| With the increasing of mobile devices and expanding of wireless communication networks, traditional static allocation of wireless spectrum resources cannot fully meet the communication requirements. Whereas in some private communication networks, some spectrum has not been fully utilized. From the year 2000, cognitive radio as an emerging technology has been proposed, which can improve spectrum utilization effi-ciency and at the same time alleviate spectrum starvation problem due to the growing number of wireless communication devices. With cognitive radio, unlicensed spectrum users can reuse the spectrum initially used by licensed users such that the interferences to licensed users are within a threshold. Meanwhile, unoccupied licensed spectrum can be allocated to unlicensed users. Existing research mainly focus on spectrum sensing, spectrum allocation, channel coding and so on to enhance spectrum utilization effi-ciency and system throughput. In this research, with relay based communication, energy harvesting, spectrum auction technologies and so on, here I concentrate on the study of spectrum allocation in different communication modes of cognitive radio network. The basic system performance is analyzed, the system throughput is optimized.In this dissertation, I firstly introduce background knowledge of cognitive radio networks, including the allocation status of radio channel resources, the background why cognitive radio is proposed, the definition of cognitive radio technology, its main features, architecture and application scenarios. Next, in Chapter 2, related works are summarized and new research directions are proposed including secondary user network throughput optimization in underlay cognitive radio networks, the queueing system per-formance analysis in underlay cognitive radio networks with heterogeneous priorities, spectrum auction mechanism designing in ubiquitous network with heterogeneous spec-trum providers and primary secrecy rate optimization with energy harvesting secondary user’s QoS guarantee. The contribution of this research can be summarized as follows.1. How to efficiently allocate spectrum resources is the main challenge to enhance the system performance of cognitive radio networks. In the underlay scheme, both chan-nel allocation and power distribution will affect the network performance, for example, throughput. Existing literatures mainly focus on the study of outage performance or re-lay selection method to maximize the throughput of secondary user network. However, these network models only consider one primary transmission pair and one secondary transmission pair, which however, neglected the situation when there are multiple trans-mission pairs in both primary user and secondary user networks. In reality, when there are multiple transmission pairs, how to effectively allocate the spectrum bands used by primary users to secondary user pairs is a challenge. Besides, there is heavy channel fading when the transmission distance is too long. Therefore, in Chapter 3,I employ a two-hop relay node in the secondary user network to enhance the system performance and the joint channel power allocation problem is defined. The aim is to maximize the total throughput of all the secondary user network (max-total problem) and max-imize the minimum throughput among secondary user pairs (max-min problem). For the max-total problem, I define it as a bipartite graph based matching problem and de-sign a bipartite graph based maximum weighted matching algorithm (STMA) to solve this problem. For the max-min problem, based on the optimal relay assignment (ORA) algorithm, I designed a polynomial time iterative algorithm, the optimal channel as-signment algorithm (OCAA) to iteratively assign channels first used by primary users to secondary users. Simulation results show, Simulation results show, the throughput of SU network grows with the maximum transmission power of SUs below the interfer-ence power of PUs. STMA algorithm achieves an average of 46.67% performance gain when path loss component α= 2 and 13.08% enhancement when α= 3 compared with random algorithm. The OCAA algorithm effectively ensures the max-min fairness of the capacity among SU pairs in finite iterations and achieves at least 97% performance gain than the random method under most cases.2. In secondary user network, the packet’s transmission strategy may also affect the transmission performance of secondary user network, such as transmission delay and so on. In underlay cognitive radio network, there are two different types of users and in the secondary user network, packets are of different priorities. Existing research mainly focus on the study of transmission of homogeneous packets in the secondary user network. Therefore, in Chapter 4, on the bases that high priority packets in the secondary user network have preemptive priority over the low ones and are impatient, I examine the queueing system performance of the secondary user network. From the global system’s point of view, I model the behavior of secondary users as a M/M/1+GI queueing system, which is depicted with a two dimensional state transition graph. We analyze the reneging probability of high priority packets and average waiting time of two kinds packets through experiments. Numerical analysis proved the correctness of theoretical results, which can also be used to choose system parameters as well as to design efficient MAC protocols.3. As one of the key technologies to guarantee the efficient spectrum reuse, spec-trum auction can motivate the spectrum operator (PO) and secondary users to participate in spectrum sharing. Previous spectrum auction works mostly assume each PO can only have one type spectrum or each SU can only buy homogeneous spectrum bands from the same PO. However, in a ubiquitous network scenario, each PO possesses heteroge- neous spectrum resources such as WiFi,3G and each SU may request different types of spectrum bands from the same PO. Existing auction schemes cannot be used to ef-fectively solve the problem. Therefore, in Chapter 5, I come out with a lightweight combinatorial double auction to tackle this challenge. The greedy algorithm G-Greedy and group-buying discounts based algorithm E-Greedy are proposed to solve the prob-lem. I theoretically prove the economy properties of the proposed schemes. Simulation results show that both of the two algorithms can yield higher utilities.4. To ensure efficient transmission of cognitive radio networks, safety commu-nication should not be neglected. Since secondary users are usually mobile and is en-ergy constrained, nodes with energy harvesting capability can ensure the consistency of communication. In Chapter 6, when overlay mode is adopted, an energy harvesting secondary transmitter in the secondary user network may act as the relay node for the transmission of primary transmitter in the primary user network. In this chapter, how to guarantee the QoS of secondary user pairs and increase the secrecy capacity of pri-mary transmission pairs is studied. I define the problem as a mixed integer non-linear program. Due to the special features, I design a polynomial time algorithm SRMA to optimally solve this problem. Numerical results demonstrate that the primary secrecy rate grows with the increasing energy save ratio and optimal energy save ratio is in-versely proportional to the energy harvesting rate.
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