Abstract
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.
Highlights
The spectrum resources are rare, and a large part of the spectrum licensed for various applications are not fully used in times or in spaces
As secondary users (SUs) cannot know the channel state exactly, we further model each channel as an hidden Markov model (HMM), which may be viewed as a discrete-time bi-variable random process {S(t), O(t)}, where t = 1, 2, · · ·, is the discrete time, S(t) is the hidden process, and O(t) is the observable process having states as the hidden process
We imposed some constraints on the system parameters and derived thresholds by which we can specify the optimal action which maximizes the utility
Summary
The spectrum resources are rare, and a large part of the spectrum licensed for various applications are not fully used in times or in spaces. With imperfect sensing and by considering the fact that actions taken by SUs do not affect the evolution of the channel state, we model the system as a hidden Markov model (HMM) [13-20]. When a busy state is observed, denoted by o1, if the hidden process is idle, we say a false alarm occurs and denote this probability by pfa; otherwise we have a correct detection, denoting this probability by pd. Let πj(t) denote the one-step ahead prediction probability of the hidden process being in state sj, expressed by πj(t) = pijπi(t − 1) These equations show that an update of the Bayesian filter may be viewed as composed of two major steps. If the hidden process is predicted to be idle with high probability, one may avoid channel sensing and directly transmit.
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