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移动基站MS(Mobile Station)在LTE-A(Long Term Evolution-Advanced长期演进)协议中要求上行链路的数据要在双天线和三个时隙中传输,这就使得针对LTE的空时分组码有了一个新的应用点.本文在传统的STBC(Space-Time Block Coding空时分组码)的基础上,通过改进,提出一种新的传输方案,使得数据实现在三个时隙使用双发射天线的传输.该方案特点:(1)它可以实现传输的全速率和全分集;(2)它的最大似然译码(Maximum Likelihood,ML)需要联合三个实信号;(3)它的最小行列式值并没有通过信号星座图的改变而变化;(4)它与单天线传输的模式相兼容.仿真结果显示提出的方案比传统的准正交空时分组码QSTBC方案和Alamouti方案在相同信噪比(SNR)的情况下有着更好的误比特率(BER). 相似文献
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首先介绍了认知无线电技术和博弈论思想,然后按照不同的分类准则,考虑无线网络资源分配的实际情况,描述了两种常用的博弈论类型:非合作型博弈模型和合作型博弈模型。重点研究了两种博弈模型在认知无线电频谱共享技术中的资源分配问题。 相似文献
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频谱共享是认知无线网络关键技术之一。为消除认知无线网络中频率选择性信道下授权主用户与认知用户间的相互干扰,本文提出了一种新的频谱共享方法。该方法充分利用了无线通信系统中由信道的频率选择性衰落导致的不同用户信道的不相关性,通过求解矩阵方程获得预处理矩阵的通解,并在主用户和认知用户发射端分别进行预处理。从而实现认知系统中主用户与认知用户之间的相互零干扰,并使每个用户都可有效地传输数据。理论推导及系统仿真均表明,新方法可以有效地消除授权主用户与认知用户之间的双向干扰,实现不同用户平等地共享无线频谱资源。新方法可以提高频谱的利用率,一定程度上缓解无线频谱资源在当前及未来无线通信领域日益紧缺的矛盾。而且新方法也同样适用于不同认知用户之间共享频谱。 相似文献
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一种认知无线Adhoc网络跨层最优频谱共享方案 总被引:1,自引:0,他引:1
针对由认知无线电组成的Adhoc网络中的频谱共享提出了一个跨层方案。通过综合考虑认知用户之间的干扰约束和流量需求,特别考虑到用户双向连接以及信道的非均匀性,以BFP(bandwidth—footprint-product)值最小为目,建立了一个二进制整数线性规划(BILP)模型。仿真结果给出了最优的频带分配和路由选择方案。 相似文献
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Sungwook Kim 《Wireless Personal Communications》2013,68(3):893-905
Bandwidth is an extremely valuable and scarce resource, and may become congested to accommodate diverse services in wireless communications. To enhance the efficiency of bandwidth usage, the concept of cognitive radio has emerged as a new design paradigm. In this paper, a new bandwidth sharing algorithm is developed for cognitive radio networks. Under dynamically changing network environments, we formulate the bandwidth sharing problem as a two-way matching game model. In addition, modified game theory is adopted to reach a near Pareto optimal solution while avoiding bandwidth inefficiency. This approach can make the system more responsive to the current network situation. With a simulation study, it is demonstrated that the proposed scheme approximates an optimized solution under widely diverse traffic load intensities. 相似文献
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本文提出一种基于群体智能的自组织网络SON (Self-Organizing Network)中的服务质量QoS (Quality of Service)单播路由机制,通过引入模糊数学相关知识描述不精确路由信息,通过引入微观经济学相关知识兼顾网络提供方和用户利益,基于蚂蚁网络寻找使各方效用达到或接近Nash均衡下Pareto最优的QoS单播路径.基于网络仿真器NS2 (Network Simulator 2)对该机制进行了仿真实现和性能评价,结果表明,该机制在用户效用、网络提供方效用、路径综合评价值、Nash均衡下Pareto最优比和路由成功率等方面具有良好性能. 相似文献
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该文研究了在WCDMA网络中如何选择价格来最大化网络收益.没有采用拥塞相关的计费,而是对每个用户有效传输的单位吞吐量收取固定的费用,但每个用户的传输速率是网络拥塞和单位带宽价格的函数,并在此基础上提出了用户净效用函数.利用Stackelberg博弈,建模网络与用户之间的交互,即一方面网络管理者设定价格,以便实现收益最大化,而用户通过自优化效用函数来寻找新的均衡点对此做出响应.本文提供了网络收益与接纳用户数目的定量关系,并研究了网络降低用户传输速率以增大网络容量和拥塞控制的经济动机. 相似文献
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The cognitive network has become a promising method to solve the spectrum resources shortage problem. Especially for the optimization of network slicing resources in the cognitive radio access network(RAN), we are interested in the profit of the mobile virtual network operator(MVNO) and the utility of secondary users(SUs). In cognitive RAN,we aim to find the optimal scheme for the MVNO to efficiently allocate slice resources to SUs. Since the MVNO and SUs are selfish and the game between the MVN... 相似文献
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为鼓励视频服务提供商参与到缓存过程中,本文提出一种基于Stackelberg博弈的激励缓存资源分配算法。与传统激励缓存资源分配方案不同,本文考虑同时存在多个网络运营商和多个视频服务提供商,视频服务提供商从网络运营商处购买存储空间以缓存热门视频。针对该场景,本文将该激励缓存模型建模为多主多从Stackelberg博弈问题,分别构建主方和从方的效用函数,证明了在网络运营商价格确定的情况下,视频服务提供商之间的非合作博弈存在纳什均衡。文章利用分布式迭代算法对该博弈模型进行求解,获得了视频服务提供商的最优缓存策略和网络运营商的最优价格策略。仿真结果表明,本文提出的激励缓存机制可使视频服务提供商获得比其他缓存分配算法更高的单位成本收益。 相似文献
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分析了具有逐段线性特性的Tent混沌映射和其推广形式:分段Tent混沌映射在计算机有限精度影响下的性能.在此基础上,根据Logistic混沌映射与Tent混沌映射之间具有的拓扑共轭关系,研究了Logistic混沌映射的推广形式:分段Logistic混沌映射,通过实验分析指出用类似于分段Tent混沌映射的方式来定义分段Logistic混沌映射是不可取的.本文构造了一个全新的分段Logistic混沌映射,通过实验对该映射产生的序列的随机性、初值敏感性等性质进行了研究.结果表明,本文定义的分段Logistic混沌映射产生的序列具有良好的随机性和初值敏感性. 相似文献
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The Long Term Evolution Advanced (LTE-A) and Internet of Things (IoT) are two of the most popular topics that receive vast attentions in the filed of information technologies. Radio Frequency Identification (RFID) greatly pushes IoT forward to practical utilization. In RFID system, one of the key issues is the collision among tags when reader tries to identify several of them simultaneously. In this paper, the authors proposed a RFID tag management scheme for cargo identification and tracking based on the L... 相似文献
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Stackelberg Game Based Incentive Mechanisms for Multiple Collaborative Tasks in Mobile Crowdsourcing
In this paper, we tackle the problem of stimulating users to join mobile crowdsourcing applications with personal devices such as smartphones and tablets. Wireless personal networks facilitate to exploit the communication opportunity and makes diverse spare-resource of personal devices utilized. However, it is a challenge to motivate sufficient users to provide their resource of personal devices for achieving good quality of service. To address this problem, we propose an incentive framework based on Stackelberg game to model the interaction between the server and users. Traditional incentive mechanisms are applied for either single task or multiple dependent tasks, which fails to consider the interrelation among various tasks. In this paper, we focus on the common realistic scenario with multiple collaborative tasks, where each task requires a group of users to perform collaboratively. Specifically, participants would consider task priority and the server would design suitable reward functions to allocate the total payment. Considering the information of users’ costs and the types of tasks, four incentive mechanisms are presented for various cases to the above problem, which are proved to have the Nash equilibrium solutions in all cases for maximizing the utility of the server. Moreover, online incentive mechanisms are further proposed for real time tasks. Through both rigid theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms have good performance and high computational efficiency in real world applications. 相似文献
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针对现有的多方人机交互存在分寸感把握较差、机器人回复自主性不强的问题,该文依据心理语言学关于对话心理理论,提出一种基于主从博弈的多方人机交互对话心理模型。该模型模拟了人与人交流时交互多方的心理过程,考虑到多方交互中主导者与从属者的交流特征,采取单主多从的博弈模型加以形式化表示,并使机器人扮演从属者角色,对多方主从博弈过程中从属关系带来的收益进行考量,将这一考量结果作为机器人回复的重要决策依据。实验结果表明,扮演从属者角色的机器人在与多方进行交互时,能准确把握对话分寸,在合适的时机下进行回复,进一步提升机器人回复的合理性与自主性。 相似文献
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This paper presents a novel spec- trum sharing design aiming at optimising the performance of a Multiuser Orthogonal Freq- uency-Division Multiplexing (MU-OFDM) Co- gnitive Radio Network (CRN) that consists of multiple secondary Transmitter-Receiver (Tx-Rx) pairs. For most MU-OFDM systems, the Exc- lusive Subchannel Assignment (ESA) is an efficient resource allocation method. Noneth- eless, it is inappropriate for the network consi- dered in this paper, because subchannels shar- ing among secondary Tx-Rx pairs can further improve the system performance. We investi- gate the Weighted Sum Rate (WSR) maximi- zation problem under the Shared Subchannel Assignment (SSA), where each subchannel is shared by multiple secondary Tx-Rx pairs. With Lagrangian duality technique, we decompose the original resource allocation problem into sev- eral sub-problems on each subchannel and pro- pose a duality-based suhchannel sharing ap- proach. For practical realisation in the cogni- tive systems without central control entity, a distributed duality-based WSR maximization scheme is presented. Simulation results mani- fest that the proposed scheme achieves sig- nificantly better performance than ESA duality scheme. 相似文献