Research On Resource Management For Cognitive Radio Networks

With the recent developments of wireless access technologies and diverse requirements of new wireless applications, more and more spectrum resource is required in the next generation wireless networks, which will make the spectrum resource become shortage. On the other hand, current static spectrum allocation strategy causes the low efficiency of spectrum utilization, which results in the waste of spectrum resource. Therefore, to solve this contradiction, one of the most important issues is how to improve the efficiency of spectrum utilization. Cognitive radio allows the secondary users (SUs) sense the surround environment, dynamically use the idle spectrum resource licensed to primary users (PUs) and adaptively change its operating parameters (e.g., transmit-power, carrier-frequency, and modulation strategy), which can improve the efficiency of the spectrum utilization while avoiding the interference to PUs. Cognitive radio has been attracted a lot of attention. In cognitve radio networks, to track the changing radio environment and improve the efficiency of spectrum utilization, one of the most important chanllegence is how to realize the resource management, such as power control, spectrum sharing and user scheduling and so on. The resource management in cognitive MIMO networks or cognitive radio networks with femtocell is also important.In this thesis, the problem of resource management in cognitive radio networks is studied, and the novelty resource allocation schemes are proposed. We also verify the performance of the proposed schemes by computer simulation methods. The main contributions of this thesis are listed as follows:1. Dynamic channel and power allocation for heterogeneous services in cognitive radio networks with imperfect channel sensing.Firstly, we analyze the most of existing resource allocation algorithms are based on the assumption of perfect channel sensing or one type of service requirement, which cannot be used in the case of heterogeneous services with imperfect channel sensing in cognitive radio networks.Secondly, we generalize the property of the service requirement for the primary users and secondary users respectively. Then, the chanllegences of the resource allocation for heterogeneous services in cognitive radio networks are discussed. Moreover, we classify heterogeneous services by QoS requirements, e.g., SUs with minimum rate guarantee and SUs with best effort service. Furthermore, we introduce the minimum rate constraint condition for SUs with minimum rate guarantee. For SUs with best-effort service, each SU may obtain different resource due to the discrepancy of channel quality. In order to solve the possible unfairness problem, we introduce a proportional fairness constraint. Based on these constraints and imperfect channel sensing information, we formulate the problem of channel and power allocation as a mixed integer programming problem.Thirdly, we assume that the channels licensed to primary users can be sensed by cognitive radio network with imperfect channel sensing due to hardware limitation, short sensing time and network connectivity issues, where only the estimate of channel information can be obtained.Fourthly, the joint power and channel allocation algorithm using discrete stochastic optimization method has been proposed. The discrete stochastic optimization method has low computation complexity and fast convergence to approximate to the optimal solution under imperfect channel information. Another advantage of this method is that it can track the changing radio environment to allocate the resources dynamically.Finally, simulation results are presented to show that the proposed scheme can improve the performance of system unerder the condition of imperfect channel sensing.2. Joint power allocation and beamforming with user selection for cognitive radio networks via discrete stochastic optimization.Firstly, the chanllegences of the resource allocation for spectrum sharing underlay cognitive radio networks is analyzed and the state-of-art is investigated.Secondly, we consider that the secondary base station (SBS) configures multiple antennas for spectrum sharing underlay cognitive radio networks, which can form multiple "beams" towards individual secondary users. We assume that there are a large number of secondary users requesting to admit to the network. In this case, to avoid interference to primary users and null the mutual interference among secondary users while maximizing the sum rate, we consider joint power allocation and beamforming with secondary user selection in cognitive radio networks.Thirdly, we emphasize on the condition of imperfect channel sensing, in which only the noisy estimate of channel information can be obtained. This is because it’s hard to know the perfect channel information due to the hardware limitation, short sensing time, and network connectivity issues in practical cognitive radio networksFourthly, a joint power allocation and beamforming with secondary user selection algorithm is proposed by using a discrete stochastic optimization method. The proposed algorithm can adaptively track the optimal solution and has fast convergence rate and low computation complexity.Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme under the condition of imperfect channel sensing.3. Energy-efficient spectrum sharing and power allocation for heterogeneous cognitive radio networks with femtocells.Firstly, we discuss the importance of the energy efficient transmission in wireless networks and investigate the state-of-art.Secondly, since both cognitive radio and femtocell are promising technologies to enable energy efficiency in wireless networks, the interplay between them merits further research. Hence, we focus on the energy efficiency aspect of spectrum sharing and power allocation in heterogeneous cognitive radio networks with femtocells.Thirdly, we formulate the energy-efficient spectrum sharing and power allocation problem in heterogeneous cognitive radio networks with femtocells as a Stackelberg game. Then the backward induction method is used to solve the Stackelberg game solution, and a gradient based iteration algorithm is proposed to obtain the Stackelberg equilibrium solution to the energy-efficient resource allocation problem.Finally, simulation results are shown that the proposed scheme can significantly improve the system performance.