基于粒子群的CDMA功率和速率联合控制算法

提出了基于效用函数的CDMA网络下行链路的功率和速率联合控制最优化算法.在这类算法中,效用函数为非凸函数,经典的最优化理论很难解决这类问题.将粒子群优化方法应用于算法的非凸性设计,并通过仿真算例证明了该算法能有效解决非凸优化问题,且可保证系统的公平性.     

Joint iterative algorithm for optimal cooperative spectrum sensing in cognitive radio networks

In this paper, joint optimization of throughput and error rate via cooperative spectrum sensing in cognitive radio networks is investigated. An optimization problem is formulated, which aims to maximize the average achievable throughput of cooperating cognitive users while keeping the error rate at a lower level. This is a multi-variable nonconvex optimization problem. Instead of solving it directly, we propose an iterative algorithm which jointly optimizes the threshold and sensing time together to decrease the effect of the error and to increase the achievable throughput. We first prove that the local error rate of the cognitive user is a convex function of energy threshold and determine a closed-form for the optimal threshold which minimizes the error rate. Then we show that the AND rule is the optimal fusion rule to maximize the achievable throughput. Furthermore we determine the least number of cooperating cognitive users that can guarantee a minimum target error rate. This initial nonconvex problem is converted into a single variable convex optimization problem which can be successfully solved by common methods e.g. Newton’s method. Simulation results illustrate the fast convergence and effectiveness of the joint iterative algorithm.     

基于粒子群优化算法的无线电 频谱分配方法研究

粒子群(PSO)算法在认知无线电频谱分配问题上发挥着重要的作用,但是在连续无约束条件下基本的PSO 算 法才能得以运用,并且在此条件下,早熟收敛和收敛速度不够快等问题仍然无法得到效解决。为了优化这些问题,本文将对粒 子群算法的早熟收敛问题进行分析并加以改进,成功地将统一的粒子群算法应用于解决频谱分配问题。在综合考虑系统的总 宽带收益及用户接入公平性的基础上,建立了相应的目标函数,并验证了该算法的可行性和优越性。     

一种基于混沌粒子群优化的OFDM系统资源分配算法

针对无线多用户正交频分复用（OFDM）系统中功率分配问题,提出一种基于效用函数最大化框架的资源分配算法.在实际网络环境中,此类最优化算法为非凸的,利用经典最优化方法很难解决.为此,将智能优化中的粒子群方法应用到非凸优化算法设计中,并针对粒子群优化容易陷入局部极值点的问题,将Logistic混沌搜索嵌入PSO算法中,提出混沌粒子群算法.与同类算法相比,所提出算法不仅有效解决了非凸性问题,而且可以使系统具有更好的性能.     

Nonconvex resource control and lifetime optimization in wireless video sensor networks based on chaotic particle swarm optimization

Wireless video sensor networks (WVSNs) have attracted a lot of interest because of the enhancements that they offer to existing wireless sensor networks applications and their numerous potential in other research areas. However, the introduction of video raises new challenges. The transmission of video and imaging data requires both energy efficiency and quality of service (QoS) assurance in order to ensure the efficient use of sensor resources as well as the integrity of the collected information. To this end, this paper proposes a joint power, rate and lifetime management algorithm in WVSNs based on the network utility maximization framework. The optimization problem is always nonconcave, which makes the problem difficult to solve. This paper makes progress in solving this type of optimization problems using particle swarm optimization (PSO). Based on the movement and intelligence of swarms, PSO is a new evolution algorithm to look for the most fertile feeding location. It can solve discontinuous, nonconvex and nonlinear problems efficiently. First, since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the paper introduces chaos mapping into PSO with adaptive inertia weight factor to avoid the disadvantage of original PSO of easily getting to the local optimal solution in the later evolution period and keep the rapid convergence performance. Second, based on the distribution characteristics of the actual network, we decompose the resource control problem into a number of sub-problems using the hierarchical thought, where each user corresponds to a subsystem which is solved using the proposed CPSO3 method. Through the cooperative coevolution theory, these sub-optimization problems interact with each other to obtain the optimum of the system. Numerical examples show that our algorithm can guarantee fast convergence and fairness within a few iterations. Besides, it is demonstrated that our algorithm can solve the nonconvex optimization problems very efficiently.     

认知用户基于频谱特征的动态频谱接入算法

认知无线电频谱接入技术的关键是指导认知用户如何选择合适的空闲信道以及如何在认知用户间实现频谱共享。在公共控制信道较难获得的情况下,基于部分可观测 Markov 决策过程（POMDP）的频谱预测算法,可以显著地提高系统的吞吐量。认知系统如果不加区分地使用授权频谱将可能导致所选择的频谱空洞不能满足认知用户需求。针对认知用户对不同信道容量的需求,引用适量选择原则,并运用融合接入策略,研究认知无线网络动态频谱接入过程。另外,通过大量仿真对认知用户的吞吐量和系统碰撞率进行分析,结果表明融合接入策略可以有效地提高系统的吞吐量及系统碰撞率。     

一种权重融合模型的物联网频谱分配方案及其免疫实现

采用认知无线电技术, 设计了一种空闲频谱资源选择排序方法。在进行频谱选择时, 提出了一种无偏好信息的主客观权重融合方法, 提高权重设置的合理性, 并将求解权重转换为一个最优化问题, 通过免疫优化算法进行求解。实验结果表明, 该算法可以有效选择满足物联网节点传输需求的频谱, 并具有较高的网络吞吐量。     

一种最大化网络吞吐量的认知无线Ad Hoc网络跨层优化算法

认知无线Ad hoc网络(cognitive wireless ad hoc networks)是由一组具有认知决策能力的节点以多跳无线方式组成的智能网络.网络容量的求解与网络吞吐量的优化是该类网络研究的难点.作者首先推导了混叠模式下认知无线Ad hoc网络容量上界的闭合表达式,并指出该上界只与用户空间分布特性相关;然后提出了一种新的基于遗传算法的跨层优化算法,通过联合优化邻居选择与功率分配实现网络吞吐量的最大化;最后仿真验证了该算法的有效性,结果表明网络吞吐量能较好地逼近网络容量上界.     

认知无线网络中动态频谱接入性能分析

认知无线电技术利用频谱空洞进行通信,有效缓解了频谱资源紧缺问题,动态频谱接入是其核心技术。网络中主用户对授权频谱的使用效率较高时,次用户接入网络无法完成符合QoS要求的通信,只有当主用户频谱效率在一定门限值下时网络才适合次用户接入。有限频谱空洞资源只能满足有限次用户的通信需求,为了保证通信质量,网络在固定的主用户频谱效率下只能接入适量的次用户。提出用强制优先排队理论对认知无线网络中的动态频谱接入过程进行模拟,通过仿真对次用户的切换概率、阻塞概率两个QoS因子进行分析,在给定的QoS条件下,得到了网络适合次用户接入的主用户频谱效率门限值,以及在固定的主用户频谱效率下网络适合接入次用户的量。     

认知无线电网络基于QoS的监听时间与资源联合分配

以最大化所有认知无线电用户(CRU)的吞吐量为目标,同时保证每个CRU的服务质量(QoS)约束,研究了联合最优监听时间和资源分配问题,并基于此提出了一种监听时间与资源联合分配算法.在多信道认知无线电网络中,频谱监听和资源分配都会影响网络的吞吐量.兼顾二者的联合优化问题可以被分解为两个子问题:固定监听时间的资源分配问题, 以及固定资源分配策略的最优监听时间一维穷举搜索问题.提出的算法可以通过穷举搜索获得最优监听时间,并通过次梯度算法获得最优资源分配策略.仿真结果表明,提出的最优监听时间与资源分配算法可以最大化认知无线网络的吞吐量; 此外,各认知用户的QoS需求也能得到保证.     

混合模式下认知无线电网络的能效优化算法

认知无线电网络中,协作频谱感知利用多个节点同时感知可提高频谱感知检测性能。然而随着感知的次用户(SU)个数增加,导致能耗增高、能效(EE)降低。为解决这一问题,本文结合机会频谱接入和衬垫式频谱共享2种共享模式,构造基于混合频谱共享模式的能效模型,同时考虑3种不同的融合规则、主用户(PU)的再占据概率和报告信道误差,以最大化SU系统的EE为目标,使用拉格朗日乘子法与次梯度下降算法对感知时间、参与感知个数、次用户发射功率进行迭代优化求解。仿真结果表明,在最低服务质量要求(QoS)和发射功率的约束下,该能效优化算法能够实现更高的吞吐量和更高的能量效率。     

基于POMDP的信道感知接入算法

在认知无线电中,为了最大化次用户的吞吐量,同时对主用户的干扰低于预定值,提出一种基于POMDP的信道感知接入算法。次用户将主用户信道在时间轴上细分成等间隔的时隙,在每个时隙开始时,次用户从频谱感知、以较高的功率接入信道和以较低的功率接入信道三种可选策略中选择最优的策略。将次用户的选择过程建模成一个POMDP问题,并采用一些相应的最优策略求解。计算机仿真结果验证了算法的有效性。     

Network utility maximization over partially observable Markovian channels

We study throughput utility maximization in a multi-user network with partially observable Markovian channels. Here, instantaneous channel states are unavailable and all controls are based on partial channel information provided by ACK/NACK feedback from past transmissions. Equivalently, we formulate a restless multi-armed bandit problem in which we seek to maximize concave functions of the time average reward vector. Such problems are generally intractable and in our problem the set of all achievable throughput vectors is unknown. We use an achievable region approach by optimizing the utility functions over a non-trivial reduced throughput region, constructed by randomizing well-designed round robin policies. Using a new ratio MaxWeight rule, we design admission control and channel scheduling policies that stabilize the network with throughput utility that is near-optimal within the reduced throughput region. The ratio MaxWeight rule generalizes existing MaxWeight-type policies for the optimization of frame-based control systems with policy-dependent frame sizes. Our results are applicable to limited channel probing in wireless networks, dynamic spectrum access in cognitive radio networks, and target tracking of unmanned aerial vehicles.     

RAC: Range Adaptive Cognitive Radio Networks

In recent years, cognitive radio has received a great attention due to tremendous potential to improve the utilization of the radio spectrum by efficiently reusing and sharing the licensed spectrum bands, as long as the interference power inflicted on the primary users of the band remains below a predefined threshold level. Cognitive radio allows the secondary users in the cognitive radio network to access the licensed spectrum of the primary users opportunistically. In this paper, an autonomous distributed adaptive transmission range control scheme for cognitive radio networks which is called the RAC is proposed. The RAC considers the QoS requirements of both the primary and the secondary users simultaneously. The cognitive user's maximization of its achievable throughput without interfering the primary user by adapting transmission range of the secondary users dynamically is the key feature of the RAC. One of the advantages of using the proposed scheme is its implementation simplicity. The RAC is compared to other cognitive radio schemes in a simulation environment by using ns2. Simulations indicate that, the RAC can well fit into the mobile cognitive radio ad hoc networks and improve the network performance. Having compared to the other schemes utilizing contemporary cognitive radio technology, the RAC provides better adaptability to the environment and maximizes throughput and minimizes data delivery latency.     

认知无线电中基于QoS分级的频谱分配策略

为提高认知无线电中的系统吞吐量,保证频谱分配的公平性,提出一种基于服务质量(QoS)分级的频谱分配策略。建立模糊综合判决模型,根据认知用户的业务类型判别其QoS级别,应用CMSB信道分配算法进行频谱分配。仿真实验结果表明,该频谱分配策略能在满足认知用户QoS需求的同时,保证较高的系统吞吐量和接入公平性。     

Spectrum Allocation for Cognitive Radio Networks Using the Fireworks Algorithm

The fireworks algorithm features a small number of parameters, remarkable optimization ability, and resistance to a local optimum. Based on the graph coloring model, the fireworks algorithm is introduced for the first time to solve the spectrum allocation problem for cognitive radio networks, thus maximizing utility and fairness of spectrum allocation. Two-layer binary coding is adopted for individual fireworks. The first layer refers to the coding of cognitive users used to determine channels that can be connected with the user. The second layer refers to the auxiliary coding of channels responsible for addressing mutual interference among multiple cognitive users when they connect with the same channel at the same time. Explosion operator, mutation operator, and the selection operation are designed to allocate the spectrum for the cognitive radio network. Simulation results demonstrate superiority and efficiency of the proposed algorithm in terms of spectrum allocation.     

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore, artificial intelligence algorithms are used to improve traditional RA mechanisms. Deep learning is a promising tool for addressing resource management problems in wireless communication. In this study, we investigate a double deep Q-network-based RA framework that maximizes energy efficiency (EE) and total network throughput in unmanned aerial vehicle (UAV)-assisted terrestrial networks. Specifically, the system is studied under the constraints of interference. However, the optimization problem is formulated as a mixed integer nonlinear program. Within this framework, we evaluated the effect of height and the number of UAVs on EE and throughput. Then, in accordance with the experimental results, we compare the proposed algorithm with several artificial intelligence methods. Simulation results indicate that the proposed approach can increase EE with a considerable throughput.     

Spatial statistics and models of spectrum use

In order to opportunistically exploit unused radio spectrum nodes of dynamic spectrum access (DSA) networks monitor the spectrum around them. Such cognitive radios can greatly benefit from a spatial characterization of spectrum use. However, there is need to find an efficient way to describe spatial use, something which has not been studied in details so far. In this paper, we introduce spatial statistics techniques as promising methods to describe spectrum use and enable optimization of DSA networks. We discuss two approaches to spatial modelling of spectrum, namely a deterministic approach based on a system model of the complete radio environment and an empirical approach that exploits passive measurements of the spectrum use. We elaborate on the impact of different network properties on the models and provide realistic parameter sets for generation of simulation scenarios. Additionally, we investigate cooperative sensing as a use case for spatial statistics based runtime optimization of the network configuration.     

动态主用户流量模型的跨层优化设计

为适应主用户流量变化较快的场景,在不完美频谱感知的情况下最大化认知用户的吞吐量,提出了一种基于集中式Overlay认知无线网络中感知时间与资源分配跨层优化算法。将优化目标分解为信道分配和检测时间同功率分配联合优化两个子问题,通过子算法迭代,最终得到感知时间与资源分配的联合最优解。仿真结果表明,相对于仅考虑频谱感知或资源分配的单层优化算法,该算法可在兼顾公平的前提下使次用户吞吐量得到有效提升。     

大规模认知无线电网络的时延分析

时延作为无线网络的最基本的性能之一,对网络信息分发、路由协议设计、节点部署等都具有重要意义。与传统的无线网络不同,认知无线电网络的频谱资源具有动态变化性,该特性会对网络时延产生极大的影响。因此,如何对动态频谱环境下的大规模认知无线电网络进行时延分析,是一项很具挑战性的课题。为此,首先对动态频谱环境进行建模,将认知用户的频谱接入过程建模为一个连续时间的马尔可夫链,并建立认知用户的生存函数来量化授权用户活动以及信道数量对频谱环境的影响;其次,将上述模型与首次通过渗流理论结合起来,研究了大规模认知无线电网络时延的伸缩规律,并获取了更为精确的时延与距离比的上限值。理论分析及仿真结果表明,动态频谱环境与密度一样会对时延产生极大影响。研究结论对认知无线电网络的设计具有重要的指导意义。