基于免疫优化的认知无线网络频谱决策与资源分配
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摘要
随着宽带无线业务的高速增长，无线频谱资源日益紧缺。认知无线网络为解决无线频谱资源的供需矛盾提供了一条新的解决途径。在认知无线网络中，认知用户可以在不影响主用户通信的前提下，使用主用户的频谱资源。由于主用户的出现与否是动态变化的，导致可用频谱资源具有时变性。因此，对动态的频谱资源进行有效管理是提高频谱资源利用率的关键技术之一。
无线资源管理围绕频谱的有效利用展开，主要包括：频谱分析、频谱决策、频谱分配、功率控制、频谱移动性、资源分配等。认知无线网络中由于频谱的“二次利用”，使得无线资源管理问题参数众多，经数学建模后多为非凸优化问题。已有的研究表明，传统的数学优化方法难以对此类问题进行有效求解，智能优化算法是求解此类问题的有效算法。人工免疫系统是模仿自然免疫系统功能的一种智能算法，提供了解决工程问题的新理论与新方法。克隆选择算法是人工免疫系统的主要算法之一，已经在数据处理、资源调度等工程领域得到了广泛应用，显示出了较强的优化能力。
本文的研究正是基于此展开，主要采用克隆选择算法求解认知无线网络的无线资源管理问题，为人工免疫算法在工程领域的应用进行有益探索。本文对认知无线网络中的频谱分配、频谱决策、OFDM系统资源分配等无线资源管理问题进行了研究，所取得的主要研究成果为：
1.对认知无线网络中的频谱分配问题进行了研究。频谱分配主要研究如何对可用频谱资源进行分配，从而最大限度地利用频谱资源，提高频谱使用效率。本文首先结合WRAN（无线区域网）,给出了频谱感知过程；通过分析认知无线网络的物理连接，给出了频谱分配的图着色数学模型，并将此模型转换为以网络效益最大化为目标的带约束优化问题，进而提出一种基于免疫克隆选择优化的认知无线网络频谱分配算法,并证明了该算法以概率1收敛。数值仿真实验结果表明，本算法可以得到较高的网络效益。基于WRAN的系统级仿真结果，进一步证明了算法的有效性。
此外，实际应用中，如果不考虑认知用户对频谱使用的需求，有可能造成频谱需求较少的认知用户反而分配到了较多的频谱资源，导致频谱的利用率降低。基于此，本文提出了考虑认知用户需求和分配公平性的频谱分配的新模型,并设计了一种采用混沌量子克隆优化的求解算法，证明了算法以概率1收敛。算法充分利用了混沌搜索的遍历性和量子计算的高效性。仿真实验结果表明，本算法提高了搜索效率，具有更高的网络收益。
2.对认知无线网络中基于认知引擎的频谱决策进行了研究。频谱决策的目标是在分析已得到的各种可用特征参数的基础上，根据当前用户的传输需求，从中优化选择合适的工作频谱。本文通过分析认知无线网络引擎决策，将其建模为一个多目标优化问题，即最小化传输功率、最小化误码率、最大化吞吐量。根据不同认知用户的通信需求，采用加权法转化为单目标问题进行求解，进而提出一种基于量子免疫克隆的优化算法，并证明了该算法以概率1收敛。算法采用量子编码，利用Logistic映射初始化抗体种群,设计了一种基于混沌扰动的量子变异方案。多载波环境下的仿真实验结果表明，在四种不同的权值目标下，算法可以得到较高的目标函数值，并且收敛速度较快，参数调整结果与优化目标偏好一致，并兼顾其它目标函数值，适合实时性要求较高的认知引擎决策。
此外，由于认知无线网络的引擎决策是多目标优化问题，如果采用加权求解，实际上是将多目标问题转换为单目标问题进行求解。考虑到难以确定合适的权值，并且加权法处理多目标优化问题时，每次只能得到一种权值下的最优解并且容易漏掉一些最优解，进而提出一种基于免疫多目标优化的认知引擎参数选择和决策方法，求出算法的Pateto最优解集，提高优化效果。在多载波环境下，模拟不同的无线信道条件，对算法进行了仿真实验。结果表明，本算法可以得到分布范围更广且均匀的Pateto解集，有利于得到符合认知用户决策需求的最优解。算法可以根据信道条件和用户需求的变化，自适应的调整子载波的发射功率和调制方式，给出理想的参数配置，实现认知引擎决策优化。
3.对认知无线网络中基于OFDM的资源分配进行了研究。认知OFDM资源分配是提高频谱资源利用率的关键技术之一。基于免疫优化算法，设计了适用于固定业务的余量自适应（MA）准则下的子载波分配算法，仿真实验表明，算法减少了系统所需的发射功率。此外，设计了适用于可变数据业务的速率自适应(RA)准则下的功率分配算法，仿真实验表明，算法可以获得更大的系统吞吐量。
此外，考虑到认知用户对资源需求的公平性，预先设定所需的服务级别，设计了RA准则下的两阶段比例公平资源分配算法。首先将子载波分配给用户，然后基于免疫优化算法，分配功率给不同的子载波，确保资源分配的公平性。此外，算法充分考虑了主用户可容忍的干扰约束。仿真实验结果表明，在总发射功率、误码率及主用户可接受的干扰约束下，算法可以获得与最优资源分配方法接近的系统吞吐量，同时兼顾了次用户对数据分配的公平性需求，在最大化系统吞吐量和次用户需求的公平性之间取得较好均衡。
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.
无线资源管理围绕频谱的有效利用展开，主要包括：频谱分析、频谱决策、频谱分配、功率控制、频谱移动性、资源分配等。认知无线网络中由于频谱的“二次利用”，使得无线资源管理问题参数众多，经数学建模后多为非凸优化问题。已有的研究表明，传统的数学优化方法难以对此类问题进行有效求解，智能优化算法是求解此类问题的有效算法。人工免疫系统是模仿自然免疫系统功能的一种智能算法，提供了解决工程问题的新理论与新方法。克隆选择算法是人工免疫系统的主要算法之一，已经在数据处理、资源调度等工程领域得到了广泛应用，显示出了较强的优化能力。
本文的研究正是基于此展开，主要采用克隆选择算法求解认知无线网络的无线资源管理问题，为人工免疫算法在工程领域的应用进行有益探索。本文对认知无线网络中的频谱分配、频谱决策、OFDM系统资源分配等无线资源管理问题进行了研究，所取得的主要研究成果为：
1.对认知无线网络中的频谱分配问题进行了研究。频谱分配主要研究如何对可用频谱资源进行分配，从而最大限度地利用频谱资源，提高频谱使用效率。本文首先结合WRAN（无线区域网）,给出了频谱感知过程；通过分析认知无线网络的物理连接，给出了频谱分配的图着色数学模型，并将此模型转换为以网络效益最大化为目标的带约束优化问题，进而提出一种基于免疫克隆选择优化的认知无线网络频谱分配算法,并证明了该算法以概率1收敛。数值仿真实验结果表明，本算法可以得到较高的网络效益。基于WRAN的系统级仿真结果，进一步证明了算法的有效性。
此外，实际应用中，如果不考虑认知用户对频谱使用的需求，有可能造成频谱需求较少的认知用户反而分配到了较多的频谱资源，导致频谱的利用率降低。基于此，本文提出了考虑认知用户需求和分配公平性的频谱分配的新模型,并设计了一种采用混沌量子克隆优化的求解算法，证明了算法以概率1收敛。算法充分利用了混沌搜索的遍历性和量子计算的高效性。仿真实验结果表明，本算法提高了搜索效率，具有更高的网络收益。
2.对认知无线网络中基于认知引擎的频谱决策进行了研究。频谱决策的目标是在分析已得到的各种可用特征参数的基础上，根据当前用户的传输需求，从中优化选择合适的工作频谱。本文通过分析认知无线网络引擎决策，将其建模为一个多目标优化问题，即最小化传输功率、最小化误码率、最大化吞吐量。根据不同认知用户的通信需求，采用加权法转化为单目标问题进行求解，进而提出一种基于量子免疫克隆的优化算法，并证明了该算法以概率1收敛。算法采用量子编码，利用Logistic映射初始化抗体种群,设计了一种基于混沌扰动的量子变异方案。多载波环境下的仿真实验结果表明，在四种不同的权值目标下，算法可以得到较高的目标函数值，并且收敛速度较快，参数调整结果与优化目标偏好一致，并兼顾其它目标函数值，适合实时性要求较高的认知引擎决策。
此外，由于认知无线网络的引擎决策是多目标优化问题，如果采用加权求解，实际上是将多目标问题转换为单目标问题进行求解。考虑到难以确定合适的权值，并且加权法处理多目标优化问题时，每次只能得到一种权值下的最优解并且容易漏掉一些最优解，进而提出一种基于免疫多目标优化的认知引擎参数选择和决策方法，求出算法的Pateto最优解集，提高优化效果。在多载波环境下，模拟不同的无线信道条件，对算法进行了仿真实验。结果表明，本算法可以得到分布范围更广且均匀的Pateto解集，有利于得到符合认知用户决策需求的最优解。算法可以根据信道条件和用户需求的变化，自适应的调整子载波的发射功率和调制方式，给出理想的参数配置，实现认知引擎决策优化。
3.对认知无线网络中基于OFDM的资源分配进行了研究。认知OFDM资源分配是提高频谱资源利用率的关键技术之一。基于免疫优化算法，设计了适用于固定业务的余量自适应（MA）准则下的子载波分配算法，仿真实验表明，算法减少了系统所需的发射功率。此外，设计了适用于可变数据业务的速率自适应(RA)准则下的功率分配算法，仿真实验表明，算法可以获得更大的系统吞吐量。
此外，考虑到认知用户对资源需求的公平性，预先设定所需的服务级别，设计了RA准则下的两阶段比例公平资源分配算法。首先将子载波分配给用户，然后基于免疫优化算法，分配功率给不同的子载波，确保资源分配的公平性。此外，算法充分考虑了主用户可容忍的干扰约束。仿真实验结果表明，在总发射功率、误码率及主用户可接受的干扰约束下，算法可以获得与最优资源分配方法接近的系统吞吐量，同时兼顾了次用户对数据分配的公平性需求，在最大化系统吞吐量和次用户需求的公平性之间取得较好均衡。
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.
引文
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