Research On Self-optimized Spectrum Resource Allocation Algorithm

The shortage of spectrum resource has restricted the development of wire-less communication technology, mainly for:(1) the shortage is caused by huge demand;(2) the waste is caused by unreasonable allocated situation;(3) the contradiction between the efficiency and the complexity of the algorithm is difficult to be reconciled, that lead-s to inefficient utilization. The best way to relieve the urgent problems is to improve the efficiency of spectrum resources, however, most efficient optimization algorithms are centralized, which need the complete channel state information (CSI) with high complex-ity, but distributed optimization algorithms are always low efficiency. This thesis studies self-optimization of spectrum resource allocation for multiple users wireless network sys-tems, and the aim is to develop simpler and more efficient power allocation strategies for uncoordinated individuals interference channels.Spectrum reform is on way to setting the scene of users competing for spectral re-sources without the aid of infrastructures. Self-optimization refers to the fact that no centralized manager or coordination between users is allowed and each user intends to maximize its achievable rate based on its local observation on the channels and its pre-diction on how other users would respond with the hope to benefit the entire network. Self-optimization is an important requirement if a resource allocation scheme for dis-tributed networks are to be feasible and the assumption of complete CSI at a user can never be justified. The innovations of this thesis mainly includes the following aspects:1. A new Forward-Looking Nash game is proposed from Nash and Stackelberg game for the resources competition problem. Users in the new game are ability with cognitive prediction to solve the inefficiency problems of the traditional Nash game and the complex calculation problems of the Stackelberg game. The new game inherits the advantages of the traditional Nash game’s simple structure and the advantages of the Stackelberg game’s high efficiency, and provides a theoretical basis for the study of non-cooperative distributed network resource optimization problems.2. Self-optimization of spectrum resource allocation for orthogonal frequency di-vision multiple access (OFDMA) interference channels (IFC) has been studied. Using the new game theory, we address users how to acquire the necessary information about the competing users by distributed self-optimizing individuals, and a self-optimization algorithm is proposed, which can achieve the maximization system rate just by local in- formation. Simulation results verify the proposed algorithm has better convergence and more efficient than ever before.3. Spectrum resource allocation problem of Multiple-Input Multiple-Output (MI-MO) system has been studied. We extend the new game to cope with MIMO user ter-minals in OFDMA interference channels and develop two kinds of truly non-cooperative distributed optimization algorithms. Results illustrate that the proposed algorithms can manage users competition effectively, and show the advantages of multi-antenna systems that the communication rates increase linearly with the number of antennas.4. Spectrum resources can become abundant by cognitive radio technologies. Based on the Forward-Looking Nash game and an iterative water-filling algorithm with the transmit interference temperature limit, we solve the main confusion for self-optimized cognitive radios and propose two self-optimization algorithms, which can make the sec-ondary users have the cognition capability to avoid those channels used by PUs and com-pete among themselves.5. We use the liner complementarity problem (LCP) formulation designing an iter-ative water-filling algorithm with the transmit interference temperature limit which can reduce or even avoid the interference, and propose a robust analytical optimization zero-forcing (ZF) solution for the multiple-input single-output (MISO) IFC, which requires fixed steps training and only local CSI to get the best ZF result. Furthermore the algorith-m is extremely robust to CSI errors.Based on the Forward-Looking Nash game as the main theoretical method, around the simple and efficient target, Self-optimization algorithm of spectrum resources for OFDMA interference channel model has been designed, and then further applied the algo-rithm to complex multi-antenna system and cognitive radio system, in addition a LCP it-erative water-filling algorithm with the transmit interference temperature limit and an an-alytical optimization ZF algorithm make all those new ideal solutions for self-optimized spectrum resource allocation algorithm.