Investigation Of Sensing Strategy And Spectrum Sharing Technology In Cognitive Radio Networks

Cognitive radio is a revolutionary future wireless communication technology which aims to greatly release the conflict between frequency scarcity and low utilization of precious radio resources and is becoming one of research focuses. There are two kinds of priority users in cognitive network, such as authorized users (or primary users) and cognitive users (or secondary users), which is mainly different from tranditional wireless system. Cognitive users can utilize idle spectrum resources (or spectrum holes) to improve the utilization of wireless spectrum without interference to authorized users. There are two key issues in order to implement reliable and efficient cognitive radio network:(1) to devise an intelligent spectrum sensing policy to reliably detect idle spectrum holes as many as possible;(2) to design an efficient spectrum sharing technology to resolve collisions of cognitive users. Therefore, this dissertation proposes cross-layer sensing strategy and stable spectrum sharing solutions to above fundamental issues based on learning algorithm and Gale-Shapley(G-S) theory which have implemented intelligent and efficient spectrum sensing and sharing.The main contributions and innovations of this dissertation are summarized as follows:(1) Fast and accurate sensing strategy is premise to reduce the overhead of spectrum detection. Only ideal channel conditions are considered and the convergence rate is slow with much overhead in current researches in the symmetrical cognitive radio network. Therefore we consider real wireless channel condition and propose a distributed cross-layer decision strategy based on learning automata, in which cognitive users can quickly probe and optimally exploit "spectrum holes" to improve detection efficiency and network capacity. Firstly,"less congested" and "even" channels are chosen according with idle probabilities and channel quality. Then cross-layer scheme integrating channel state information in PHY layer and collision probability in MAC layer is proposed to access less congested channels in order to avoid multi-user collisions in distributed cognitive network. Finally, cognitive users learn channel parameters with statistical learning automata to quickly search best idle channels without prior channel occupancy model and information. It is shown that this cross-layer scheme can quickly converge which improves detection efficiency and cognitive network capacity based on stochastic learning automata.(2) Intelligent and robust spectrum sharing is key to spectrum resource utilization. Much communication overhead and wide hardware bandwidth are required in existing schemes in an asymmetrical opportunistic spectrum access (OSA) system, which can not be applied in a distributed cognitive network. A new cognitive medium access scheme is proposed based on Gale-Shapley(G-S) theory and learning algorithm which realizes stable spectrum sharing with partial sensing capability. In an asymmetrical OSA system, channel rewards are correlated with cognitive user pairs and each secondary user possibly has different channel rewards even in the same channels due to geographic dispersion. Firstly, a "one-to-one" pairs matching scheme based on G-S theory are applied to allocate channels to cognitive users. Secondly, reinforcement learning algorithm are utilized to achieve the stable matching without prior channel information under partial-channel sensing capability. Finally timers are set to implement distributed opportunistic communication in which timers are inversely proportional to channel rewards. Simulation results show that the G-S strategy greatly eliminated multi-user collisions by a one-to-one matching policy and efficiently improve cognitive network throughput. It is very suitable to resolve cognitive medium access problem in an asymmetrical OSA system.(3) In LTE-Advanced system, interference is a important limitation of femtocell networks deployment with co-channel deployments. In order to overcome shortcomings of heavy overhead and high complexity in existing interference management schemes, a joint opportunistic interference avoidance scheme with Gale-Shapley spectrum sharing (GSOIA) based on the interweave paradigm is proposed to mitigate both tier interferences in macro/femto heterogeneous networks. It is easy to be implemented and less communication overhead. Firstly, cognitive femtocells listen to scheduled information of macro base station and actively sense wireless environment to reliably detect more spectrum holes in macrocell networks. Secondly, cognitive femtocells exploit orthogonal spectrum resources (idle time-frequency RBs of macrocell) obtained from sensing to avoid inter-tier interference based on opportunistic interference avoidance approach. In additional, a one-to-one policy between idle spectrum resources and femtocells is utilized to mitigate intra-tier interferences. The simulations show that more idle spectrum resources can be probed in the GSOIA scheme that improves efficiency and reliability of detection. Furthermore, CR-enabled femtocells can actively sense their environment and exploit the network side information to adaptively mitigate interference improving network capacity. The distributed intelligent scheme not only reduces communication overhead but also improves considerable performance, which validates potential benefits of ad-hoc femtocells for in-home coverage.