On transitional probabilistic routing in cognitive radio mesh networks

Adaptability and accuracy in decision making of routing protocols play an important role in the performance of the network. This role is even more crucial in a cognitive radio mesh network operating in a densely populated urban area where the environment is dynamic due to the erratic nature of spectrum availability and spectrum diversity. In this work, a probability distribution called ArgMax is proposed, which can be used as the transitional probability distribution in probabilistic routing and selective protocols. A probabilistic selection routing procedure (PSRP) is also proposed that adopts ArgMax probability distribution to guide packets throughout the network. We have compared the performance of PSRP using ArgMax with PSRP incorporating the well-known and frequently used distribution Odds-On-Mean (OOM) in evaluating its transitional probability distribution. OOM is the distribution that is used in many MAC and routing protocols that select the next node probabilistically. The simulation result suggests that ArgMax enables the routing scheme to adapt to the network dynamic more quickly, and locates the best candidate to route to, more accurately. Hence, the network throughput increased and the end-to-end delay improved.