基于社会福利最大化的柔性业务资源分配研究

对经济学方法在无线资源管理中的应用进行了研究,考虑业务、用户、资源等多个域,将无线资源分配看作生产–消费模型,兼顾用户公平性原则,针对不同业务的QoS（quality of service）要求采用不同的资源分配方法,建立了基于社会福利最大化的资源分配模型。采用基于用户柔性业务的调度算法优化所提模型,综合考虑用户效用、网络效益以及运营商收益,实现了基于社会福利最大化的柔性业务资源分配。仿真结果验证了所提算法的优越性。     

一种支持云计算虚拟资源分配的可信多需求拍卖机制

使用拍卖方式来进行资源分配可以使得资源提供商获得更大的收益,是云计算领域近年来研究的重点。但现有研究多是基于非可信、单资源、单需求的前提。该文提出一种基于拍卖方式的云计算虚拟资源分配和定价机制(VRAP)。这种机制的特点在于,用户在一次拍卖中可以提出多个资源需求。证明了在这种机制下,资源提供商可以获得较以往拍卖机制更大的收益,同时能够保证用户出价是可信的。进而在具体资源分配问题上,提出一种单调的启发式算法能够在很短时间内计算出分配结果,通过资源稀有度概念设计了再分配策略,可以保证云资源提供商的收益极大化;在支付价格计算算法设计中,基于临界值理论计算支付价格,从而保证机制的公平可信。在社会福利、执行时间、资源利用率等多个方面对VRAP进行了测试分析,取得了很好的效果。     

Solving WDP in combinatorial double auction based on trading strategy

A solution for winner determination problem (WDP) in combinatorial double auction (CDA) was presented. Different from determining winner by solving optimization model which was non-deterministic polynomial (NP)-hard when the number of participants or resource types amounted to a large scale, the solution put the WDP goal–social welfare maximization into design of the trading strategy and determined winners by the process of resource allocation and pricing. In its corresponding allocation and pricing algorithm, sellers were allowed partially trading their bundles; the transaction path was approximated to Marshallian path; the utility for each winner was non-negative. Simulations show, compared to the WDP optimization model, the proposed algorithm can generate larger social welfare and it is particularly time efficient in conducting large-scale CDA.     

Resource planning and incentive engineering for a congested wireless access point: an integrated multiple time scale control mechanism

Wireless networks are playing an increasingly important role for global communications. Many resource allocation mechanisms have been proposed to efficiently utilize the limited radio resources in wireless networks to support a large number of mobile users with a diversity of applications. Among them, pricing frameworks that provide incentives to users to maximize their individual utility while optimizing allocation of network resources have attracted a lot of attention recently. Nevertheless, most of these pricing schemes require dynamic charging rates and may be too complex for wide acceptance by users, as most users would prefer relatively simple charging schemes. Moreover, use of a pricing framework to facilitate resource planning and future expansion at the service provider’s side has not yet been widely considered. In this paper, we propose Integrated Multiple Time Scale Control (IMTSC), a novel incentive engineering mechanism to facilitate resource allocation and network planning. Over different time scales, IMTSC combines the functions of network capacity planning, admission control for resource allocation, and tracking of users’ instantaneous traffic demands. The proposed mechanism is applied for access control at a congested access point in a wireless network. By decomposing the original problem into distributed optimization problems that are solved locally by the service provider through adjusting charging rate and remotely by individual users by appropriately changing her service requests, we show that maximization of user’s utility and increase of network efficiency can be simultaneously achieved. Results from extensive simulations demonstrate the effectiveness of the proposed IMTSC mechanism.     

基于组合多臂赌博机的移动群智感知用户招募算法

在移动群智感知任务分配中,数据平台不知道用户的感知质量或成本值的前提下,如何建立合适的用户招募机制是该文需要解决的关键问题,不仅需要在用户执行的过程学习其感知质量值,还要尽可能保证移动群智感知平台的高效性和利润最大化.因此该文提出基于组合多臂赌博机(CMAB)的移动群智感知用户招募算法来解决用户成本已知和未知的招募问题...     

全双工中继协作下的移动边缘计算系统能耗优化算法

为缓解终端设备处理大数据量、低时延业务的压力,该文提出一种基于全双工中继的移动边缘计算网络资源分配算法。首先,在满足计算任务时延约束、用户最大计算能力、用户和中继的最大发射功率约束条件下,考虑中继选择与子载波分配因子、用户任务卸载系数、用户与中继的传输功率的联合优化,建立了系统总能耗最小化问题。其次,利用交替迭代和变量代换的方法,将原非凸问题分解为两个凸优化子问题,并利用内点法和拉格朗日对偶原理分别进行求解。仿真结果表明,所提算法具有较低的能量消耗。     

上行多用户MIMO系统中的用户与天线联合选择方案

在多用户MIMO系统中,基站所能同时进行通信的用户数受到基站和用户端天线数的限制,随着用户数的增加,系统的性能反而会降低,因此,用户选择技术就成为一种改善系统性能的有效技术.在本文中,针对上行多用户MIMO系统提出了一种低复杂度的用户选择算法.为获得更大的系统性能,在用户选择的基础上,进一步提出了一种用户与天线联合选择算法.这两种算法在极大地简化计算复杂度的条件下,提供了与最优算法几乎相同的性能.     

Incentive mechanism based on auction model for mobile crowd sensing network

The selfishness and uncertainty of user behaviors in the mobile crowd sensing network make them unwilling to participate in sensing activities,which may result to a lower sensing task completion rate.To deal with these problems,an incentive mechanism based on auction model was proposed.In order to maximize the utility of each user,the proposed incentive method based on reverse auction (IMRA) leveraged a task-centric method to choose winners,and payed them according to a critical-price strategy.Furthermore,the proposed user-bidirectional interaction incentive mechanism (UBIM) helped drop-out users (buyers) to transfer their unfinished tasks to new users.Simulation results show that,compared with TRAC and IMC-SS,IMRA can achieve a better performance in terms of average user utility and tasks coverage ratio,and the task completion ratio can also be improved by UBIM.     

Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks

Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. The introduction of Millimeter Wave (mm-wave) communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved. However, interference among cellular and D2D links remains severe due to spectrum sharing. In this paper, to guarantee user Quality of Service (QoS) requirements and effectively manage the interference among users, we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands. The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs. To solve it, a distributed multiagent deep Q-network algorithm is proposed, where the reward function is redefined according to the optimization objective. In addition, to reduce signaling overhead, a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning. Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.     

移动性感知下基于负载均衡的任务迁移方案

针对移动边缘计算中用户移动性导致服务器间负载分布不均,用户服务质量(Quality of Service, QoS)下降的问题,提出了一种移动性感知下的分布式任务迁移方案。首先,以优化网络中性能最差的用户QoS为目标,建立了一个长期极大极小化公平性问题(Max Min Fairness, MMF),利用李雅普诺夫(Lyapunov)优化将原问题转化解耦。然后,将其建模为去中心化部分可观测马尔可夫决策过程(Decentralized Partially Observable Markov Decision Process, Dec-POMDP),提出一种基于多智能体柔性演员-评论家(Soft Actor-Critic, SAC)的分布式任务迁移算法,将奖励函数解耦为节点奖励和用户个体奖励,分别基于节点负载均衡度和用户QoS施加奖励。仿真结果表明,相比于现有任务迁移方案,所提算法能够在保证用户QoS的前提下降低任务迁移率,保证系统负载均衡。     

基于Stackelberg博弈的多任务协作频谱感知算法

随着移动设备的增多,认知无线电技术诞生,而频谱感知是认知无线电技术中的重要一环。本文将群智感知和频谱感知结合,提出了一种基于Stackelberg博弈的多任务协作频谱感知算法。该算法将融合中心(平台)与次用户分别建模为Stackelberg博弈领导者和从属者。在领导者博弈中,平台给次用户发布最优的报酬值以获得最佳的效用;在从属者博弈中,本文着重考虑了剩余能量对次用户的影响,次用户在平台给的报酬下改变感知时间以获得最优的效用。仿真结果表明,该算法可以提高融合中心对频谱的检测概率。      

一种Internet优化速率控制机制

该文提出了一种Internet优化速率控制机制Morcs,通过设计合理的链路拥塞费用函数,实现了社会福利最大化的系统优化目标。理论和仿真均证明了Morcs机制的收敛性和稳定性,仿真更进一步证明了Morcs比REM算法更稳定。本文接着进一步提出了Morcs机制的自适应实现方式aMorcs。aMorcs不需要网络链路的参与,完全在用户端实现,具有更好的可实现性,仿真表明aMorcs具有和Morcs相似的性能。     

A Game Theoretical Framework for Dynamic Pricing-Based Routing in Self-Organized MANETs

In self-organized mobile ad hoc networks (MANETs) where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other or honestly revealing its private information, cannot be directly assumed. The pricing mechanism is one way to provide incentives for the users to act cooperatively by awarding some payment for cooperative behaviors. In this paper, we consider efficient routing in self-organized MANETs and model it as multi-stage dynamic pricing games. A game theoretical framework for dynamic pricing-based routing in MANETs is proposed to maximize the sender/receiver?s payoff by considering the dynamic nature of MANETs. Meanwhile, the forwarding incentives of the relay nodes can also be maintained by optimally pricing their packet-forwarding services based on the auction rules and introducing the Cartel Maintenance enforcing mechanism. The simulation results illustrate that the proposed dynamic pricing-based routing approach has significant performance gains over the existing static pricing approaches.     

基于在线拍卖的网络切片资源分配算法

为满足未来移动通信网络中多样化的业务需求,为用户提供定制化服务的同时提升网络经济效益,该文提出一种基于在线拍卖的网络切片资源分配算法。根据业务类型将用户的服务请求转化为相应投标信息,以最大化拍卖参与者的社会福利为目标,将切片资源分配问题建模为基于多业务的在线赢家确定问题。结合资源分配与价格更新策略,实现基于在线拍卖的资源优化配置。仿真结果表明,该算法能够在满足用户业务需求的同时,提升网络经济效益。     

Fairness based resource allocation for multiuser MISO-OFDMA systems with beamforming

Resource allocation problem in multiuser multiple input single output-orthogonal frequency division multiple access (MISO-OFDMA) systems with downlink beamforming for frequency selective fading channels is studied. The article aims at maximizing system throughput with the constraints of total power and bit error rate (BER) while supporting fairness among users. The downlink proportional fairness (PF) scheduling problem is reformulated as a maximization of the sum of logarithmic user data rate. From necessary conditions on optimality obtained analytically by Karush-Kuhn-Tucker (KKT) condition, an efficient user selection and resource allocation algorithm is proposed. The computer simulations reveal that the proposed algorithm achieves tradeoff between system throughput and fairness among users.     

Downlink user selection and resource allocation for semi-elastic flows in an OFDM cell

We are concerned with user selection and resource allocation in wireless networks for semi-elastic applications such as video conferencing. While many packet scheduling algorithms have been proposed for elastic applications, and many user selection algorithms have been proposed for inelastic applications, little is known about optimal user selection and resource allocation for semi-elastic applications in wireless networks. We consider user selection and allocation of downlink transmission power and subcarriers in an orthogonal frequency division multiplexing cellular system. We pose a utility maximization problem, but find that direct solution is computationally intractable. We first propose a method that makes joint decisions about user selection and resource allocation by transforming the utility function into a concave function so that convex optimization techniques can be used, resulting in a complexity polynomial in the number of users with a bounded duality gap. This method can be implemented if the network communicates a shadow price for power to power allocation modules, which in turn communicate shadow prices for rate to individual users. We then propose a method that makes separate decisions about user selection and resource allocation, resulting in a complexity linear in the number of users.     

可信的反馈声誉保护协作频谱感知软判决算法

协作频谱感知中信任机制的引入,起到了抑制恶意用户频谱感知数据伪造( SSDF)攻击行为的作用。然而,数据融合中心不加区分地接收协作感知结束后的反馈信息,为恶意用户带来了实施“掺沙子”攻击的机会。恶意用户向数据融合中心反馈错误的主用户频谱状态,使信任机制不能得出准确的信任值。为此,提出了一种基于反馈声誉的信任机制,考虑反馈中的个体性特征,引入反馈声誉的思想来量化认知用户信任值。同时,将信任值量化结果用于权重经典软判决算法———序贯概率比检测( SPRT)算法,消除SSDF恶意用户参与软判决数据融合的影响,形成可信序贯概率比检测算法( FSPRT)。仿真结果表明FSPRT算法的性能优于传统SPRT算法,能有效降低网络信任值计算误差,并保持较好的感知性能。     

Cognitive Engine with Dynamic Priority Resource Allocation for Wireless Networks

A cognitive engine with dynamic priority resource allocation (CE-DPRA) is proposed for wireless networks by utilizing a maximum likelihood estimation method. The receiving signal strength (RSS) from an unlicensed cognitive radio (CR) user can be measured through estimating the unknown position of a licensed mobile user. The priority algorithm for access control enables the selection of a proper CR user waiting for transmission. Both data rate and spectral efficiency can be increased after adapting CE-DPRA. Also the power constraint method can avoid excessive interference caused by signal transmitting from CR users so as to improve the communication quality of mobile users. Simulation results show that the proposed CE-DPRA achieves the performance of high transmission data rate, less interference power, and low average outage probability.     

Fault-Tolerant Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks

In order to solve the uncertainty resulting from shadowing effect and resist the attack from malicious cognitive radio (CR) users, we propose a fault-tolerant cooperative spectrum sensing scheme for CR networks, where an energy detection-based local spectrum sensing is performed at each CR user, a coefficient is used to weight each CR user’s sensing result, a linear weighted fusion process is performed at the fusion center (FC) to combine received sensing results. For a fault-tolerant cooperative spectrum sensing scheme, the most important issue is to distinguish whether the CR user is reliable or not. In this paper, a reputation-based cooperative mechanism is presented to alleviate the influence of the unreliable sensing results from CR users suffering shadowing and the false sensing data from malicious CR users on the detection result at the FC. In proposed fault-tolerant cooperative scheme, each cooperative CR user has a reputation degree which is initialized and adjusted by the FC and used to weight the sensing result from the corresponding user in the fusion process at the FC. And then, two reputation degree adjusting methods are presented to manage the reputation degree of each CR user. Simulation results show that the proposed scheme can not only weaken the harmful influence caused by malicious CR users, but also alleviate the corrupted detection problem resulting from destructive channel condition between the primary transmitter and the CR user. Moreover, the detection performance of the fault-tolerant cooperative scheme, which has a feasible computational complexity and needs no instantaneous SNRs, is close to that of the optimal scheme.     

A hierarchical framework of dynamic relay selection for mobile users and profit maximization for service providers in wireless relay networks

Although extensive research has been carried out on the issue of how to optimally select relays in wireless relay networks, relay selection for mobile users is still a challenging problem because of the requirement that the dynamic selection should adapt to user mobility. Moreover, because the selected relays consume their energy on relaying data for the users, it is required that the users have to pay for this relay service. The price of selecting relays will affect the users' decisions. Assuming that different relays can belong to different service providers, we consider the situation that the service providers can strategically set the prices of their relays to maximize their profits. In this paper, we jointly study the dynamic relay selection for mobile users and profit maximization for service providers. Also, we design a Stackelberg‐game hierarchical framework to obtain the solution. At the lower level, we investigate the relay selection problem for the mobile users under given prices of selecting the relays. It is formulated as a Markov decision process problem with the objective to minimize the mobile user's long‐term average cost (which consists of the payment to the relay service and the cost due to packet loss), and solved by applying the linear programming technique. At the upper level, we study the game of setting relay prices for the service providers, with the knowledge that the mobile users will make relay selections based on their given prices. Nash equilibrium is obtained as the solution. Our results can help to provide a guidance for service providers to compete for providing relay services. Copyright © 2012 John Wiley & Sons, Ltd.