Immune Optimization Based Spectrum Decision-Making And Resource Allocation In Cognitive Wireless Network

With the rapid growth of broadband wireless services, wireless spectrum resourcesare increasingly scarce. Cognitive radio network provides a new way to solve the thecontradiction between supply and demand of the wireless spectrum. In cognitive radionetwork, under the premise that cognitive users can not affect the communications ofprimary users，cognitive users can use the spectrum resources of primary user. Thepresence of primary user is dynamic, so the available spectrum resources aretime-varying. Therefore, effective management for dynamic spectrum resource is a keytechnology to improve the spectrum resources utilization and to provide reliable servicefor cognitive wireless network.Radio resource management aims to use spectrum resource effectively. It mainlyincludes spectral analysis, spectrum decision-making, spectrum allocation, accesscontrol, power control, spectrum mobility, resource scheduling etc. Due to thesecondary use of the spectrum resource, the radio resource management has manyparameters, which result that it is a non-convex optimization problem aftermathematical modeling. Previous studies have shown that the traditional mathematicaloptimization method is difficult to effectively solve such problems. Intelligentoptimization algorithm is suitable for solving such problem. Artificial immunealgorithm, as a kind of intelligent optimization method, is inspired by some mechanismsof the nature immune system.It provides new theories and methods to solve engineeringproblems. Clonal selection algorithm is one of the artificial immune algorithms, whichhas been widely used in the fields such as data processing, resource scheduling and soon.It shows strong optimization ability.The study of this paper is based on the described above, which is mainly for radioresource management issues of cognitive radio network using clonal selection algorithm.It is useful exploration for artificial immune algorithm in the engineering applicationfields. In this paper, spectrum allocation, spectrum decision-making and resourceallocation have been studied.The following research results was obtained:1. The spectrum allocation of cognitive wireless network has been studied.Spectrum allocation mainly focuses on how to allocate the available spectrum resourcesin order to maximize the use of the spectrum resources and improve the efficiency ofspectrum utilization. In this paper, the spectrum sensing process was described based onWRAN(Wireless Region Area Network).By analyzing physical connection of cognitive wireless network, the graph coloring based mathematical model of spectrum allocationwas given, and then it was converted into a constrained optimization problem, whosegoal was to maximize the network profit. An immune clonal selection algorithm wasproposed to solve the problem, and the algorithm convergent with probability1wasproved. The experimental simulation results show that this algorithm can achievemaximum network profits. Meanwhile, the system simulation results based on WRANconfirmes its effectiveness.In addition, if the spectrum demands of cognitive users were not considered inpractical applications, it may cause that cognitive user who demand fewer spectrum butbe assigned to more spectrum, leading to lower spectrum utilization. Taking intoaccount the spectrum demands of secondary users and the fairness allocation of thespectrum, the new mathematical model of spectrum allocation is given. A chaosquantum clonal optimization algorithm is proposed to solve the problem, and then theconvergence of the algorithm with probability1is proved. The algorithm fully takesadvantages of the ergodicity of chaos search and efficiency of quantum computing. Thesimulation experimental results show that the algorithm improves the search efficiencyand can achieve higher network profits.2. The cognitive engine based spetrum decision-making of cognitive wirelessnetwork was studied. The goal of the spectrum decision-making is to select theappropriate spectrum according to current user’s transmission demands, which is basedon the analysis results of the available spectrum characteristic parameters. By analyzingengine decision of cognitive wireless network, the mathematical model of enginedecision is given, and then it is converted into a multi-objective optimization problemaiming to minimize the transmission power and the error rate, and to maximize thethroughput. According to the communication demands of different cognitive users, it isconverted into a single objective problem by weighed method. A Chaos quantum clonalalgorithm is proposed to solve the problem, and the algorithm convergent withprobability1is proved. The quantum coding and logistic mapping are used to initializethe population and a quantum mutation scheme is designed with chaotic disturbances.The simulation experiments are done to test the algorithm under a multi-carriersystem.The results show that, with four different weights settings, this algorithm hasgood convergence and objective function value. Parameter adjustments are consistentwith the preferences of optimization objective and other objective function values arealso taken into account. It meets the real-time requirement for cognitive engine. In addition, the cognitive engine decision-making is a multi-objective optimizationproblem. In fact, it was converted into a single objective problem if a weighted methodwas used to slove it. It is difficult to determine the appropriate weights and the weightedmethod can only get one optimal solution under a certain weights, and also it may misssome optimal solution. Thus, a multi-objective immune algorithm was proposed toobtain the Pateto optimal set for parameters selection and decision. The simulationexperiments were done under multi-carrier system wtih different channel conditions.The results show that the algorithm can get more wider and even Pateto optimal set. Itcan adjust transmission power and modulation mode according to the changes ofchannel conditions and user demands.It can obtain ideal parameter configuration andoptimize cognitive engine decision-making.3. The OFDM based resource allocation of cognitive wireless network was studied.Cognitive OFDM resource allocation is one of the key technologies to improve theutilization of spectrum resources. Based on immune optimization, subcarrier allocationalgorithm was designed under margin adaptive (MA) criterion, which is suitable forfixed business. Simulation experiments show that it reduces the required transmissionpower. In addition, based on immune optimization, power allocation algorithm wasdesigned under rate adaptive (RA) criterion, which is suitable for variable data services.Simulation results show that the algorithm can achieve greater system throughput.In addition, taking into account the fairness demand for resources of cognitiveusers, a two-stage proportional fair resource allocation algorithm under RA criteria wasdesigned, in which the desired service levels were predefined. Firstly, the subcarriers areallocated to secondary users. Second, the immune-based algorithm is presented forpower allocation to ensure the fairness. Moreover, the proposed algorithm fully takesinto account the interference that primary user can tolerate. Simulation results show that,subject to the constraints of total power, bit error rate and the acceptable interferences ofprimary user, the proposed algorithm achieves near-optimal throughput and moresatisfying proportional fairness rate among secondary users. It can achieve betterbalance between maximizing system throughput and the fairness demands of thecognitive users.