基于指数信任度函数的频谱感知改进算法

从多个用户合作感知的角度出发,针对目前的融合规则没有考虑感知结果的信任度,对现有的分布式感知算法进行改进,提出基于指数信任度的频谱感知算法;在认知无线电网络安全方面,将异常点感知引入到感知融合规则中,在指数信任度算法的基础上提出一种基于异常点的频谱感知算法,能抵御认知无线网络中出现故障的或者恶意认知用户;在提高感知速度和可靠性方面,结合指数信任度算法和异常点感知算法,将对感知信息的审核引入到频谱感知算法中,提出一种基于审核的频谱感知算法,该算法在提高感知速度的基础上能获得更好的感知性能。     

一种新型的认知无线电合作频谱感知算法*

为了提高认知无线电频谱感知性能,同时考虑到不同认知用户SU(Secondary User)具有不同的感知贡献和谈判力量,该文利用合作博弈理论提出了一种新的基于认知无线电的合作频谱感知非对称纳什谈判算法ANBS(Asymmetric Nash Bargaining Solution),该算法充分考虑到了每一个认知用户的感知可信度不尽相同的情况。仿真结果表明,与NBS(Nash Bargaining Solution)等算法相比,该算法不仅具有更强的合理性和可靠性,而且使系统整体感知性能得到了较大提高。     

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。     

基于认知用户移动性的协作频谱感知研究

协作频谱感知能在一定程度上消除多径和阴影的影响,然而现有协作频谱感知方案均假设各认知用户处于静止状态,未考虑认知用户在协作感知期间的移动对检测性能的影响。针对上述问题,在集中式频谱检测方法的基础上,提出一种认知用户移动状态下的协作检测模型,使主用户处于静止状态,认知用户以一定速度作匀速直线运动。仿真及分析结果表明,随着移动认知用户速度的增大,运用线性融合规则的移动认知用户与静止认知用户协作检测性能均逐渐降低,因此需选择移动速度较小的认知用户作为协作对象以提高频谱感知的整体检测性能。     

基于双门限的多天线分级融合协作频谱感知

针对当前协作频谱感知检测性能和协作开销的矛盾,提出了一种基于双门限能量检测的多天线认知用户分级融合协作频谱感知机制。通过引入多天线平方律合并技术,利用空间分集增益提高了认知节点的本地感知性能,并设置门限将本地感知结果划分为1 bit、2 bit两种,通过融合中心限制参与融合认知节点的最小个数,认知用户分级上传感知结果,融合中心根据上传结果分级融合感知信息。仿真结果表明,该机制较好地实现了感知性能和通信开销的折衷,同时对双门限区间的取值有较好的鲁棒性;当大部分感知数据落入双门限之间时,明显降低了有故障节点时感知失败的概率。     

认知无线电中一种n步串行信道感知策略

为了提高频谱感知的整体性能,基于链路层多认知用户集中式协作感知思想,提出一种带缓冲区的双周期n步串行协作感知机制.该机制利用多认知用户分时、分段协作提高频谱感知效率,建立频谱池缩短被中断用户切换延迟时间,使用离散马尔可夫模型对感知参数建模,通过求解最优搜索步长和双感知周期比,提高认知用户频谱感知性能和QoS.仿真实验结果显示,该算法在感知效率、被迫中断概率和中断时间方面均优于随机搜索和传统串行搜索策略.     

认知无线网络中基于博弈的频谱共享*

研究了认知无线网络环境中基于价格动态性的频谱共享,主要用户服务提供商以不同的频谱价格向认知用户服务提供商出售空闲频谱,认知用户根据获得的收益调整对频谱的需求。在全体服务提供商对频谱价格满意的基础上,为最大化认知用户的收益,提出了基于静态博弈和动态博弈的频谱共享方案。仿真结果表明,通过合理设置学习速率,动态博弈达到了接近静态博弈的性能,最大化认知用户的收益。     

认知无线电网络中宽频谱的信号分离算法

在认知无线电网络中,认知用户随机接入宽带频谱进行数据传输,但是这样很容易受到恶意用户的干扰,这些恶意用户随意地接入共享频带进行信号传输,这些信号会干扰主用户和认知用户。为此,提出了一种基于压缩感知的信号分离方法。该方法可以很好地从宽带信号中分离出恶意用户信号。算法主要采用以下三个步骤：（1）所有认知用户采用压缩感知技术从宽带频谱中恢复各信号;（2）认知用户将分离的信号发送到融合中心,融合中心通过小波边缘检测的方法确定频谱边缘,并按照边缘特性将频谱分成若干频段;（3）融合中心根据具体特征对每个子频段进行信号分离。分析和仿真结果表明,这种新的基于压缩感知的宽频带信号分离方法能很好地从宽带信号中将含有恶意用户干扰的混合信号分离出来。     

衰落信道下提高协作频谱感知能力的方法

由于协作频谱感知能够更准确地确定主用户的存在与否,因此,它被认为是认知无线电技术中的一个重要研究分支.在协作频谱感知中,各认知用户需要通过报告信道发送其本地判决结果(0或1)到融合中心(FC).然而,在实际中,报告信道通常受衰落的影响,这将降低频谱感知的检测性能.针对这个问题,首先,文中提出一种基于认知用户选择的协作频谱感知算法.该算法通过减小由衰落信道引入的误码概率来提高协作频谱感知的检测性能.其基本思想为:只有具有较好瞬时信道增益报告信道的认知用户才将其本地二进制判决结果发送到FC,否则,该认知用户不发送其本地检测结果到FC.其次,提出一种基于空时编码的协作频谱感知方法.通过在CR网络中,利用空间分集增益来降低由衰落型报告信道引入的误码概率,从而提高检测性能.最后,对提出的这两种协作频谱感知方法的检测性能进行理论分析研究,并对其进行数值仿真.仿真结果表明:相比于传统的协作频谱感知,本文提出的这两种协作频谱感知算法都能提高对主用户的检测性能.     

基于位置预测的认知车联网协作频谱感知算法

针对认知车联网中由地形起伏或密集城市结构而引起的频谱感知性能低、延时大等问题,提出了一种基于位置预测的协作频谱感知算法。首先,采用能量检测法进行本地频谱感知,通过将次用户接收到的信号能量大小和预先设定的阈值进行比较,初步判定频谱是否被主用户占用。然后,利用认知车联网中车辆位置预测技术,计算车辆位置和信道状态信息,并据此设置置信值,删除置信值低的次用户。最后,在融合中心采用加入置信值的似然比融合规则得到最终判决结果。仿真结果表明,与传统算法相比,所提算法能有效提高频谱感知性能并缩短感知时间,尤其适用于对实时性要求较高的车联网系统。     

On the computational complexity of qualitative coalitional games

We study coalitional games in which agents are each assumed to have a goal to be achieved, and where the characteristic property of a coalition is a set of choices, with each choice denoting a set of goals that would be achieved if the choice was made. Such qualitative coalitional games (qcgs) are a natural tool for modelling goal-oriented multiagent systems. After introducing and formally defining qcgs, we systematically formulate fourteen natural decision problems associated with them, and determine the computational complexity of these problems. For example, we formulate a notion of coalitional stability inspired by that of the core from conventional coalitional games, and prove that the problem of showing that the core of a qcg is non-empty is D^p₁-complete. (As an aside, we present what we believe is the first “natural” problem that is proven to be complete for D^p₂.) We conclude by discussing the relationship of our work to other research on coalitional reasoning in multiagent systems, and present some avenues for future research.     

A coalitional game approach for fractional cooperative caching in content-oriented networks

In content-oriented networks, popular contents are replicated at the intermediate nodes to enhance content delivery performance. Under cooperative caching, the caching nodes collaborate to leverage one another’s cache capability and to reduce the amount of traffic transferring inside the network. This study considers the cooperation among service providers (SPs). The transferable-payoff coalitional game model is applied for analysis. We investigate the stability of the grand coalition and show that the dual-based cost allocation is in the core. A linear program (LP) minimizing the network bandwidth-expense is used for the characteristic function of the game model. However, solving the LP is a challenge because of a large amount of contents in the network. The Dantzig–Wolfe decomposition approach is further applied to decompose the large-scale problem into many subproblems, which can be solved in parallel. The analysis provides not only a deeper insight into the cooperative cache among SPs but also content placement and distribution strategies as a solution to the LP.     

A linear approximation method for the Shapley value

The Shapley value is a key solution concept for coalitional games in general and voting games in particular. Its main advantage is that it provides a unique and fair solution, but its main drawback is the complexity of computing it (e.g., for voting games this complexity is #p-complete). However, given the importance of the Shapley value and voting games, a number of approximation methods have been developed to overcome this complexity. Among these, Owen's multi-linear extension method is the most time efficient, being linear in the number of players. Now, in addition to speed, the other key criterion for an approximation algorithm is its approximation error. On this dimension, the multi-linear extension method is less impressive. Against this background, this paper presents a new approximation algorithm, based on randomization, for computing the Shapley value of voting games. This method has time complexity linear in the number of players, but has an approximation error that is, on average, lower than Owen's. In addition to this comparative study, we empirically evaluate the error for our method and show how the different parameters of the voting game affect it. Specifically, we show the following effects. First, as the number of players in a voting game increases, the average percentage error decreases. Second, as the quota increases, the average percentage error decreases. Third, the error is different for players with different weights; players with weight closer to the mean weight have a lower error than those with weight further away. We then extend our approximation to the more general k-majority voting games and show that, for n players, the method has time complexity O(k²n) and the upper bound on its approximation error is .     

On the computational complexity of coalitional resource games

We study Coalitional Resource Games (crgs), a variation of Qualitative Coalitional Games (qcgs) in which each agent is endowed with a set of resources, and the ability of a coalition to bring about a set of goals depends on whether they are collectively endowed with the necessary resources. We investigate and classify the computational complexity of a number of natural decision problems for crgs, over and above those previously investigated for qcgs in general. For example, we show that the complexity of determining whether conflict is inevitable between two coalitions with respect to some stated resource bound (i.e., a limit value for every resource) is co-np-complete. We then investigate the relationship between crgs and qcgs, and in particular the extent to which it is possible to translate between the two models. We first characterise the complexity of determining equivalence between crgs and qcgs. We then show that it is always possible to translate any given crg into a succinct equivalent qcg, and that it is not always possible to translate a qcg into an equivalent crg; we establish some necessary and some sufficient conditions for a translation from qcgs to crgs to be possible, and show that even where an equivalent crg exists, it may have size exponential in the number of goals and agents of its source qcg.     

Reasoning about coalitional games

We develop, investigate, and compare two logic-based knowledge representation formalisms for reasoning about coalitional games. The main constructs of Coalitional Game Logic (cgl) are expressions for representing the ability of coalitions, which may be combined with expressions for representing the preferences that agents have over outcomes. Modal Coalitional Game Logic (mcgl) is a normal modal logic, in which the main construct is a modality for expressing the preferences of groups of agents. For both frameworks, we give complete axiomatisations, and show how they can be used to characterise solution concepts for coalitional games. We show that, while cgl is more expressive than mcgl, the former can only be used to reason about coalitional games with finitely many outcomes, while mcgl can be used to reason also about games with infinitely many outcomes, and is in addition more succinct. We characterise the computational complexity of satisfiability for cgl, and give a tableaux-based decision procedure.     

一种云间合作博弈资源提供方法

为了满足云资源消费者的需求,并有效扩展云资源的服务能力,设计基于云间合作博弈的资源联盟形成算法。以联盟总体利益最大化为目标,将多个云资源提供者间的合作行为建模为联盟博弈模型,从而得到最优联盟结构满足用户方的虚拟机实例请求;设计一种联盟的合并与分裂规则,使得最终联盟结构是稳定不变的;设计基于联盟成员贡献能力的标准化估计Banzhaf值法实现联盟总体利益的个体分割。实验结果表明,在不同虚拟机请求类型测试条件下,云联盟形成算法不仅可以确保更高的联盟总体利益,而且使利益分割更加公平,其算法执行效率也更高。     

Power in threshold network flow games

Preference aggregation is used in a variety of multiagent applications, and as a result, voting theory has become an important topic in multiagent system research. However, power indices (which reflect how much “real power” a voter has in a weighted voting system) have received relatively little attention, although they have long been studied in political science and economics. We consider a particular multiagent domain, a threshold network flow game. Agents control the edges of a graph; a coalition wins if it can send a flow that exceeds a given threshold from a source vertex to a target vertex. The relative power of each edge/agent reflects its significance in enabling such a flow, and in real-world networks could be used, for example, to allocate resources for maintaining parts of the network. We examine the computational complexity of calculating two prominent power indices, the Banzhaf index and the Shapley-Shubik index, in this network flow domain. We also consider the complexity of calculating the core in this domain. The core can be used to allocate, in a stable manner, the gains of the coalition that is established. We show that calculating the Shapley-Shubik index in this network flow domain is NP-hard, and that calculating the Banzhaf index is #P-complete. Despite these negative results, we show that for some restricted network flow domains there exists a polynomial algorithm for calculating agents’ Banzhaf power indices. We also show that computing the core in this game can be performed in polynomial time.     

Coalitional tracking

We propose a novel tracking method that uses a network of independent particle filter trackers whose interactions are modeled using coalitional game theory. Our tracking method is general, it maintains pixel level accuracy, and can negotiate surface deformations and occlusions. We tested our method on a substantial video set featuring non-trivial motion from over 40 objects in both the infrared and visual spectra. The coalitional tracker demonstrated fault tolerant behavior that exceeds by far the performance of single particle filter trackers. Our method represents a shift from the typical tracking paradigms and may find application in demanding imaging problems across the electromagnetic spectrum.     

Coalitional model predictive control of an irrigation canal

We present a hierarchical control scheme for large-scale systems whose components can exchange information through a data network. The main goal of the supervisory layer is to find the best compromise between control performance and communicational costs by actively modifying the network topology. The actions taken at the supervisory layer alter the control agents’ knowledge of the complete system, and the set of agents with which they can communicate. Each group of linked subsystems, or coalition, is independently controlled through a decentralized model predictive control (MPC) scheme, managed at the bottom layer. Hard constraints on the inputs are imposed, while soft constraints on the states are considered to avoid feasibility issues. The performance of the proposed control scheme is validated on a model of the Dez irrigation canal, implemented on the accurate simulator for water systems SOBEK. Finally, the results are compared with those obtained using a centralized MPC controller.     

Development of a module based service family design for mass customization of airline sector using the coalition game

The Airline industry faces various issues and difficulties related to its management, which have a peculiar and a complex solution. The objective of this paper is to introduce new and simplified methodologies for improving the airline sector by introducing cost efficient and relatively high payout services. In this paper, we have applied strategic sharing of modules in service family design using coalition game and Cournot game theories to model the related situations. Service family design is a cost-effective way for achieving mass customization by developing highly differentiated products from a common platform while laying emphasis on individual products. A coalitional game is applied which is used to model the potential module sharing and thus determines which modules that are being used in the platform proves to be the most beneficial ones. The empirical evidence suggests that convenience, safety and service quality also have a major influence on the choice of airline passengers. In a Cournot model, new services are predicted to be introduced by an airline and the effect of that service on its profits and thus response from other competitor airlines are carefully examined and result of this, decides the layout of the service in the functionality of the airline. This research can be efficiently used to assist the working of airline companies and help them to select services and have a better understanding of the role of quality of different services in the airline sector as a whole, thus giving them an edge over its competitors.