Analysis of opportunistic spectrum access in cognitive radio networks using hidden Markov model with state prediction
We propose a study using hidden Markov model (HMM) with state prediction for opportunistic spectrum access (OSA) in cognitive radio (CR) networks. The primary channels are assumed to be operating in a TDMA manner with synchronous time slots of equal length and alternating between idle and busy states. The secondary users (SUs) may use them when they are idle by channel sensing. In contrast to the traditional scheme relying only on channel sensing for exploring spectrum opportunities, the proposed prediction scheme takes advantage of state prediction, channel sensing, and acknowledgments (ACKs) from the receiver in an attempt to maximize the utility. In the prediction scheme, there are three distinct actions: direct skip, sensing then conditional access, and direct access. We impose some constraints on the system parameters and derive thresholds by which we can specify the optimal action. We then conduct simulations to compare the performance of the prediction scheme to that of the traditional scheme. Results show that the former is superior to the latter. We believe the proposed prediction scheme is suitable for the OSA system using spectrum sensing.
hidden markov model (hmm) opportunistic spectrum access (osa) cognitive radio (cr) bayesian filter