Research On The Key Technologies For Spectrum Resource Management In Cognitive Radio Networks

With the emergence of new wireless technologies and applications, the currently deployed spectrum is becoming increasingly more crowded. The scarcity of frequency spectrum has severly postponed the development of the future wireless communications. Hence, how to accommodate more wireless services within the limited spectrum becomes a challenging problem. According to the report published by the Federal Communication Commission (FCC), most of the authorized spectrum allocated to primary users today is actually underutilized as low as 15%, which leads to a large number of spectrum holes.Cognitive radio (CR) is considered as a novel intelligent wireless communication technology to efficiently deal with the spectum underutilization problem due to the current fixed spectrum allocation policy, by fully utilizing the vacant spectrum in time and space domain. Cognitive radio network (CRN) is the networking of CR technology, in which secondary users (SUs) with cognitive abilities can exploit the existence of spectrum holes by intelligently detecting its surrounding radio spectrum environment, and adaptively change its communication parameters to access the primary spectrum in a legally and efficiently way. This thesis would concentrate on several key technologies in cognitive radio spectrum resource management, such as multi-dimensional spectrum allocation, transmit power control, spectrum mobility management and so on, combining existing research results with wireless transmission environments, proposing effective and fair approaches from several important respects. The main contributions of the thesis are listed as follows:1?Proposing a fair and efficient spectrum resource allocation scheme for the maximization problem of spectrum utilization in multiuser multi-dimensional OFDM-based cognitive radio systems, constrained by available subcarriers, interference temperature, secondary power budget, fairness, and etc. Our scheme with much reduced complexity is realized in two steps. A proportionally fair algorithm for subcarrier allocation is firstly applied. Next, iterative water-filling with improved efficiency is applied using our proposed algorithm for fast power allocation to maximize the sum capacity. Extensive simulation results show that the proposed scheme offers a proportionally fair subcarrier allocation among SUs compared to the existing solution, while significantly enhance the user fairness with a little reduction in system capacity.2?Formulating the power optimization problem as Nash bargaining game for cognitive underlay networks to allow unlicensed SUs to share the available spectrum of licensed primary users (PUs) fairly and efficiently, subject to the interference power constraint at each PU, power budget of all SUs, minimum transmit date rate requirement of each SU, etc. To find a simple way to solve the non-linearly constrained optimization problem, we propose to use Sequential Quadratic Programming (SQP) approach to obtain the unique and Pareto optimal Nash bargaining solution (NBS) for all SUs. Through extensive simulation results, we demonstrate that the proposed solution is very close to the optimal one, and user fairness is worse than that of the max-min scheme but much better than that of non-cooperative scheme.3?Subject to meeting PUs' blocking probability objective, we propose a channel reservation scheme to enhance QoS for SUs. The proposed scheme employs a centralized control manager (CCM) to coordinate the dynamic spectrum access among PUs and SUs by dividing the spectrum into two parts:the normal access channels (NACs) that may be taken back anytime by PUs, and the reserved secondary channels (RSCs) that are locked by occupied SUs until their call sessions are complete. A Markov chain model is developed to analyze the proposed spectrum access scheme and compare its performance to that of a CRN without reserved channels. Furthermore, the number of reserved channels is optimized based on SUs' utility functions. Numerical results show that by optimally selecting the number of reserved channels to adapt to various traffic loads, the proposed scheme can significantly improve SUs'QoS while meeting PUs' QoS objectives.