Research On Routing Algorithms In Cognitive Wireless Mesh Networks

The success of wireless technology has generated an explosive demand for wireless spectrum, thereby creating an expected shortage of the bandwidth resource. Cognitive radio improves the utilization of spectrum resources as a key solution to this shortage problem. The science and industry have proposed a few of network architectures for cognitive radio networks. Wireless Mesh Network is a typical example that supports multi-channel multi-interface among those. This kind of network is called as Cognitive Wireless Mesh Networks (Cognitive Wireless Mesh Networks, CogWMNs). CogWMN is a kind of intelligent wireless network which can make intelligent decisions to modify transmission parameters based on surrounding environment cognition such as routing and channel assignment during coordination.Nodes in Wireless Mesh Networks based on Cognitive Radio environment can change their communication frequency actively. When they look for routing and schedule channels,channel allocation seems unbalanced commonly. In order to increase the utility of licensed bands and increase the throughput with multi-channel, a kind of joint routing and channel assignment strategy based on channel statistics metric with priorities without common control channel is proposed in this paper. This scheme may handle the unbalanced channel allocation problem and the mesh nodes in network can access AP in fewer hops. The simulation results show that the proposed strategy can increase the throughput performance and reduce network time-delay.Nodes in CogWMN can access spectrum holes dynamically. However, frequent changes of spectrum holes result in breaking the routes and degrading network performance. In this paper, a CogWMN routing algorithm based on fuzzy reasoning is proposed. The algorithm adopts reasoning mechanism of fuzzy Petri net to compute the reliabilities of available routes. Finally, the highest reliability of routes and channels is selected. The simulation results show that reasoning mechanism routing algorithm may enhance the reliabilities of selected routes. The proposed algorithm can not only decrease the number of broken routes but also improve network throughput in CogWMN.Nodes perceive environment and select the appropriate route for business transfers in Cognitive Wireless Mesh Network. However, paths alter with the changing spectrum holes that give negative effects to transmissions. An adaptive multi-path algorithm, which uses multi-agent reinforcement learning, is proposed in this paper. Better route can always be used to transmit data when route changes. The simulation results show that the algorithm can reduce network latency and improve network performance.