改进的基于合作博弈的资源分配和接入控制策略

为了提高频谱共享的有效性和公平性,提出了一个基于合作博弈的认知网络频谱共享 模型和接入控制策略。博弈规则为在满足各用户最小收益的前提下,认知用户进行相互合作 ,并最大化总体收益;接入控制策略以满足服务质量需求为约束条件,并引入惩罚函数,迫 使认知用户按照其实际需求竞争频谱资源。仿真结果表明：提出的合作博弈模型和接入策略 提高了认知用户的总体收益和公平性,改善了系统中认知用户的满意度。     

OFDMA毫微微小区网络中基于效用公平的功率控制策略

毫微微小区(Femtocell)网络能够增强室内覆盖,提高系统容量,但是在频谱共享正交频分多址(OFDMA) Femtocell网络中,同频干扰严重限制了网络的性能。针对频谱共享Femtocell网络中的上行链路,基于网络效率和毫微微小区用户间的公平性,该文提出合作纳什议价功率控制博弈模型,该博弈模型不仅考虑了对宏基站的干扰,而且考虑了毫微微小区用户最小信干噪比(SINR)需求。根据该博弈模型,进一步分析了具有帕累托(Pareto)最优的Kalai-Smorodingsky(KS)议价解。仿真结果表明,该策略既能保证用户公平性、最小SINR需求,又能够有效提高网络频谱利用率。     

IEEE802．22 WRAN网络中基于拍卖的频谱租借算法

在IEEE802．22WRAN网络中,基站间的频谱共享是一个重要的研究方向。若每个WRAN基站伺机占用信道,将带来信道分配不均的问题。利用拍卖理论激励基站间平衡信道分配,提出一种动态的频谱租借算法。该算法将频谱共享情况建模为一个拍卖基站和多个竞拍基站的拍卖模型,同时考虑竞拍基站间的信道需求和拍卖基站的收益,建立频谱共享的数学优化模型。仿真结果表明,与已有的频谱拍卖机制相比,该算法能获得较高的拍卖收益。     

基于合作博弈的多信道认知无线网络中的频谱共享算法简

采用合作博弈对多信道认知无线网络中的频谱共享问题进行了建模分析,提出了次用户在各信道上的信干噪比乘积作为合作博弈的效用函数。次用户在各信道上保证对主用户的干扰小于一定门限的要求下,通过最大化各自效用函数的乘积来进行功率分配。由于最大化次用户效用函数的乘积问题是非凸的,通过变量替换将其转化为了一个等价的凸优化问题,利用该凸优化问题的对偶分解,提出了一种次用户间的频谱共享算法。仿真结果表明,所提算法在次用户和速率与公平性之间进行了有效折中。     

基于博弈论的D2D通信功率控制算法

针对多D2D通信用户共享蜂窝网络频谱资源中的干扰进行研究,为了减少D2D用户间干扰带来的影响,提出了一种改进的基于博弈论的D2D通信功率控制算法。算法中的博弈者是复用相同频谱资源的多D2D通信用户。建立了博弈模型,并在代价函数中引入代价因子,确定了效用函数,得到参与博弈的D2D用户的最优响应函数。文中对博弈均衡特性进行分析,证明了模型中的策略能够达到稳定状态。通过仿真实验验证了算法的有效性,并且与其他算法对比,证明使用此改进算法的D2D用户吞吐量更大,公平性更高。     

双向拍卖结合贝叶斯模型的认知无线电网络频谱共享方案

针对无线电网络中频谱资源有限且利用率较低的问题,提出了基于双向拍卖结合贝叶斯推理模型的频谱共享算法。首先,主用户和次用户自适应地选择拍卖价格分享频段;然后,玩家基于反馈学习过程捕捉调整价格的策略;最后,进行重复拍卖过程直到达成共识。该算法采用了贝叶斯推理技术,能够自适应地响应不断变化的系统环境和玩家数量,具有良好的可扩展性。仿真结果表明,该算法在PU受益、交易成功率、频谱利用率、网络吞吐量等方面显著优于其他几种较新的频谱共享算法。     

一种新的认知无线电非合作功率控制博弈算法

当认知无线电网络以"衬底式"(Underlay)的方式与主用户网络共享频谱时,需要对认知用户进行功率控制,以确保认知用户在不干扰主用户的前提下,公平地共享认知频谱资源。利用博弈分析的方法,设计了一个基于链路增益因子的代价函数,并据此提出了一种新的非合作功率控制博弈算法。仿真结果表明,该算法的均衡结果既改善了用户的帕累托(Pareto)性能,又提高了链路增益较差的用户的吞吐量,实现了网络资源的平等共享。     

感知无线电中频谱共享技术的研究

文章研究了感知无线电网络中联合频谱分配和调度的问题,给出了基于物理干扰模型的频谱分配算法。调度算法是对由频谱分配算法得到的一系列传输模式进行调度,从而实现频谱共享。本文基于提出的频谱分配算法通过仿真比较了三种调度算法,结果表明考虑了公平性的调度算法的网络吞吐量有稍微的降低（可达到最大吞吐量的96%）,但感知用户的整体需求满足度却得到了很大的提高,即达到了很好的公平性。     

认知无线电网络中基于随机学习博弈的信道分配与功率控制

传统的认知无线电频谱分配算法往往忽略节点的传输功率对网络干扰的影响,且存在节点间交互成本高的问题.为此,通过量化传输功率等级,以最大化弹性用户收益为目标,构建联合频谱分配与功率控制非合作博弈模型,证明了该博弈为严格潜在博弈且收敛到纳什均衡点.进一步,将随机学习理论引入博弈模型,提出了基于随机学习的策略选择算法,并给出了该算法收敛到纯策略纳什均衡点的充分条件及严格证明.仿真结果表明,所提算法在少量信息交互前提下能获得较高的传输速率,并提升用户满意度.     

分组多用户检测联盟模型与联盟形成算法研究

 以共享频谱的无线系统为背景,基于联盟博弈模型研究盲分组多用户检测性能与联盟结构的关系及最佳联盟形成算法.首先从系统指标角度提出了一种多用户检测可转移支付统计博弈模型;然后从用户偏好角度提出了一种不可转移支付模型;最后根据上述两种模型的研究结果,给出了最大化用户平均吞吐量的多用户检测联盟形成算法,并通过仿真验证了该算法的有效性.     

Pricing in combinatorial double auction-based grid allocation model

This article proposes a novel grid resource allocation model, in which the users and the grid service providers participate in the combinatorial double auction for the resource allocation. To obtain the detailed resource allocation status and the price information, a novel pricing algorithm is designed for the allocation model. Simulation results demonstrate that the proposed algorithm completes the resource allocation and pricing efficiently, and exhibits incentive compatible characteristic. Moreover,users with the higher average price and providers with the lower average price get compensation during the pricing process.     

基于组合双向拍卖的网格资源分配

 针对网格环境中资源分配的特点,提出新的基于组合双向拍卖的网格资源分配模型,通过网格用户和业务提供者的组合双向拍卖来实现资源的分配,并提出了网格资源定价算法,用以解决资源分配基础之上的计费问题.仿真结果表明,所提出的方案可以高效的完成资源的分配及定价,并且可以对平均报价较高的竞买方以及平均报价较低的竞卖方给予价格补偿.     

Comparison of Group-Buying Online Auction and Posted Pricing Mechanism in an Uncertain Market

Demand uncertainty is a key factor for the seller's decision making, especially in the e-business environment, for the website to sell products through the online auction. In this paper, two kinds of demand uncertainties are considered: the consumer regime uncertainty and the inherent randomness of the market environment. Then, how to use a novel business model and group-buying auction (GBA) is analyzed in such a market environment. Based on the comparison of the GBA and the posted price mechanism, some conditions that favor the GBA are provided.     

A provable secure sealed‐bid multi‐attribute auction scheme under semi‐honest model

Secure electronic auction protocol attracts increasing attention in the field of information security research. A variety of secure electronic auction protocols have achieved a lot. However, those studies mainly focus on price‐based auction model. There are few literatures involving security issues of multi‐attribute e‐auction. In recent years, the existing literatures mainly focus on the winner determination problem of multi‐attribute auction, but research of multi‐attribute e‐auction just begin from the perspective of information security. The proposal intends to start with the multi‐attribute e‐auction protocol, explores the different results in the multi‐attribute auction model because of different bid structures, focuses on qualitative attribute‐based winner determination auction model, proposes a new qualitative attribute‐based sealed‐bid multi‐attribute auction scheme under semi‐honest model for the first time. According to the security analysis and computation comparison, it provides strong bid privacy, public verifiability, and reasonable computational cost. Copyright © 2013 John Wiley & Sons, Ltd.     

TMAHS: a truthful multi-unit double auction framework for heterogeneous spectrum in secondary market

Auction was widely used to tackle spectrum allocation and sharing in the secondary market under the condition of spectrum scarcity. In real communication system, such as broadband communication, the utilization of spectrum resource is various because of different requirements and complex application scenarios. So, these schemes cannot be directly applied to the above wireless communication system. To solve this problem, a new model where sellers/buyers can sell/buy multi-unit for heterogeneous spectrum was proposed and a truthful multi-unit double auction framework was designed for heterogeneous spectrum trading. A valuation function is first applied to represent the buyer’s true valuation of the sub-band and reflect the buyer’s satisfaction degree and a novel concept termed ‘virtual player’ was introduced. Then the buyer group was constructed based on the conflict graph to reuse the same spectrum among interference-free buyers in both spatial and temporal domains. The winner determination strategy and algorithm of clearing price were designed elaborately. According to the theoretical analysis, the scheme can satisfy three critical economic properties: truthfulness, individual rationality, and budget balance. Finally, simulation results show that the proposed scheme can achieve better user satisfaction, auction efficiency and spectrum reuse rate for the real communication system. The proposed auction framework is practical and effective.     

A Parallel Repeated Auction for Spectrum Allocation in Distributed Cognitive Radio Networks

Cognitive radio (CR) is applied to solve spectrum scarcity. Although the auction theory and learning algorithm have been discussed in previous works, their combination is not yet researched in the distributed CR networks, where secondary users (SUs) can occupy several channels simultaneously by assuming that one channel can be accessed by at most one SU. A parallel repeated auction scheme is proposed to solve resource allocation in multi-user multi-channel distributed spectrum-overlay CR networks. A novel bid scheme in the light of the first-price sealed auction is designed to balance the system utility and allocation fairness. The proposed auction scheme can be developed based on a learning algorithm and be applied to the scenarios where the cooperation among SUs is unavailable. Under the assumption of limited entry budget, SUs can directly decide whether or not to participate in spectrum auction by comparing the possible bid with access threshold which can be applied into situations that SUs have different transmit power. Theoretical analysis and simulation results show that, compared with original myopic scheme and original genie-aided scheme, the proposed auction scheme can obtain a considerable improvement in efficiency and fairness, especially with adequate available resources.     

Auction in Multi-Path Multi-Hop Routing

We model the multi-path multi-hop routing in networks with selfish nodes as an auction and provide a novel solution from the game-theoretical perspective. We design a mechanism that results in Nash equilibria rather than the traditional strategyproofness, which alleviates the over-payment problem of the widely used VCG mechanism. Through theoretical analysis, the proposed protocol is shown to be effective     

一种安全的公共认证电子拍卖方案

利用Bit承诺,单向函数和数字签名算法,对一种安全电子拍卖模型进行改进,提出了一种安全、简单、高效的密封式电子拍卖方案.它可以在注册中心离线的情况下,最大程度地缩小拍卖行的权力实现公共认证.同时,该方案不仅能对除中标者外的其他所有投标者的投标价进行保密,而且能有效防止拍卖行不诚实的行为.     

基于非均匀谱压缩特征的模型补偿新算法

在信噪比依赖的非均匀谱压缩(SNSC)鲁棒语音特征提取技术和VTS算法的基础上,该文提出了一种新的MC-SNSC模型补偿算法。SNSC技术是一种根据人类听觉对声音强度-响度感知转化关系的谱幅度变化操作和噪声抑制技术。基于对数谱域的噪声以及SNSC特征提取对语音信号特征所产生的失配函数,推导出了MC-SNSC模型补偿算法。实验证明使用这一新算法,识别率比当前较理想的VTS和PMC算法有很明显的提升,算法的复杂度较VTS等算法仅有轻微的增加。     

A market-oriented dynamic collaborative cloud services platform

Currently, interoperability and scalability are two major challenging issues for cloud computing. Forming a dynamic collaboration (DC) platform among cloud providers (CPs) can help to better address these issues. A DC platform can facilitate expense reduction, avoiding adverse business impacts and offering collaborative or portable cloud services to consumers. However, there are two major challenges involved in this undertaking; one is to find an appropriate market model to enable a DC platform, and the other one is to minimize conflicts among CPs that may occur in a market-oriented DC platform. In this paper, we present a novel combinatorial auction (CA)-based cloud market (CACM) model that enables a DC platform in CPs. To minimize conflicts among CPs, a new auction policy is proposed that allows a CP to dynamically collaborate with suitable partner CPs to form groups and publishes their group bids as a single bid to compete in the auction. However, identifying a suitable combination of CP partners to form the group and reduce conflicts is a NP-hard problem. Hence, we propose a promising multi-objective (MO) optimization model for partner selection using individual information and past collaborative relationship information, which is seldom considered. A multi-objective genetic algorithm (MOGA) called MOGA-IC is proposed to solve the MO optimization problem. This algorithm is developed using two popular MOGAs, the non-dominated sorting genetic algorithm (NSGA-II) and the strength pareto evolutionary genetic algorithm (SPEA2). The experimental results show that MOGA-IC with NSGA-II outperformed the MOGA-IC with SPEA2 in identifying useful pareto-optimal solution sets. Other simulation experiments were conducted to verify the effectiveness of the MOGA-IC in terms of satisfactory partner selection and conflict minimization in the CACM model. In addition, the performance of the CACM model was compared to the existing CA model in terms of economic efficiency.