Enabling a “Use-or-Share” Framework for PAL–GAA Sharing in CBRS Networks via Reinforcement Learning

By implementing reinforcement learning-aided listen-before-talk (LBT) schemes over a citizens broadband radio service (CBRS) network, we increase the spatial reuse at secondary nodes while minimizing the interference footprint on higher-tier nodes. The federal communications commission encourages “use-or-share” policies in the CBRS band across the priority access license (PAL)–general authorized access (GAA) priority tiers by opportunistically allowing the lower-priority GAA nodes to access unused higher-priority PAL spectrum. However, there is currently no mechanism to enable this cross-tier spectrum sharing. In this paper, we propose and evaluate LBT schemes that allow opportunistic access to PAL spectrum. We find that by allowing LBT in a two carrier, two eNB scenario, we see upward of 50% user perceived throughput (UPT) gains for both eNBs. Furthermore, we examine the use of ${Q}$ -learning to adapt the energy-detection threshold (EDT), combating problematic topologies, such as hidden and exposed nodes. With merely a 4% reduction in primary node UPT, we see up to 350% gains in average secondary node UPT when adapting the EDT of opportunistically transmitting nodes.