Opportunistic Access to Spectrum Holes Between Packet Bursts: A Learning-Based Approach

We present a cognitive radio (CR) mechanism for opportunistic access to the frequency bands licensed to a data-centric primary user (PU) network. Secondary users (SUs) aim to exploit the short-lived spectrum holes (or opportunities) created between packet bursts in the PU network. The PU traffic pattern changes over both time and frequency according to upper layer events in the PU network, and fast variation in PU activity may cause high sensing error probability and low spectrum utilization in dynamic spectrum access. The proposed mechanism learns a PU traffic pattern in real-time and uses the acquired information to access the frequency channel in an efficient way while limiting the probability of collision with the PUs below a target limit. To design the channel learning algorithm, we model the CR system as a hidden Markov model (HMM) and present a gradient method to find the underlying PU traffic pattern. We also analyze the identifiability of the proposed HMM to provide a condition for the convergence of the proposed learning algorithm. Simulation results show that the proposed algorithm greatly outperforms the traditional listen-before-talk algorithm which does not possess any learning functionality.