On Spectral Intelligence in 6G URLLC Networks

Abstract

Cognitive radio (CR)-empowered 6G networks are deemed a key candidate technology to achieve ultra-reliable low latency communication (URLLC) with enhanced spectrum utilization efficiency. However, there are several challenges to address in achieving this objective. In particular, the sequential and random sensing performed by secondary users (SUs) to find idle channels within a given band in a CR network (CRN) leads to time and energy consumption, and processing overheads, which consequently cause early depletion of the device's energy, underutilization of the available spectrum, and prolonged delays in communication. To circumvent this problem, in this paper, a spectrum efficient scheme is proposed based on idle spectrum inference and ranking, which takes into account the devices' heterogeneity as well as their priorities in resource allocation. Based on the probabilistic approach, the scheme uses multiple parameters in a channel's evaluation and suitability assessment before selection for transmission. Markov chain modeling is leveraged to deal with the users' arrival and departure uncertainties and to derive expressions for core performance metrics, including service capacity and retainability, spectrum utilization efficiency, reliability, network unserviceable and handoff probabilities, channel availability, and communication latency. The scheme is analyzed under various patterns of users' arrivals. The acquired analytical and simulation results confirm the effectiveness of the proposed scheme compared to the state-of-the-art to realize URLLC applications.