Opportunity prediction at MAC-layer sensing for ad-hoc cognitive radio networks

Knowledge of channel usage pattern of primary users (PUs) helps in predicting future channel availability which can reduce MAC-layer sensing overhead in cognitive radio networks. In this work, two important issues of MAC-layer sensing have been investigated for underlay mode cognitive radio networks. These are - (a) estimation and modeling of licensed channel usage pattern of PUs, while tolerating interference from secondary users (SUs), and (b) usage of learnt channel usage patterns for discovery of opportunities by the SUs. Accordingly, a Hidden Markov Model (HMM) based channel usage pattern of PUs is proposed for use by the SUs to predict the spectrum opportunity. The proposed model uses estimated interference power constraint (IPC) in determining the interference due to presence of SUs to protect the PUs from harmful interference. A formulation deriving the availability metric (AM) for licensed channels is developed which helps in selecting the best channel by an SU for its transmission needs. Experimental results show that the trained HMMs can be used for predicting future channel availability, provided the same IPC condition prevails for a certain period. It is also observed that the AM of the channel sequences generated by the trained HMMs is effective in selecting a suitable channel for transmission. Furthermore, a distributed medium access control protocol for data dissemination (DMDD) in underlay mode CRNs is proposed which utilizes the proposed channel usage model. Simulation based results have shown the effectiveness of the proposed channel usage model.