OFDMA认知无线电网络中面向功率控制的频谱定价与分配

针对OFDMA认知无线电网络,提出一种基于Stackelberg博弈的频谱定价和分配模型.对于次基站控制次网络传输功率来保护主网络通信的场景,主基站可通过该模型获得最优的频谱定价方案.从功率控制的角度,重新设计次用户的效用函数,运用Stackelberg博弈对单个主基站和多个次用户在频谱租赁市场中的交易行为进行建模.通过逆向归纳法,求解市场均衡下的最优频谱定价,使得主基站在考虑主网络QoS降级的同时获得最大收益.此外,对于主基站只能获取本地信息的情形,提出了基于动态Stackelberg博弈的分布式频谱定价和分配模型.仿真实验表明,该模型能够在控制次网络传输功率的基础上,提供最优频谱定价和频谱分配方案.     

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。     

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。     

基于博弈资源分配的认知异构网络干扰协调算法

基于underlay频谱共享模式的认知异构网络可有效缓解频谱资源短缺问题,但同时会加剧网络中的干扰。针对该问题,提出了一种基于非合作博弈模型的动态频谱分配和功率控制算法进行干扰协调。首先,考虑频谱共享造成的干扰问题,引入认知用户优先等级,将问题构建为联合动态频谱分配与功率控制的频谱定价博弈模型;其次,通过两阶段动态博弈得到纳什均衡解,实现认知网络层频谱资源合理分配和发射功率控制。仿真表明,所提算法能够实现不同优先级用户频谱资源的合理分配和认知基站发射功率控制,有效抑制认知异构网络的跨层干扰和层内干扰。     

On oligopoly spectrum allocation game in cognitive radio networks with capacity constraints

Dynamic spectrum sharing is a promising technology to improve spectrum utilization in future wireless networks. The flexible spectrum management provides new opportunities for licensed primary user and unlicensed secondary users to reallocate the spectrum resource efficiently. In this paper, we present an oligopoly pricing framework for dynamic spectrum allocation in which the primary users sell excessive spectrum to the secondary users for monetary return. We present two approaches, the strict constraints (type-I) and the QoS penalty (type-II), to model the realistic situation that the primary users have limited capacities. In the oligopoly model with strict constraints, we propose a low-complexity searching method to obtain the Nash Equilibrium and prove its uniqueness. When reduced to a duopoly game, we analytically show the interesting gaps in the leader–follower pricing strategy. In the QoS penalty based oligopoly model, a novel variable transformation method is developed to derive the unique Nash Equilibrium. When the market information is limited, we provide three myopically optimal algorithms “StrictBEST”, “StrictBR” and “QoSBEST” that enable price adjustment for duopoly primary users based on the Best Response Function (BRF) and the bounded rationality (BR) principles. Numerical results validate the effectiveness of our analysis and demonstrate the convergence of “StrictBEST” as well as “QoSBEST” to the Nash Equilibrium. For the “StrictBR” algorithm, we reveal the chaotic behaviors of dynamic price adaptation in response to the learning rates.     

A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework

Due to their inherent limitations in computational and battery power, storage and available bandwidth, mobile devices have not yet been widely integrated into grid computing platforms. However, millions of laptops, PDAs and other portable devices remain unused most of the time, and this huge repository of resources can be potentially utilized, leading to what is called a mobile grid environment. In this paper, we propose a game theoretic pricing strategy for efficient job allocation in mobile grids. By drawing upon the Nash bargaining solution, we show how to derive a unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing. In particular, we characterize a two-player, non-cooperative, alternating-offer bargaining game between the Wireless Access Point Server and the mobile devices to determine a fair pricing strategy which is then used to effectively allocate jobs to the mobile devices with a goal to maximize the revenue for the grid users. Simulation results show that the proposed job allocation strategy is comparable to other task allocation schemes in terms of the overall system response time.     

基于博弈论的动态频谱分配技术研究

提出了一种改进的动态频谱分配博弈模型,对现有的频谱定价函数进行改进,在授权用户对单位频谱价格满意的条件下,频谱价格与授权用户提供带宽数量和次用户的频谱需求数量有关。此外,在次用户的效用函数中,考虑了频谱置换参数,并分析了置换参数和信道质量对次用户动态博弈以及次用户达到纳什均衡的影响。最后,分别用静态博弈和动态博弈分析了次用户之间的竞争频谱行为,并通过仿真验证,次用户的策略最终可以收敛到纳什均衡。     

A Scalable Pricing Model for Bandwidth Allocation

In this paper, we present a scalable pricing model for dynamic bandwidth allocation. Using simulation we demonstrate that service providers can achieve increased revenue as well as better resource utilization without compromising user specified levels of Quality of Service (QoS). Our model allows for increasing the revenue while maintaining an acceptable level of QoS, this is done by selecting an appropriate bandwidth allocation policy, to reduce the number of blocked users.     

Optimal resource allocation in multi-class networks with user-specified utility functions

In this work, we consider the problem of resource allocation in multi-class networks, where users specify the value they attach to obtaining different amounts of resource by means of a utility function. We develop a resource allocation scheme that maximizes the average aggregate utility per unit time. We formulate this resource allocation problem as a Markov decision process. We present numerical results that illustrate that our scheme performs better than the greedy resource allocation policy. We also discuss the implications of deliberate lying by users about their utility functions and develop a pricing scheme that prevents such lying.     

面向多租户网络资源分配的博弈优化策略

为了应对5G及未来网络中用户间差异化的服务需求,改善多租户网络切片资源利用率低和部署成本高的问题,提出一种基于多租户网络资源分配的博弈优化策略。在多租户网络中,网络切片租户（NSTs）租用基础设施提供商基站的无线频谱资源,将接入服务切片构建为网络切片即服务,为用户提供网络接入服务。将NSTs和用户的关系建模为一个多主多从的Stackelberg博弈,引入切片流行度和服务命中率指标,建立博弈双方的策略空间和收益函数,并证明NSTs的切片订购策略存在唯一的纳什均衡。通过逆向归纳法分析博弈模型,提出一种分布式迭代算法求得用户的最优吞吐量需求以及NSTs的最优切片定价。仿真结果表明,与传统考虑切片资源分配的优化策略对比,基于多租户网络资源分配的博弈优化策略能够有效提高资源利用率和用户满意度,并降低切片部署能耗,较好地实现频谱带宽资源的合理分配。     

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.     

A Game Theory-Based Pricing Strategy to Support Single/Multiclass Job Allocation Schemes for Bandwidth-Constrained Distributed Computing Systems

Today's distributed computing systems incorporate different types of nodes with varied bandwidth constraints which should be considered while designing cost-optimal job allocation schemes for better system performance. In this paper, we propose a fair pricing strategy for job allocation in bandwidth-constrained distributed systems. The strategy formulates an incomplete information, alternating-offers bargaining game on two variables, such as price per unit resource and percentage of bandwidth allocated, for both single and multiclass jobs at each node. We present a cost-optimal job allocation scheme for single-class jobs that involve communication delay and, hence, the link bandwidth. For fast and adaptive allocation of multiclass jobs, we describe three efficient heuristics and compare them under different network scenarios. The results show that the proposed algorithms are comparable to existing job allocation schemes in terms of the expected system response time over all jobs     

A Dantzig-Wolfe Decomposition Based Heuristic Scheme for Bi-level Dynamic Network Design Problem

We present a heuristic to solve the NP-hard bi-level network design problem (NDP). The heuristic is developed based on the Dantzig-Wolfe decomposition principle such that it iteratively solves a master problem and a pricing problem. The master problem is the budget allocation linear program solved by CPLEX to determine the budget allocation and construct a modified cell transmission network for the pricing problem. The pricing problem is the user-optimal dynamic traffic assignment (UODTA) solved by an existing combinatorial algorithm. To facilitate the decomposition principle, we propose a backward connectivity algorithm and complementary slackness procedures to efficiently approximate the required dual variables from the UODTA solution. The dual variables are then employed to augment a new column in the master program in each iteration. The iterative process repeats until a stopping criterion is met. Numerical experiments are conducted on two test networks. Encouraging results demonstrate the applicability of the heuristic scheme on solving large-scale NDP. Though a single destination problem is considered in this paper, the proposed scheme can be extended to solve multi-destination problems as well.     

SPRITE: a novel strategy-proof multi-unit double auction scheme for spectrum allocation in ubiquitous communications

With the increasing number of handheld and mobile devices, the demand for available spectrum resource is experiencing a rapid growth. Auction has been widely used for spectrum resource allocation in ubiquitous communications due to its fairness. However, many existing spectrum auction schemes inherently limit users’ ability to fully utilize the spectrum due to the assumption that the spectrum resource is single unit and indivisible. Besides, most of them fail to take into account of buyer/seller’s distinctive demands in auction and consider spectrum allocation as single-sided auction. In this paper, we consider the multi-unit double auction problem under the context that multiple buyers/sellers have different demands to buy/sell. Particularly, we present a novel strategy-proof multi-unit double auction scheme (SPRITE). SPRITE establishes a series of bid-related buyer group construction and winner determination strategies. It improves the spectrum reusability and achieves sound spectrum utilization, fairness, and essential economic properties. In the theoretical analysis, we have shown the correctness, effectiveness, and economic properties of SPRITE and prove that SPRITE is strategy-proof. In the evaluation study, we further show that SPRITE can achieve multi-unit spectrum auction with better auction efficiency compared with existing double auction mechanisms. To the best of our knowledge, SPRITE is the first multi-unit double auction approach that guarantees the competitive fairness among buyers while remaining strategy-proof for spectrum allocation. Furthermore, the multi-unit double auction mechanism proposed in this work helps increasing flexibility of the ubiquitous devices for spectrum usage.     

The Power of Fair Pricing Mechanisms

We explore the revenue capabilities of truthful, monotone (“fair”) allocation and pricing functions for resource-constrained auction mechanisms within a general framework that encompasses unlimited supply auctions, knapsack auctions, and auctions with general non-decreasing convex production cost functions. We study and compare the revenue obtainable in each fair pricing scheme to the profit obtained by the ideal omniscient multi-price auction. We show that for capacitated knapsack auctions, no constant pricing scheme can achieve any approximation to the optimal profit, but proportional pricing is as powerful as general monotone pricing. In addition, for auction settings with arbitrary bounded non-decreasing convex production cost functions, we present a proportional pricing mechanism which achieves a poly-logarithmic approximation. Unlike existing approaches, all of our mechanisms have fair (monotone) prices, and all of our competitive analysis is with respect to the optimal profit extraction.     

Grid Economics in Departmentalized Enterprises

The application of Grid technology is finally spreading from engineering and natural science-related industrial sectors to other industries with a high demand for computing applications. However, the diffusion of Grid technology within these sectors is often hindered by a lack of the incentive to share the computational resources across departments or branches even within the same enterprise. A promising way of overcoming these barriers is the introduction of a pricing mechanism for the use of Grid-based resources. This work introduces such a pricing approach to Grid computing and provides three simulation scenarios to illustrate the effectiveness of such an economized Grid solution. The simulation results indicate that the pooling of information technology resources can produce a reduction of 33% in cost compared to individual and dedicated servers. However, with a price-based allocation of computing resources, a further 10% of cost reduction can be achieved by introducing an auction mechanism. Therefore we claim that there is huge cost reduction potential in departmentalized enterprises beyond the savings that can be achieved by a utility-based allocation of computing resources, if economically measured allocation methods are combined with advanced refining and learning methods in the allocation process.     

基于博弈论的认知无线电频谱分配

为了解决频谱分配中的授权用户定价博弈问题,根据博弈论中的Bertrand均衡理论,提出了基于Bertrand模型的授权用户信道价格竞争的动态博弈算法。分析了稳定的纳什均衡解与速率调整参数的关系,用控制理论中阶跃函数研究价格无震荡博弈过程,提出了三值法确定阶跃响应参数。仿真结果表明,当数率调整参数在小于0.04时,可以获得稳定的信道价格;同时,验证了用阶跃函数分析无震荡博弈过程的可行性,方便授权用户快速实时定价,带来更大的经济效益。     

多媒体系统多资源分配中效率与公平性的权衡

针对多媒体系统多资源分配问题,提出了一种权衡效率与公平性的有效方法。分析 -公平性与系统效用及公平性指标的关系,提出利用 -公平性来实现两者之间的权衡,并验证其合理性。为了获得满足 -公平性的资源分配,提出了一种基于定价机制的多资源分配算法。仿真结果表明,所提出的多资源分配算法能够在较短时间内获得近似最优的资源分配;通过调整 值能够实现效率与公平性之间的权衡, 值越大,越强调公平性。     

Cooperative game-based distributed resource allocation in horizontal dynamic cloud federation platform

Distributed resource allocation is a very important and complex problem in emerging horizontal dynamic cloud federation (HDCF) platforms, where different cloud providers (CPs) collaborate dynamically to gain economies of scale and enlargements of their virtual machine (VM) infrastructure capabilities in order to meet consumer requirements. HDCF platforms differ from the existing vertical supply chain federation (VSCF) models in terms of establishing federation and dynamic pricing. There is a need to develop algorithms that can capture this complexity and easily solve distributed VM resource allocation problem in a HDCF platform. In this paper, we propose a cooperative game-theoretic solution that is mutually beneficial to the CPs. It is shown that in non-cooperative environment, the optimal aggregated benefit received by the CPs is not guaranteed. We study two utility maximizing cooperative resource allocation games in a HDCF environment. We use price-based resource allocation strategy and present both centralized and distributed algorithms to find optimal solutions to these games. Various simulations were carried out to verify the proposed algorithms. The simulation results demonstrate that the algorithms are effective, showing robust performance for resource allocation and requiring minimal computation time.     

Double-sided auctions applied to vertical handover for mobility management in wireless networks

In the next generation Internet, we have seen the convergence of multimedia services and Internet with the mobility of users. Vertical handover decision (VHD) algorithm are essential components of the mobility management architecture in mobile wireless networks. VHD algorithms help mobile users to choose the best mobile network to connect among available candidates. It also can help the network manager to optimize easily the limited resources shared among the network providers and the users. In this article, we formulate VHD algorithm as a resource allocation problem for down-link transmission power in multiple W-CDMA networks and show how combinatorial double-sided auctions can be applied to this specific problem. The proposed pricing schemes make use of the signal interference to noise ratio, achievable data rates, power allocation at mobile networks, and monetary cost as decision criteria and our model differentiate between new calls and on-going communications to take into account that the last category has somewhat more importance. Several combinatorial double-sided auction are proposed to maximize the social welfare and /or to provide incentives for mobile users and mobile operators to be truth-telling in terms of valuation or cost. Finally, the economic properties of the different proposed pricing schemes are also studied by means of simulations.