URLLC Latency Minimization in Interweave CRNs Using Digital Twin and DRL Approach

In this paper, we present an innovative approach to spectrum management in cognitive radio networks (CRNs) aimed at serving ultra-reliable low-latency communication (URLLC) enabled secondary users (SUs). Unmanned aerial vehicles (UAVs) are deployed for accurate and reliable spectrum sensing (SS), enhancing cooperative spectrum sensing (CSS) effectiveness. A distinctive aspect of our methodology is the integration of digital twin (DT) technology, which, to our knowledge, has not been explored previously in the context of CRNs for bandwidth assignment to URLLC-enabled SUs. This integration facilitates more sophisticated and adaptive management of spectrum resources. Moreover, we propose a deep reinforcement learning (DRL) framework incorporating a modified proximal policy optimization (MPPO) algorithm. This algorithm is designed for better stability and convergence, outperforming the standard PPO in terms of faster convergence in the present URLLC transmission latency minimization process. Simulation results indicate that our proposed DT-based spectrum management and MPPO in CRNs result in a 27.89% increase in CRN's average throughput and a 39.94% reduction in transmission latency compared to the conventional equal resource allocation scheme.