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.

IEEE Transactions on Cognitive Communications and Networking
Chance Tarver
Chance Tarver
Staff Research Engineer for Wireless Systems