Real-Time Capability and Practical Feasibility of Attention-Enhanced Multi-Agent RL for Cooperative Spectrum Sensing

Ascertain the real-time capability and practical feasibility of the attention-enhanced multi-agent reinforcement learning approach for cooperative spectrum sensing in cognitive radio networks, and rigorously characterize its performance accuracy through comparative evaluation.

Background

Table III summarizes methods for spectrum sensing and signal classification. For the attention-enhanced multi-agent reinforcement learning scheme in cooperative spectrum sensing (reference [178]), the authors explicitly flag unresolved aspects regarding its real-time viability and practical deployment, as well as limitations in performance accuracy comparison.

Resolving these uncertainties is important for assessing whether such RL-based cooperative sensing methods can meet latency and resource constraints of real-world wireless networks and how they compare to established baselines.