Performance of cognitive radio networks using reconfigurable intelligent surfaces with RF energy harvesting for Nakagami channels

In this paper, we derive the performance of cognitive radio networks (CRNs) with energy harvesting using reconfigurable intelligent surfaces (RISs) for Nakagami fading channel with m-fading figure M. We derive the detection probability when the primary source (PS) harvests energy using radio frequency (RF) signals. A RIS is located between PS and secondary source (SS) where spectrum sensing is performed. We also derive the primary and secondary throughput and optimise harvesting duration to maximise the throughput. We observed significant performance enhancement in detection probability and throughput with respect to CRN without RIS.