MAC and Routing Protocols for Multi-Hop Cognitive Radio Networks
Type of DegreeThesis
Electrical and Computer Engineering
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Cognitive radio networks that allow dynamic spectrum access are considered spectrally more efficient than networks using fixed spectral allocation. These networks are characterized by dynamically changing channel sets at each node. Multi-hop cognitive radio network is a cooperative network in which cognitive users take help of their neighbors to forward data to the destination. Control signals used to enable cooperation are communicated through a common control channel (CCC). Such a usage introduces conditions like channel saturation which degrades the overall performance of the network. Thus, exchanging control information is a major challenge in cognitive radio networks. Moreover, the graph theoretic approach used in traditional multi-hop networks fails to efficiently model multi-hop cognitive radio networks and capture the required information for optimal routing. Hence, conventional graph-based routing protocols such as DSR or AODV cannot be used directly, for route discovery in such networks. Two ideas are proposed in this thesis as a solution to the above problems. Firstly, a MAC protocol for multi-hop cognitive radio networks is proposed to avoid a common control channel. The scheme is applicable in heterogeneous environments where channels have different bandwidths and frequencies of operation. It inherently provides a solution to issues like CCC saturation problem, Denial of Service attacks (DoS) and multi-channel hidden problem. The proposed protocol is shown to provide better connectivity and higher throughput than a CCC based protocol, especially when the network is congested. Secondly, a unique multi-edge planar graph model for routing is proposed which can efficiently represent a multi-hop cognitive radio network. The model is quite simple and could be used in conjunction with any conventional graph-based routing protocol. The model is validated through simulations and the complexity of the model is shown to be lesser than an earlier layered graph model.