigate link degradation from model mismatch.
This article introduces a neural network-based signal processing framework for 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 that generalizes the entire IRS-aided system as a reservoir computing (RC) system, an efficient recurrent neural network (RNN) operating in a state near the "edge of chaos". This framework enables us to take advantage of the nonlinearity of this "fabricated" wireless environment to overcome link degradation due to model mismatch. Accordingly, the randomness of the wireless channel and RF imperfections are naturally embedded into the RC framework, enabling the internal RC dynamics lying on the edge of chaos. Furthermore, several practical issues, such as channel state information acquisition, passive beamforming design, and physical layer reference signal design, are discussed.