排序方式: 共有9条查询结果,搜索用时 15 毫秒
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Discrete channel modelling based on genetic algorithm and simulated annealing for training hidden Markov model 下载免费PDF全文
Hidden Markov models (HMMs) have been used to model burst error
sources of wireless channels. This paper proposes a hybrid method of
using genetic algorithm (GA) and simulated annealing (SA) to train
HMM for discrete channel modelling. The proposed method is compared
with pure GA, and experimental results show that the HMMs trained by
the hybrid method can better describe the error sequences due to SA's
ability of facilitating hill-climbing at the later stage of the
search. The burst error statistics of the HMMs trained by the
proposed method and the corresponding error sequences are also
presented to validate the proposed method. 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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提出了一种随机解调器压缩采样重构成败的判定方法. 该方法利用两次连续重构所得稀疏信号支撑之间的相关性来判断重构是否成功,其计算复杂度低,易于实现. 仿真结果表明,该方法能准确判断随机解调器压缩采样重构成败,用于宽带频谱感知中能够显著降低信号不稀疏时对主用户的干扰概率.
关键词:
认知无线电
频谱感知
随机解调器
压缩采样 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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Cognitive radio adaptation for power consumption minimization using biogeography-based optimization 下载免费PDF全文
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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