认知网络中基于竞价模型的切换频谱分配

通过对最新竞价算法的分析,针对频谱分配过程中频谱使用率低下的缺点,提出一种认知无线电中基于竞价机制的具有较高频谱使用率的频谱分配算法。引入干扰价格,通过设置干扰价格指数,降低系统干扰,保证主用户的服务质量。数值仿真结果表明,该算法通过多次分配有效地提高了频谱的利用率,增加了系统的吞吐量。     

WRAN中基于拍卖理论的频谱分配

本文将WRAN中电视广播台与认知用户之间的频谱分配过程建模为连续双向拍卖,通过该机制,有空闲电视频段的电视台可以将频谱出售给认知用户,由认知基站充当频谱经纪人.在频谱分配过程中,作为主用户的电视广播台和认知用户依据以往的历史交易信息建立自己的信心函数,结合自身的成本和预期收益,在最大化自己收益的前提下提出当前报价.仿真...     

基于NSGA-II算法的认知无线电频谱分配

认知无线电中,频谱拍卖是解决动态频谱分配的有效方法,其主要目的是最大化所有主用户的收益。然而,主用户间的收益是存在冲突的,给拍卖算法的优化带来了困难。为此,提出一种新的拍卖方案,来解决获胜者确定问题(Winner Determination Problem, WDP)。在该方案中,动态频谱拍卖问题被建模成多背包问题,并通过非支配排序遗传算法II (NSGA-II)得到最终的解决方案。最后,仿真实验结果表明,就解决WDP问题而言,与贪心算法相比,NSGA-II算法有更好的表现。     

基于博弈学习的动态频谱分配算法研究

为了解决认知无线网络中的动态频谱分配问题,提高频谱利用率和避免干扰,提出了一种基于学习的库诺(Cournot)博弈模型,将授权用户对于空闲频谱的分配行为模拟为动态的博弈过程,并赋予授权用户学习的能力,通过对博弈过程中博弈者行为的学习和总结形成新的博弈策略,而且还比较了将最优反应学习算法和模拟退火算法应用到自适应博弈学习中系统的性能和用户的收益。仿真结果表明,两种算法均能够使授权用户通过学习达到策略的均衡,而有限理性下基于模拟退火的自适应博弈学习算法的鲁棒性更强,收敛性更好,且能够使授权用户获得更高的收益。     

认知网络中基于竞价模型的频谱分配研究*

研究了动态频谱分配的现状,提出基于竞价模型的频谱分配算法设计应关注的三个主要问题。针对目前基于竞价的分配模型中没有保证授权用户的QoS的缺点,通过频谱质量分级和服务质量分级,设计了一个竞价频谱分配方案。在该方案中提出干扰价格的概念,并且把干扰价格作为竞价人竞标价格的重要组成部分。仿真结果表明,干扰价格能保证主用户的QoS。     

基于图着色理论的最大效用频谱分配算法

研究基于图着色理论的频谱分配算法,提出一种改进的最大效用频谱分配算法。该算法生成类似于ISAA算法的用户效用矩阵和相应的干扰矩阵,选择最大效用用户或干扰值为0的用户进行频谱分配。仿真结果表明,该算法能有效减少频谱分配的时间开销,提高频谱的使用效率,最大化认知无线电系统的总效用。     

基于随机游走算法的频谱组合拍卖机制

如何将频谱有效地分配给用户并提高提供商的收益是目前研究的热点。针对频谱组合拍卖中提供商收益低的问题，结合用户估值分布不对称的特点，设计了基于随机游走的频谱组合拍卖（RWSCA）机制，以最大化频谱提供商的收益。首先引入了虚拟估值的思想，用随机游走算法在参数空间搜索一组最优参数，并根据参数线性映射买家的估值；然后运行基于虚拟估值的VCG (Vickrey-Clarke-Groves)机制，从而确定赢得拍卖的用户并计算相应的支付金额。理论分析证明了所提机制具有激励相容和个体理性的性质。在频谱组合拍卖仿真实验中，相较于VCG机制，RWSCA机制至少提高16.84%以上提供商收益。     

认知无线网络中基于无休止多臂赌博机模型的多用户频谱接入机制

针对如何协调多个认知用户择机接入多段空闲频域信道的问题,提出了一种基于无休止多臂赌博机(RMAB)模型的动态频谱接入机制。首先,考虑到实际环境下认知用户的信道感知误差,推导出能有效处理感知误差的Whittle索引值算法,该算法通过历史经验积累给予每个信道一定的信任值,并综合考虑在当前信任值下选择每个信道的立即收益与未来收益的多少,选择出需要感知接入的信道;其次,对于多个认知用户接入相同信道时产生冲突的问题,提出了基于多标拍卖的协调机制,通过多标拍卖的方式处理认知用户之间的冲突。仿真结果表明,在相同的环境中,所提出的频谱接入机制与未处理误差的或者未采用多标拍卖的接入机制相比,认知用户获得的吞吐量更大。     

基于两步决策与ε-greedy探索的增强学习频谱分配算法

在认知无线网络中,认知基站需要进行频谱管理来提升非授权用户的服务质量。基站在寻找频谱空洞分配给非授权用户的过程中,需要做出最好的选择,但极可能是局部最优解,从而造成非授权用户频繁的频谱切换和吞吐率的下降。针对此问题,本文提出基于两步决策与探索的集中式增强学习频谱分配算法。通过设计新型状态动作集,认知基站进行信道分配的两步决策,并应用探索模式,解决认知基站在增强学习过程中探索环境和利用经验进行决策的平衡问题,防止决策的局部最优,提升频谱管理的性能。仿真结果表明,该算法在提升非授权用户吞吐率以及降低频谱切换方面明显优于现有的一些频谱分配策略。     

基于连续双向拍卖的频谱分配算法研究

根据频谱分配的现状,不同区域的部分机构拥有不同带宽的频谱并且为认知用户提供通信服务,因此,对多个频谱服务提供者和多认知用户共存环境下的频谱资源进行简单、高效地分配与管理并提高动态频谱访问的可靠性就成为了一个关键问题。对以上多对多分配问题进行简化,将多个频谱服务提供者和多认知用户之间的复杂关系用市场交易机制描述,提出了基于连续双向拍卖的频谱分配算法,针对不同任务的优先级别设计了灵活的交易指令。对该算法进行实验仿真,仿真结果表明,该算法时间复杂度较低,能有效地加快收敛速度。     

Adaptive Auction Framework for Spectrum Market in Cognitive Radio Networks

For the recent decade, cognitive radio networks have received much attention as an alternative to the traditional static spectrum allocation policy since the licensed spectrum channels are not being used efficiently. The most critical issue of the cognitive radio networks is how to distribute the idle spectrum channels to the secondary users opportunistically. The auction-based market is desirable for the trade of idle spectrum channels since the secondary users can purchase a channel in timely manner and the licensed primary users can earn the additional profit while not using the channels. Among the auction algorithms proposed for the spectrum market, we focus on the TASG framework, which consists of two nested auction algorithms, because it enables the group-buying of spectrum channels for the secondary users with limited budgets, and possesses many positive properties such as budget-balance, individual rationality and truthfulness. However, the TASG framework is not very attractive to the market participants since the seller earns the small revenue and the buyer has the low utility. In this paper, we propose a new auction framework for the spectrum markets, called aDaptive and Economically robust Auction-based Leasing (DEAL), that keeps all the benefits of TASG while improving the utility (or revenue) of the participants. To this end, we develop an enhanced inner-auction algorithm, called the Global Auction algorithm in our DEAL framework, and adapt the involved parameters dynamically based on the previous bids from the potential buyers. Simulation results demonstrate that our framework significantly outperforms the previous TASG.     

Towards real-time dynamic spectrum auctions

In this paper, we propose a low-complexity auction framework to distribute spectrum in real-time among a large number of wireless users with dynamic traffic. Our design consists of a compact and highly expressive bidding format, two pricing models to control tradeoffs between revenue and fairness, and fast auction clearing algorithms to achieve conflict-free spectrum allocations that maximize auction revenue. We develop analytical bounds on algorithm performance and complexity to verify the efficiency of the proposed approach. We also use both simulated and real deployment traces to evaluate the auction framework. We conclude that pricing models and bidding behaviors have significant impact on auction outcomes and spectrum utilization. Any efficient spectrum auction system must consider demand and spectrum availability in local regions to maximize system-wide revenue and spectrum utilization.     

Think,feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.     

基于拍卖的混合接入异构网络频谱共享

针对Overlay与Underlay模式的传统频谱共享系统网络效益较低的问题，提出了一种基于拍卖机制的频谱共享。当宏用户靠近飞蜂窝接入点时，飞蜂窝接入点与宏用户之间的链路性能比宏基站至宏用户的性能要好。此时宏基站希望将该用户卸载至飞蜂窝网络中，获得更好的通信服务质量的同时还能为宏基站节约能量。在保证飞蜂窝网络授权用户的QoS前提下，为激励网络内的混合接入，宏基站将自己的剩余频谱供飞蜂窝接入点使用，并对飞蜂窝接入点进行补偿。每个飞蜂窝接入点独立地决定自己的竞价，宏基站根据收集到的竞价决定最优分配策略。提出的基于拍卖机制的混合频谱共享能够使异构网络中的宏基站和飞蜂窝接入点的效用函数最大。并且补偿函数的引入能更好地激励飞蜂窝采用混合接入模式。     

An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds

Cloud computing is able to allocate different resources as virtual machines (VMs) to users, who need only pay for the amount of resources used. Two of the challenges in clouds are resource allocation and pricing in such a way to satisfy both cloud providers and users. Existing allocation and pricing mechanisms cannot guarantee increased profits due to various reasons. A better solution to increase the satisfaction of both parties, which is supported by economic theory, is the employment of auction-based allocation and pricing mechanisms. In these mechanisms, cloud resources and services are awarded based on the highest bids, while winners receive the quality of services expected. However, most existing auction-based mechanisms are inefficient and cannot be used in real clouds due to high computational or communication overhead, the bid function’s time complexity, and/or its inaccurate estimates. In the present paper, a lightweight mechanism is introduced which can be utilized in the real-world application of clouds. The currently proposed mechanism is a winner-bid auction game that seals users’ bids by a multi-criteria valuation-based bid function and sends them to the auctioneer. During scheduling, the auctioneer awards VMs exclusively to users with the highest bids. The presented approach is an online auction whose main aim is to increase the profits of the provider and user from different criteria. While determining the Nash equilibrium, the current study specifies the prices to be paid by users in various cases and proves the truthfulness of the proposed method. Finally, the effectiveness of the presented mechanism is examined through extensive experiments on different simulation scenarios and actual workload data.     

一种新型面向频谱高利用率的认知MAC协议

认知无线电是无线访问领域出现的新技术,目的在于大幅度提高无线频谱的使用.其基本思想是:次用户(非授权用户)在不干扰主用户(授权用户)的条件下允许使用授权频谱.提出一种新的基于独占模式的认知MAC协议.在该协议中,次用户被划分为若干不重叠的组,每个组使用特定的拍卖算法来对其需要租用的信道进行投标.实验表明,这种新协议能够最大化利用频谱资源,并且保证信道在组间分配的公平性和动态性.     

Consumer adoption of group-buying auctions: an experimental study

Internet-based group-buying auctions enable consumers to obtain volume discounts, but they face risk and trust issues that are not present in other e-retailing formats, which affects their adoption by consumers. Bidders experience uncertainty about the final auction price, and the risk of whether the auction will be completed. We evaluate textual comments and the number of bids made in an auction as drivers of a consumer’s perceived financial and psychological risks toward the group-buying auction mechanism and trust in the auction initiator. We use an Internet-based experimental test bed for online group-buying auctions and will report on one experiment that we conducted. Our results indicate that textual comments made by the participants about sellers in past auctions and existing bids affected a consumer’s perceived trust in the auction initiator and the financial risk of the mechanism. Positive textual comments and more bids appear to enhance perceived trust in the auction initiator and reduce financial risk, and other consumers are more willing to make bids as a result. Consumers continued to express concerns about the uncertainty of the final group-buying auction price though.