基于组合混沌遗传算法的认知无线电资源分配

提出了基于组合混沌遗传算法用于认知无线电资源分配,设计了相应的组合混沌序列发生器,并分别运用组合混沌遗传算法、粒子群优化算法、模拟退火算法及简单遗传算法对认知无线电资源分配问题进行了仿真分析.结果表明,组合混沌遗传算法具有收敛速度快、搜索空间广、全局收敛等优点.相比其他三种算法,基于组合混沌遗传算法进行资源分配提高了认知无线电系统的传输速率,降低了系统的发射功率及误码率,同时加快了收敛速度. 关键词： 组合混沌 认知无线电 遗传算法 资源分配     

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

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.     

基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配

提出了一种基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配方法,该方法运用混沌序列和并行遗传算法来动态调整传感器网络节点的探测目标及通信时隙等参数,对资源分配方式进行跨层整体优化.在多目标无线传感器网络环境下,将本文方法与传统的随机分配方法、动态规划方法、T-MAC协议及S-MAC协议等资源分配算法进行了仿真比较.仿真结果表明,本文提出的混沌并行遗传算法具有通信时延小,目标检测成功率高等优点,在降低了无线传感器网络功率消耗的同时提高了对目标检测的实时性. 关键词： 无线传感器网络 无线资源管理 Henon映射 并行遗传算法     

Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of primary users (PU) networks are shared with secondary users (SU) and energy harvesting allows nodes of the CRN to acquire energy from the environment for operation sustainability. However, amount of energy harvested from the environment is not fixed and requires dynamic allocation of resources for obtaining optimum network and throughput capacity. In this work, we overcome the limitations of existing Q-learning based resource allocation schemes which are constrained by large state-space systems and have slow convergence. Proposed deep Q-learning based algorithm improves the resource allocation in EH-CRN, while considering quality of service (QoS), energy and interference constraints. Simulation results show that proposed algorithm provide improved convergence and better resource utilization compared to other techniques in literature.     

一种用于认知无线电资源分配的并行免疫遗传算法

提出了用于认知无线电自适应调制和资源分配的并行免疫遗传算法,并对该算法、简单遗传算法和静态调制分配算法进行了仿真.仿真结果显示,该算法具有很强的全局搜索能力和较快的收敛速度,在误码率和功率受限条件下,该算法比简单遗传算法和静态调制方式的性能更好,同时明显降低了计算复杂度.     

Chaotic itinerancy in coupled dynamical recognizers

We argue that chaotic itinerancy in interaction between humans originates in the fluctuation of predictions provided by the nonconvergent nature of learning dynamics. A simple simulation model called the coupled dynamical recognizer is proposed to study this phenomenon. Daily cognitive phenomena provide many examples of chaotic itinerancy, such as turn taking in conversation. It is therefore an interesting problem to bridge two chaotic itinerant phenomena. A clue to solving this is the fluctuation of prediction, which can be translated as "hot prediction" in the context of cognitive theory. Hot prediction is simply defined as a prediction based on an unstable model. If this approach is correct, the present simulation will reveal some dynamic characteristics of cognitive interactions.     

混沌量子克隆算法求解认知无线网络频谱分配问题

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     

基于量子遗传算法的认知无线电决策引擎研究

提出了基于量子遗传算法的认知无线电决策引擎，设计了待优化的多目标函数，利用量子遗传算法调整优化无线电参数，运用多载波系统对算法性能进行了仿真分析.实验结果表明该方法在收敛速度、收敛精度和算法稳定性上都明显优于经典遗传算法，在种群规模较小时仍然能获得很好性能，适合于实际实现.不同权重设置模式下仿真结果表明该方法能够在多个目标函数间进行权衡，参数调整结果与当前对目标函数的偏好一致.     

认知无线网络中基于Sigmoid函数的功率控制算法研究

为了实现认知无线网络中频谱分配公平性以及契合现代化绿色通信的需求,根据非合作博弈论和干扰温度,引入信道状态概念,设计出一种新型功率控制算法,分析了该算法的收敛性、纳什均衡解的存在性和唯一性。该算法不仅可以快速收敛,符合实时通信,而且分布式实施,简单实用。仿真结果表明,相比其他算法,该算法系统干扰小,能源消耗低,具有抗干扰性能,而且在日益多用户网络的情况下,具有低功率、低干扰,提高网络的整体效益,更加符合现代化的绿色通信的需求。     

Resource allocation for LTE-based cognitive radio network with queue stability and interference constraints

We consider the problem of interference management and resource allocation in a cognitive radio network (CRNs) where the licensed spectrum holders (primary users) share their spare capacity with the non-licensed spectrum holders (secondary users). Under such shared spectrum usage the transmissions of the secondary users should have a minimal impact on the quality of service (QoS) and the operating conditions of the primary users. Therefore, it is important to distinguish the two types of users, and formulate the problem of resource allocation considering hard restrictions on the user-perceived QoS (such as packet end-to-end delay and loss) and physical-layer channel characteristics (such as noise and interference) of the primary users. To achieve this goal, we propose to assign the bandwidth and transmission power to minimize the total buffer occupancy in the system subject to capacity constraints, queue stability constraints, and interference requirements of the primary users. We apply this approach for resource allocation in a CRN built upon a Third Generation Partnership Project (3GPP) long-term evolution (LTE) standard platform. Performance of the algorithm is evaluated using simulations in OPNET environment. The algorithm shows consistent performance improvement when compared with other relevant resource allocation techniques.     

拟态物理学优化的认知无线电网络频谱分配

针对认知无线电网络中基于图着色模型的频谱分配问题,基于其非确定性多项式特性,以最大化网络收益总和为目标,提出了一种基于拟态物理学优化的求解算法. 在拟态物理学优化算法中,将频谱分配问题的解映射为一个具有质量的微粒,通过建立微粒的质量与其适应值之间的关系,并利用万有引力定律定义微粒间的虚拟作用力的大小,使整个群体向更好的方向运动,实现群体寻优. 给出了频谱分配问题的具体求解过程,并根据分配问题的二进制编码特点,改进了微粒的位置更新方程. 仿真实验表明：本文算法能更好地实现网络收益最大化. 关键词： 拟态物理学优化 认知无线电网络 频谱分配 网络收益     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

混沌免疫算法求解认知无线电网络资源分配问题

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.     

Efficient design method for cell allocation in hybrid CMOS/nanodevices using a cultural algorithm with chaotic behavior

The hybrid CMOS molecular (CMOL) circuit, which combines complementary metal–oxide–semiconductor (CMOS) components with nanoscale wires and switches, can exhibit significantly improved performance. In CMOL circuits, the nanodevices, which are called cells, should be placed appropriately and are connected by nanowires. The cells should be connected such that they follow the shortest path. This paper presents an efficient method of cell allocation in CMOL circuits with the hybrid CMOS/nanodevice structure; the method is based on a cultural algorithm with chaotic behavior. The optimal model of cell allocation is derived, and the coding of an individual representing a cell allocation is described. Then the cultural algorithm with chaotic behavior is designed to solve the optimal model. The cultural algorithm consists of a population space, a belief space, and a protocol that describes how knowledge is exchanged between the population and belief spaces. In this paper, the evolutionary processes of the population space employ a genetic algorithm in which three populations undergo parallel evolution. The evolutionary processes of the belief space use a chaotic ant colony algorithm. Extensive experiments on cell allocation in benchmark circuits showed that a low area usage can be obtained using the proposed method, and the computation time can be reduced greatly compared to that of a conventional genetic algorithm.     

混沌量子克隆优化求解认知无线网络决策引擎

通过分析认知无线网络引擎决策, 给出了其数学模型, 并将其转化为一个多目标优化问题, 进而提出一种基于混沌量子克隆的优化求解算法, 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. 最后, 在多载波环境下对算法进行了仿真实验. 结果表明, 与QGA-CE(基于量子遗传算法的认知引擎)算法相比, 本文算法收敛速度较快, 具有较高的目标函数值, 可以对无线参数优化调整, 满足认知引擎的实时性要求.     

A multi-timescale resource allocation algorithm based on self-learning for distributed fog radio access networks

Faced with limited network resources, diverse service requirements and complex network structures, how to efficiently allocate resources and improve network performances is an important issue that needs to be addressed in 5G or future 6G networks. In this paper, we propose a multi-timescale collaboration resource allocation algorithm for distributed fog radio access networks (F-RANs) based on self-learning. This algorithm uses a distributed computing architecture for parallel optimization and each optimization model includes large time-scale resource allocation and small time-scale resource scheduling. First, we establish a large time-scale resource allocation model based on long-term average information such as historical bandwidth requirements for each network slice in F-RAN by long short-term memory network (LSTM) to obtain its next period required bandwidth. Then, based on the allocated bandwidth, we establish a resource scheduling model based on short-term instantaneous information such as channel gain by reinforcement learning (RL) which can interact with the environment to realize adaptive resource scheduling. And the cumulative effects of small time-scale resource scheduling will trigger another round large time-scale resource reallocation. Thus, they constitute a self-learning resource allocation closed loop optimization. Simulation results show that compared with other algorithms, the proposed algorithm can significantly improve resource utilization.     

基于二进制粒子群算法的认知无线电决策引擎

提出了基于粒子群算法的认知无线电决策引擎,并提出了一种种群自适应粒子群算法,利用粒子群算法调整优化无线电参数,运用多载波系统对算法性能进行了仿真分析.实验结果表明基于二进制粒子群算法的认知决策引擎在收敛速度、收敛精度和算法稳定性上都要明显优于经典遗传算法,基于种群自适应粒子群算法的决策引擎则能进一步提高算法初期性能,满足认知无线电实时性要求. 关键词： 认知无线电 粒子群算法 遗传算法 认知决策引擎     

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.     

Binary power allocation for cognitive radio networks with centralized and distributed user selection strategies

Motivated by the desire for efficient spectral utilization, we present a novel algorithm based on binary power allocation for sum rate maximization in Cognitive Radio Networks (CRN). At the core lies the idea of combining multi-user diversity gains with spectral sharing techniques and consequently maximizing the secondary user sum rate while maintaining a guaranteed quality of service (QoS) to the primary system. We consider a cognitive radio network consisting of multiple secondary transmitters and receivers communicating simultaneously in the presence of the primary system. Our analysis treats both uplink and downlink scenarios. We first present a distributed power allocation algorithm that attempts to maximize the throughput of the CRN. The algorithm is simple to implement, since a secondary user can decide to either transmit data or stay silent over the channel coherence time depending on a specified threshold, without affecting the primary users’ QoS. We then address the problem of user selection strategy in the context of CRN. Both centralized and distributed solutions are presented. Simulation results carried out based on a realistic network setting show promising results.     

单模激光混沌系统的可视化模型及仿真研究

提出了一种直接用仿真软件建立单模激光混沌系统可视化模型的方法。分析了单模激光系统的混沌特性 ,并对单模激光系统的混沌同步与控制进行了仿真研究。该方法的优点是无需用传统的程序代码对模型和算法进行编程 ,且可实现混沌光学系统建模及仿真分析的全程可视化 ,是研究光学动力学系统的一种简便、有效的新方法