Sparse Spectrum Detection And Resource Allocation For Cognitive Radio

With the rapid development of 4G and 5G wireless communication networks, a large number of wireless business need to occupy a wide frequency band, however the available spectrum resources are becoming scarcer. On the other hand, ubiquitous wireless services and complex transmission technologies make the energy consumption of wireless networks more and more serious. Therefore, it is great significant to find a high spectrum utilization ratio and energy efficient wireless transmission technology. Cognitive radio is an intelligent wireless communication system, which can improve the spectrum utilization ratio via dynamic spectrum access strategy. Abundant research results have bee obtained on cognitive radio technology in recent years. At the same time, green techinologies, which can reduce energy consumption, carbon dioxide emissions, and utilize resources circularly, have received widely attentions from academia, industry and government. The green wireless communication technologies are mainly devoted to improve energy efficiency, and to solve the contradiction between high QoS requirement and energy saving. Therefore, the combination of green wireless communications technologies and cognitive radio technology will become a focus in the future wireless communications research.In this paper, we mainly investigate cognitive radio and green wireless communications technologies, including the sparse spectrum sensing, energy-efficient resource allocation, and energy-efficient optimal relay selection. The main contents of this dissertation are summarized as follows:(1) This dissertation studies the distributed sparse spectrum sensing problem, and proposes two kinds of distributed sparse spectrum sensing algorithm. Because the signal energies of primary users are sparse in frequency domain, therefore the spectrum sensing problem can be transformed into sparse vector recovery problem. When the channel state information is known, spectrum sensing problem is equals to recover a common sparse vector from multiple measurement vectors of different secondary users; When the channel state information is unknown, spectrum sensing problem is equals to recover multiple sparse vectors with the same sparse structure from multiple measurement vectors of different secondary users; We use different distributed methods to solve the above two problems and then obtain two kinds of distributed sparse spectrum sensing algorithms.Centralized sparse spectrum sensing problem is also studied in this dissertation and two kinds of centralized sparse spectrum sensing algorithms are proposed. Similar to distributed sparse spectrum sensing problem, we transform the spectrum sensing problem into a common sparse vector recovery problem from multiple measurement vectors, and multiple sparse vectors recovery problem with the same sparse structure from multiple measurement vectors. Path-wise coordinate optimization algorithm and multi-task Bayesian compressive sensing algorithms are used to solve the above two problems.(2) This dissertation studies energy-efficient resource allocation problem in cognitive radio system, and proposes an energy-efficient resource allocation algorithm and a robust energy-efficient power allocation algorithm. Considering multiuser orthogonal frequency division multiple access (OFDMA) based cognitive radio system,1) when the channel state information between secondary users and primary users are known, our aim is to maximize the sum of secondary users’ energy efficiency under interference power constraints and power constraints. We propose a two steps algorithm:the first step is the subcarrier allocation algorithm and the second step is energy-efficient power allocation algorithm; 2) when the channel state information between secondary users and primary users are unknown, our aim is to maximize the minimum energy efficiency of secondary user, under interference power constraints, power constraints and the channel estimation error constraints. Since the objective function and the channel estimation error constraints are non convex, it is very difficult to solve it. To make it solvable, the channel estimation error constraints are first transformed into convex constraints, and then the original optimization problem is transformed into an equivalent convex optimization problem by introducing variables. Finally, the robust energy-efficient power allocation algorithm is proposed.(3) This dissertation studies resource allocation problem for the cognitive amplify-and-forward relay system, and presents an energy-efficient relay selection and power allocation algorithm. The aim of the resource allocation problem is to maximize the energy efficiency of cognitive amplify and forward relay system. To solve the above problem, we propose an energy-efficient resource allocation algorithm, which include two algorithms:the optimal energy-efficient power allocation algorithm and the optimal relay selection algorithm.We also study the power allocation problem for cognitive decode-and-forward relay system from energy efficiency perspective considering statistical constraints. To solve the problem, we first transform the statistical constraints into an equivalent expression, and then introduce variables to transform the original optimization problem into a fractional programming problem and then parameter optimization method is used to solve the equivalent problem.(4) The energy-efficient power allocation for cognitive radio with delay constraints is studied in this paper. In order to satisfy the quality of service while improve the energy efficiency, the ratio of effective capacity and energy consumption is used as the optimization objective, considering the total transmission power constraints and interference constraints. The optimization problem is non convex, to facilitate solving, the original optimization problem is first transformed into an equivalent one-dimension optimization problem and a traditional effective capacity maximization problem, and then an efficient algorithm is proposed to solve the above problem.