共查询到16条相似文献,搜索用时 218 毫秒
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对认知无线网络中的频谱进行有效分配是实现动态频谱接入的关键技术.考虑3次用户对频谱的需求和分配的公平性,给出了频谱分配的数学模型,并将其转换为以最大化网络收益为目标的带约束优化问题,进而提出一种采用混沌量子克隆优化求解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.最后,通过仿真实验比较了本文算法与颜色敏感图着色算法、基于遗传算法的频谱分配、基于量子遗传算法的频谱分配的性能.结果表明:本文算法性能较优, 能更好地实现网络收益最大化.
关键词:
混沌量子克隆算法
认知无线网络
频谱分配 相似文献
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为了优化认知无线电网络中多用户正交频分复用子载波的资源分配, 将其转换为一个约束优化问题, 进而提出了一种基于混沌免疫优化的求解方法.给出了算法的实现过程和关键技术, 设计了适合算法求解的编码、 克隆、 重组、 变异算子.实验结果表明, 在满足认知用户速率、 所需误码率及干扰约束的条件下, 本文所用算法减小了整个系统所需的总发射功率, 同时收敛速度较快, 能够得到较优的子载波分配方案, 进而提高频谱利用效率. 相似文献
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为了解决认知无线电系统中最大和网络效益和用户间公平性联合最优化的多目标频谱分配难题,基于量子蜂群理论和膜计算,提出了一种新的离散多目标组合优化算法—–膜量子蜂群优化.所提算法在基础膜可以搜索到单个目标的全局最优解,在表层膜获得兼顾网络效益和公平的Pareto前端解.通过膜间的通信规则、量子觅食行为的协同演进和非支配解排序可获得能同时求解单目标和多目标优化问题的多目标优化算法,并与经典的敏感图论着色算法、遗传算法、量子遗传算法和粒子群算法等频谱分配算法在不同的目标函数下进行仿真性能比较.仿真结果表明:在不同网络效益函数下所提的膜量子蜂群频谱分配算法都能够较好地找到单目标最优解,优于经典的频谱分配算法和已有的智能频谱分配算法,还可获得多目标频谱分配的Pareto前端最优解集. 相似文献
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提出了一种用于认知无线电线性加权协作频谱感知的改进混合蛙跳算法(shuffled frog leaping algorithm, SFLA) 的群体初始化技术, 提出在SFLA初始群体中包含基于修正偏差因子所得的解, 从而改进算法初期性能. 仿真结果表明相比于传统群体初始化技术, 本文所提出的群体初始化技术能够以更快的速率得到期望解, 从而节约计算时间, 更有利于实时应用
关键词:
认知无线电
频谱感知
混合蛙跳算法
群体初始化 相似文献
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针对认知无线电网络中认知用户广义传输时间的优化问题, 提出了一种基于抢占式续传优先权M/G/m排队理论的频谱切换模型. 在该排队模型中, 为了最小化认知用户广义传输时间, 采用混合排队-并列式服务的排队方式. 在此基础上, 深入分析多个认知用户、多个授权信道、多次频谱切换条件下认知用户信道使用情况, 从而推导出广义传输时间表达式. 最后探讨了该模型下自适应频谱切换策略. 仿真结果表明, 相比于已有的频谱切换模型, 该模型不仅能够更加完整地描述认知用户频谱切换行为, 而且使得认知用户传输时延更小, 广义传输时间更短. 此外, 认知无线电网络允许的认知用户服务强度增加, 能够容纳的认知用户数量增多. 因此, 该模型提升了认知用户频谱切换的性能, 更好地实现了认知用户与授权用户的频谱共享. 相似文献
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针对认知无线电网络(CRN)中空闲频谱感知困难的问题,本文提出了基于前向纠错和差分进化算法的多节点频谱感知算法。首先,利用基于差分进化算法的协同检测完成信号感知;然后,研究了信道噪声对频谱感知性能的影响;最后,分析了前向纠错技术在信道存在噪声时对频谱感知性能的影响。仿真实验将纠错和无纠错控制信道的不同信噪比作为依据,采用三种不同的检测方法评估了本文算法。仿真实验结果表明,在存在噪声的认知无线电网络中,本文算法提高了系统的性能和检测概率,且协同感知算法的性能随着节点数目的增加而提高,该算法适合应用于实时性要求较高的应用程序。 相似文献
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提出了一种改进的混合蛙跳算法(shuffled frog leaping algorithm,SFLA),并提出了基于改进SFLA的认知无线电协作频谱感知方法,通过仿真对改进SFLA算法性能与传统SFLA算法性能进行了比较,并对本文提出的基于改进SFLA的协作感知方法与已有的基于修正偏差因子(modified deflection coefficient,MDC)的协作感知方法性能进行了比较.结果表明改进SFLA算法性能优于传统SFLA;基于改进SFLA的协作感知方法比MDC方法能获得更大的检测概率,验证
关键词:
认知无线电
频谱感知
混合蛙跳算法 相似文献
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《中国物理 B》2015,(12)
A cognitive radio(CR) network with energy harvesting(EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model(HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree(WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user(SU) and the interference to the primary user(PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming(MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution(DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service(Qos). Numerical results are given to verify our analysis. 相似文献
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Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm 总被引:1,自引:0,他引:1
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Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. 相似文献
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In this paper, we propose a spectrum-sharing protocol for a cooperative cognitive radio network based on non-orthogonal multiple access technology, where the base station (BS) transmits the superimposed signal to the primary user and secondary user with/without the assistance of a relay station (RS) by adopting the decode-and-forward technique. RS performs discrete-time energy harvesting for opportunistically cooperative transmission. If the RS harvests sufficient energy, the system performs cooperative transmission; otherwise, the system performs direct transmission. Moreover, the outage probabilities and outage capacities of both primary and secondary systems are analyzed, and the corresponding closed-form expressions are derived. In addition, one optimization problem is formulated, where our objective is to maximize the energy efficiency of the secondary system while ensuring that of the primary system exceeds or equals a threshold value. A joint optimization algorithm of power allocation at BS and RS is considered to solve the optimization problem and to realize a mutual improvement in the performance of energy efficiency for both the primary and secondary systems. The simulation results demonstrate the validity of the analysis results and prove that the proposed transmission scheme has a higher energy efficiency than the direct transmission scheme and the transmission scheme with simultaneous wireless information and power transfer technology. 相似文献
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Cognitive radio adaptation for power consumption minimization using biogeography-based optimization
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Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics.In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization(BBO) is introduced to solve this optimization problem. A novel habitat suitability index(HSI) evaluation mechanism is proposed,in which both the power consumption minimization objective and the quality of services(Qo S) constraints are taken into account. The results show that under different Qo S requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the Qo S requirements. Comparison with particle swarm optimization(PSO) and cat swarm optimization(CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications. 相似文献
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This paper presents a novel decision making framework for cognitive radio networks. The traditional continuous process of sensing, analysis, reasoning, and adaptation in a cognitive cycle has been divided into two levels. In the first level, the process of sensing and adaptation runs over the radio transmission hardware during run-time. In the second level, the process of analysis and reasoning runs in the background in offline mode. This arrangement offloads the convergence time and complexity problem of reasoning process during run-time. For implementation of the first level, a random neural network (RNN) based controller trained on an open loop case based database on the cloud has been designed. For the second level, a genetic algorithm (GA) based reasoning and an RNN based learning has been developed. The proposed framework is used to address the uplink power control problem of long-term evolution (LTE) system. The performance of RNN is compared with artificial neural network (ANN) and state-of-the-art fractional power control (FPC) scheme in terms of essential cognitive engine (CE) design requirements, capacity, and coverage optimization (CCO). The simulation results have shown that RNN based CE can achieve comparable results with faster adaptation, even subject to severe environment changes without the need of retraining. 相似文献