Spectrum-learning-aided reconfigurable intelligent surfaces for 'Green' 6G networks

In the sixth generation (6G) era, emerging large-scale computing-based applications (e.g., processing enormous amounts of images in real time in autonomous driving) tend to lead to excessive energy consumption for end users, whose devices are usually energy-constrained. In this context, energy efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize 'green' 6G networks. As a remedy, reconfigurable intelligent surfaces (RISs) have been proposed for improving energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-to-interference-plus-noise ratio (SINR) sometimes may become degraded. This is because the signals impinging on an RIS are typically contaminated by interfering signals that are usually dynamic and unknown. To address this issue, 'learning' the properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, referred to here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for intelligently exploiting the inherent characteristics of the radio frequency spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently 'thinking and deciding' whether or not to reflect the incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityNational Research Foundation (NRF)This research is supported by MOE Tier 2 MOE000168-01 and A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund – Pre Positioning (IAFPP) (Grant No. A19D6a0053). The work of C. Huang was supported in part by the National Natural Science Foundation of China under Grant 62101492, Zhejiang University Education Foundation Qizhen Scholar Foundation, and Fundamental Research Funds for the Central Universities under Grant 2021FZZX001-21. The work of M. Di Renzo was supported in part by the European Commission through the H2020 ARIADNE project under grant agreement number 871464 and through the H2020 RISE-6G project under grant agreement number 101017011. The work of D. Niyato was supported in part by the National Research Foundation, Singapore under the AI Singapore Programme (AISG) (AISG2-RP-2020-019), WASP/NTU grant M4082187 (4080) and Singapore Ministry of Education (MOE) Tier 1 (RG16/20). The work of L. Hanzo was supported in part by the Engineering and Physical Sciences Research Council projects EP/P034284/1 and EP/P003990/1 (COALESCE) as well as the European Research Council’s Advanced Fellow Grant QuantCom (Grant No. 789028)