Research On Topology Control In Cognitive Ad Hoc Network

Nowadays, with rapid development of wireless telecommunication system, numerous wireless telecommunication techniques have been put forward. However, the problem of spectrum shortage hasn't been perfectly solved. Under this circumstance, the cognitive radio has been proposed. Integrating cognitive radio techniques into conventional Ad-hoc network, Cognitive Ad-hoc network has been proved to be an effective way of improving the spectrum usage. And this type of network has already brought a lot of attention to many relative research institutions and researchers. In this area, spectrum detection, spectrum sharing and channel allocation have always been hot topics. In this paper, channel allocation in Cognitive Ad-hoc has been mainly analyzed. What's more, a type of topology control mechanism with robustness optimization has been targeted in this paper. Based on this type of mechanism, two optimized schemes have been proposed. One of the two schemes was adopted by the greedy algorithm mechanism, and the other one was aiming at improving the rationality of the cognitive Ad-hoc network by adding action prediction of primary user into the scheme.The main work and the innovation point of this paper can be concluded into following three parts:1. Analyzing the topology control algorithm with robustness optimization. By means of looking up a massive number of related literatures and documents, relevant channel allocation techniques in cognitive Ad-hoc network have been studied, among which the topology control algorithm with robustness optimized has been mainly analyzed.2. Adopting the greedy algorithm mechanism for the channel allocation scheme. In the original topology control algorithm with robustness optimization, the order of the channel allocation is organized by the usage counter of the channel itself. To some extent, the interference between the cognitive user can be alleviated by applying this type of channel allocation strategy. However, using this strategy can only reduce the probability of interference in the network, which means the actual interference between randomly 2 users can't be alleviated. Aiming at solving this type of problem, a greedy mechanism has been applied to the original topology algorithm by applying the quantification of the interference when it comes to channel allocating, then calculating the specific value of it and choosing the one channel with minimum interference to be assigned to the link. According to the simulating result, the improved topology control algorithm can keep the robustness of the network while achieving to reduce the interference between the cognitive user in the network.3. Proposing a more rational scheme of channel allocating by appending an action prediction of primary user to the original topology control algorithm. Apparently, the spectrum of channel occupation by the primary user is the main reason causing the network partition. As for the cognitive user, if it was assigned a channel which has already been frequently assigned to the primary user, it would lead to the re-allocation of the channel to the cognitive user, which :might cause the unsteadiness of the network communication. In this context, the Markov chain model was used for predicting the action of the primary user in the network communication. Combining the predicting result with the original interference calculation will produce a more reasonable way of channel allocation. And by running the simulation of this strategy, the result shows that using prediction of the primary user's action can improve the total throughput of the cognitive Ad-hoc network and reducing the re-allocation the channel while the network robustness can still be guaranteed.