Risk-aware dynamic spectrum allocation using learning-augmented RIS control for cognitive radio networks

Mallikarjun Dheshmuk (BLDEA’s V. P. Dr. P. G. Halakatti College of Engineering and Technology (Affiliated to Visvesvaraya Technological University))
Santosh B. Kumbalavati (Basaveshwar Engineering College (Affiliated to Visvesvaraya Technological University))

Bulletin of Electrical Engineering and Informatics
Vol 15, No 3: June 2026

The proposed RADIANT-CRN framework introduces a risk-aware dynamic spectrum allocation approach for reconfigurable intelligent surface (RIS)-enabled cognitive radio networks while maintaining reliable protection for primary users (PUs). It incorporates our previously developed bidirectional long short-term memory and adaptive manta-ray foraging optimization (BiLSTM-AMRFO) spectrum prediction and deep channel estimation models into a bilevel optimization framework, where semidefinite relaxation (SDR) adjusts RIS phase shifts, geometric programming (GP) allocates transmit power, and entropy-regularized assignment performs channel selection. A primal-dual actor–critic framework coordinates these modules. On a 120 MHz testbed with 256-element RIS, RADIANT-CRN gets a sum rate of 846±18 Mbps with a 0.7±0.2% PU-violation and 99.3% chance-constraint coverage. This is about 25% higher than a greedy non-risk baseline and about 43% higher than a no-RIS optimizer. It also lowers interference CVaR_0.95 from 9.1 mW to 2.4 mW. These results demonstrate that RADIANT-CRN is the first framework to enforce both chance and CVaR guarantees in RIS-assisted CRNs, achieving high spectral efficiency with statistically certifiable PU protection that aligns with Federal Communications Commission interference requirements. The framework is validated on a prototype SDR testbed (Rician fading, K=6 dB, bursty PU activity); implementation uses Python 3.11/PyTorch 2.x with convex optimization python (CVXPy/MOSEK), and synthetic testbed traces are available upon request.