A Distributed Power Allocation Scheme in Green Cognitive Radio Ad Hoc Networks

For the realization of green communications in cognitive radio ad hoc networks(CRAHNs), selfadaptive and efficient power allocation for secondary users(SUs) is essential. With the distributed and timevarying network topology, it needs to consider how to optimize the throughput and power consuming, avoid the interference to primary users(PUs) and other SUs, and pay attention to the convergence and fairness of the algorithm. In this study, this problem is modeled as a constraint optimization problem. Each SU would adjust its power and corresponding strategy with the goal of maximizing its throughput. By studying the interactions between SUs in power allocation and strategy selection, we introduce best-response dynamics game theory and prove the existence of Nash equilibrium(NE) point for performance analysis. We further design a fully distributed algorithm to make the SUs formulate their strategy based on their utility functions, the strategy and number of neighbors in local area. Compared with the water-filling(WF) algorithm, the proposed scheme can significantly increase convergent speed and average throughput, and decrease the power consuming of SUs.     

认知无线电带宽频谱感知性能分析及优化

为了降低认知无线电中主用户受到的干扰,根据主用户状态的变化,通过将通信带宽分为感知频带和传输频带,建立了次用户在整个帧周期内进行带宽频谱感知的模型.该模型中,次用户在每个帧使用部分带宽进行频谱感知,其余的带宽传输数据.在限制主用户受到的干扰和次用户虚警概率的基础上,通过联合优化感知带宽比和检测概率,最大化次用户的频谱效率.使用MATLAB软件仿真模型的干扰概率和频谱效率,结果表明:相比传统的时间频谱感知,当虚警概率为0.5时,带宽频谱感知能够使主用户受到干扰的概率降低30%;当干扰概率为0.2时,带宽频谱感知能够使次用户的频谱效率提高0.3 (bit/s)/Hz.      

Reputation-based collaborative spectrum sensing scheme in cognitive radio networks

Collaborative spectrum sensing is proposed to improve the detection performance in cognitive radio (CR) networks. However, most of the current collaborative sensing schemes are vulnerable to the interference of the malicious secondary users (SUs). In this paper we propose a reputation-based collaborative spectrum sensing scheme to improve the security of cooperative sensing by mitigating the impacts of misbehaviors. The fusion center calculates the reputation rating of each SU according to their history reports to weight their sensing results in the proposed scheme. We analyze and evaluate the performance of the proposed scheme and its advantages over previous schemes in expansibility and integrity. Simulation results show that the proposed scheme can minimize the harmful influence from malicious SUs.     

基于价格机制的无线多跳网络拥塞控制算法

为有效缓解拥塞,并在QoS保证与业务公平性之间找到合适的平衡点,基于定价机制提出了一种链路层逐跳拥塞控制算法.该算法考虑了MAC的时间限制和二进制干扰模型限制,将传输失败率作为网络拥塞的控制准则之一引入拥塞价格函数中;流路径上的每个节点根据接收到的拥塞价格动态调整每个流的传输速率;优化目标是系统中总的流效用之和达到权重比例公平.此外,还考虑了节点内部流的调度策略,根据等待时间函数动态调整节点内各个流的优先级,以保证每个流的QoS需求和公平性.仿真结果表明:提出的拥塞控制策略能够有效缓解链路拥塞,系统总吞吐率比未采用拥塞控制机制提高约41%,公平性提高约29%,且极大地改善了平均时延、平均丢包率、平均吞吐率等其他性能指标.     

基于偏好序的高铁票价与售票时间窗联合优化

采用形式灵活且适用性好的偏好序模型刻画旅客购票选择行为，拓展现有高速铁路动态定价与售票控制策略联合优化的研究方法。按照票价变动范围划分客票等级，结合偏好序模型的特点，通过决策各等级客票售票时间窗进行售票控制，同时，在一定范围内优化各客票等级的票价，实现票价与售票控制的联合决策。本文在分析弹性客流的基础上，采用偏好序选择概率、 到达率和时间窗长度测算客票销售量。根据运营需要设定客票产品排序，以各客票产品停售时间和票价作为决策变量，建立以期望总收益最大为目标的非线性规划模型，并且将粒子群算法与线性规划精确求解嵌套求解模型。以京沪高铁为背景进行数值实验，结果表明：与固定各等级票价只优化售票时间窗的方案相比，联合优化方案可提升期望总收益约4.54%；不同需求水平下，联合优化方案的期望总收益均高于固定票价方案；期望总收益随着转移购买概率增加而增加，且不同转移概率取值下联合优化方案的期望总收益均高于固定票价方案。所提方案可为高铁列车动态定价和制定售票控制策略提供决策支持。     

Stochastic optimization in cooperative relay networks for revenue maximization

In cellular networks, cooperative relaying is an economic and promising way to enlarge the network capacity and coverage. In the case that multiple users and multiple relays are taken into account, efficient resource allocation is important in such networks. In this paper, we consider the joint relay power control with amplify- and-forward （AF） strategy and dynamic pricing for uplink cellular networks in order to maximize the network administrator＇s system revenue. The system revenue is associated with pricing strategies and mobile users＇ random data request, which is supported by the relay assisted transmission. To deal with the problem of the coupling in pricing and relay resource allocation, we utilize Lyapunov optimization techniques to design online pricing and relay power control without any statistic information of random events in networks. Theoretical analysis shows that the proposed algorithm can achieve a near-optimal performance and simulation results also validate its effectiveness and robustness.     

An Iterative Power Allocation Algorithm for Group-wise Space-Time Block Coding Systems

An iterative transmit power allocation (PA) algorithm was proposed for group-wise space-time block coding (G-STBC) systems with group-wise successive interference cancellation (GSIC) receivers. Group-wise interference suppression (GIS) filters are employed to separate each group's transmit signals from other interferences and noise. While the total power on all transmit symbols is constrained, all transmit PA coefficients are updated jointly according to the channel information at each iteration. Through PA, each detection symbol has the same post-detection signal to interference-and-noise ratio (SINR). The simulation results verify that the proposed PA algorithm converges at the equilibrium quickly after few iterations, and it achieves much lower bit error rates than the previous single symbol SIC PA and the fixed ratio PA algorithms for G-STBC systems with GSIC receivers .     

基于离散价格的高铁差异化定价优化研究

与现有大多数结果为连续值的高铁动车组定价理论不同，本文提出一种基于离散价格的高铁动车组差异化定价优化模型。首先设置离散的折扣率档位，再将旅客群体按照价格敏感性分类，考虑出发到达时间不在同一时间段给旅客带来额外机会成本的情况下分别得到各群体旅客乘车广义费用函数，基于广义费用函数估计总体旅客弹性需求函数，构建基于收益最大化为目标的多列车多区段下的线性整数规划模型，最后采用粒子群算法进行求解。算例结果表明，采用本文提出的优化方法能够显著提升列车的整体收益，同时可以将定价理论中输出最优价格为连续值的情况看做是基于离散价格定价优化问题的一个特殊情况，基于离散价格的假设应该更接近于定价优化问题的实际，应得到更多的关注。     

基于批量订购的铁路集装箱货运多等级运价研究

铁路集装箱货运发展迅速,在铁路货运中占比逐渐增大.当前铁路总公司没有针对 大批量订购采取优惠折扣,导致部分客户转向其他运输方式.针对这个问题,设计了多等级运 输价格策略来吸引大客户,建立了铁路集装箱货运收益最大的动态价格模型,求解出各订购 量对应的最优价格等级.本文以大朗到大红门的集装箱货运班列为例,求解出不同时刻各订购 量的最优等级运价.通过对比优化前后的结果,证明了该模型可以显著提高铁路集装箱运输的 收益.     

QoS awared power and admission controls based on non-cooperative game theory in wireless networks

In order to better accommodate heterogeneous quality of service (QoS) in wireless networks, an algorithm called QeS-aware power and admission controls (QAPAC) is proposed. The system is modeled as u non- cooperative game where the users adjust their transmit powers to maximize the utility, thus restraining the interferences. By using adaptive utility functions and tunable pricing parameters according to QoS levels, this algorithm can well meet different QoS reqniremcnts and improve system capacity compared with those that ignore the QoS differ- ences.     

随机扰动项呈离散分布的库存路径和定价问题的模型与算法

针对产品需求价格函数随机扰动项呈离散分布的库存路径和定价问题(Inventory Routing and Pricing Problem,IRPP),利用随机扰动项的离散分布率和零售商库存服务水平要求,以供货商期望收益最大化为目标,构建IRPP优化模型,将禁忌搜索算法嵌入改进的粒子群算法中求解模型.3组不同规模的算例分...     

城市轨道交通峰前折扣定价方案编制模型

构建面向高峰拥挤缓解的峰前折扣定价方案编制模型，为定价方案编制提供决策方法. 基于票价变动对高峰客流的影响分析，考虑运营企业可承担的收益损失，建立以运力运量匹配度最大化为目标的峰前折扣定价方案编制模型，科学确定折扣车站、折扣比例及折扣时段. 对北京地铁八通线进行实证研究，结果表明：相比实际方案，优化方案中折扣截止时间较晚，认为限制当前政策效果的关键在于折扣截止时间设置不合理；在不增加收益损失的前提下，可通过定价方案优化提高客流拥挤疏解效果.     

RFID系统多阅读器防碰撞问题研究

为了解决多阅读器碰撞问题,提出了基于概率功率控制的神经网络优化算法。该算法中各阅读器的发射功率按β分布动态发生变化,然后利用简单神经网络即感知器对多个阅读器功率的相似性进行判别并处理,使各阅读器彼此之间工作在最佳状态。仿真实验结果表明:多阅读器之间的干扰大大减小,其识别标签的距离可以达到期望值。     

A Novel Interference Modeling Scheme in Cognitive Networks

In this paper,we propose a mathematical model of aggregate co-channel interference over Rayleigh fading in cognitive networks.Unlike the statistical models in the literature that aim at finding the bound or approximation of the interference,the proposed model gives an accurate expression of probability density function(PDF),cumulative distribution function(CDF) and mean and variance of the interference,which takes into account a number of factors,such as spectrum sensing scheme,and spatial distribution of the secondary users(SUs).In particular,we focus on a more general spatial structure where there are two roles of primary users(PUs)and the interfering SUs distributed in the two-dimensional space.The framework developed in this paper is easy to be applied in power control,error evaluation and other applications.     

运输企业中不同差别价格策略

用微观经济学方法对不同结构的交通市场中的生产者行为进行了分析, 对不完全竞争者和垄断者利用市场权力谋求最大化利益的情况进行的分析表明, 生产者的市场权力取决于需求弹性和替代品数量, 不完全竞争者可以根据具体情况运用不同的差别价格策略实现利益最大化, 特别是固定投资巨大而机会成本相对较小的铁路企业还可以通过差别价格策略获得最佳价格组合。铁路企业若要理性地实施差别价格策略, 必须对市场进行细分, 估计各细分市场的需求弹性与替代品数量, 然后还要估计各产品的短期成本函数与制造产品差异。     

基于模块化仿真的共享汽车联合调度优化

运营商在调度车辆时单独采用员工或顾客调度策略均难以有效解决共享汽车分布不均衡导致的盈利难问题.为此，在传统时空网络基础上，考虑道路拥堵和用车需求随时间变化对运营的影响，基于C#语言和O2DES(object-oriented discrete event simulation)离散事件仿真框架，建立由模块化站点和路段模型组成、可高效率运行的共享汽车仿真系统；在此基础上，提出一个以运营商日均净收益最大化为目标，联合决策车辆库存量阈值和行程定价的仿真优化模型，并为解决随机环境下的全局优化问题，设计了EGA-OCBA (elitist genetic algorithm with optimal computing budget allocation)算法；最后，以成都市的5个共享汽车站点为例，验证了仿真优化模型的有效性.仿真优化结果表明：在相同车队规模下，与采用固定价格的顾客调度策略相比，联合策略可使日均净收益提升10.37%～162.30%；与单独的员工调度策略相比，联合策略可使日均净收益提升15.34%.     

Distributed Power Control Considering Different Behavioural Responses of Electric Vehicle Drivers in Photovoltaic Charging Station

A distributed energy management in a photovoltaic charging station(PV-CS) is proposed on the basis of different behavioural responses of electric vehicle(EV) drivers. On the basis of the provider or the consumer of the power, charging station and EVs have been modeled as independent players with different preferences. Because of the selfish behaviour of the individuals and their hierarchies, the power distribution problem is modeled as a noncooperative Stackelberg game. Moreover, Karush-Kuhn-Tucker(KKT) conditions and the most socially stable equilibrium are adopted to solve the problem in hand. The consensus network, a learning-based algorithm, is utilized to let the EVs communicate and update their own charging power in a distributed fashion. Simulation analysis is supported to show the static and dynamic responses as well as the effectiveness and workability of the proposed charging power management. For the sake of showing the responses of EV drivers, different behavioural responses of EVs' drivers to the discount on the charging price offered by the station are introduced. The simulation results show the effectiveness of the proposed energy management.     

基于负载均衡的OFDMA双跳中继网络资源分配策略

为适应蜂窝小区内不同的用户分布,针对基于正交频分多址接入技术的双跳中继网络,提出了一种基于负载均衡的资源分配策略.考虑难以得到联合分配的最优解,在降低计算复杂度的前提下,采用分步式次优化分配.采用比例公平算法对子载波进行分配,并用数学建模的方法解决功率分配问题;根据凸规划和注水算法确定功率分配最优解需满足的条件,在逼近最优解的目标下,求得基站和中继站的发射功率.仿真分析结果表明,与传统的静态资源分配策略相比,提出的基于负载均衡的资源分配策略可以适应不同的用户分布和信道条件,系统吞吐率提高7.8%以上.      

连续交通网络设计的全局双层多项式优化模型

提出了一种面向典型连续交通网络设计问题的全局双层多项式优化模型,其函数均为多项式,且下层问题为凸问题;上层问题旨在优化网络性能,下层问题用来刻画确定性用户均衡(DUE)交通流模式;利用Fritz John条件和乘子代替下层规划,将提出的双层多项式优化模型转换为等价单层优化问题,并利用矩半定规划(MSDP)方法得到其全局最优解;利用矩矩阵的秩作为保证全局最优性的充分条件,并估计全局最优解的个数;给出了最优道路收费问题的数值算例,用提出的双层多项式优化模型描述了算例中的最优道路收费问题,并通过Wardrop用户均衡约束调整现有路段上的交通流量,使总通行费收益最大化。研究结果表明：该简单算例的最大收益为13.5元,同时可以得到该算例的矩矩阵的秩为1,从而证明了该结果的全局最优性,提出的方法克服了均衡约束数学规划(MPEC)法和值函数法等现有求解双层优化问题的经典算法由于连续交通网络设计固有的非凸性,只能找到局部最优的问题;提出的全局双层多项式优化模型与算法为典型连续交通网络设计提供了更好的探索工具。     

第三方仓储能力配置与分配的收益优化

基于收益管理思想，对随机市场需求条件下仓储能力的配置与分配问题进行了研究，以谋求收益最大化．建立了随机规划模型，并运用稳健优化处理随机变量，将随机问题转换为线性规划问题．通过实例仿真，确定了最优仓储能力配置和分配方案，并对结果进行了分析．计算结果表明，用该模型优化的收益增长了2．66％．