Research On Energy Efficient Resource Management For Cognitive Radio Networks Enhanced By Distributed Antennas

With the recent increasing number of mobile communication devices and developments of new wireless applications, more and more spectrum resource is required in the next generation wireless networks, which will make the spectrum resource becomes a bottleneck holding back the development of wireless communications. Cognitive radio technology, which allows secondary users to sense the wireless environment and reuse the authorized spectrum resource dynamically without influencing primary networks, has been considerred as a promising way to meet spectrum shortage. Besides, distributed antenna technology has been introduced into cognitive radio due to its benefit of capacity, coverage and lower transmission power. On the other hand, in order to reduce the amount of greenhouse gas emission caused by wireless communications, the concept of "green communication" has gained general concern in recent years. Therefore, energy efficient wireless resource management schemes need to be investigated to improve the network energy efficiency.In this thesis, the problem of energy efficient resource management in cognitive radio networks enhanced by distributed antennas is studied. The novelty spectrum sensing and resource allocation schemes are proposed. The main contributions of this thesis are listed as follows:1. Compressed sensing with structurally random matrix in cognitive WLAN over fiberWe introduce compressed sensing theory into cognitve wireless local access network over fiber (CWLANoF) in order to improve the collecting process of sensing data and save reduce energy consumption. We choose a novel sampling matrix, structurally random matrix (SRM), in order to implement fast and efficient compressed sensing. To fuse sensing data from different remote access unit, the variance of each recover sensing sequence is estimated using the wavelet transform, and the optimum weighting factor to each sensing sequence is obtained accordingly. At last, the simulation results show that compressed sensing can improve efficiency of the data processing and satisfies the architecture of CWLANoF’s requirements well.2. A QoS-aware energy efficient cooperative spectrum sensing scheme in cognitive network enhanced by distributed antennas.We introduce compressed sensing theory into cognitive network enhanced by distributed antennas and propose a QoS-aware green cooperative compressed sensing scheme. We established the energy efficiency maximization problem under cognitive users’basic QoS constraints of packet drop probability, sensing delay and total throughput. Besides, the expression for accuracy of compressed signal recovery under certain sparsity is fitted into exponential function, and the number of compressed samples number is taken account into the energy efficiency optimization for the first time. To solve the formulated nonlinear multivariate optimal problem, a sub-optimal algorithm based on particle swarm optimization is presented. At last, the performances of the proposed scheme are simulated and discussed.3. Adaptive Energy-Efficient Resource Allocation for WLAN Enhanced by Cognitive Radio。The energy efficient aspect of resource management for WLANs enhanced by cognitive radios is studied. We jointly design power control, channel allocation and access points (APs) selection in order to improve the energy efficiency. Taking different parts of energy consumption during a frame into consideration, radio resource management for cognitive WLAN is formulated as three optimization problems for different network load situations. To overcome the difficulty of solving mixed integer nonlinear programming problem, we transform them into equivalent continuous integer nonlinear problems and choose artificial fish-swarm algorithm (AFSA) to get sub-optimal solutions. Then, an adaptive energy efficient resource allocation mechanism is presented based on these proposed optimal problems to adapt network load conditions. Simulation results are presented to demonstrate that energy efficiency can be improved significantly in the proposed scheme.