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.     

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。     

认知型飞蜂窝网络中的子信道分配与功率控制

为了解决宏蜂窝与飞蜂窝构成的两层异构网络上行干扰与资源分配问题,提出了一种在认知型飞蜂窝的双层异构网中结合子信道分配和功率控制进行资源分配的框架。通过对异构网中跨层干扰问题进行分析与建模,将求解最优子信道分配矩阵和用户发射功率矩阵作为干扰管理问题的解决方法。模型中认知型飞蜂窝网络子信道和飞蜂窝网络用户构成非合作博弈,双方利用效用函数最优值进行匹配,构成初始信道分配矩阵;再由接入控制器根据接入条件从初始信道分配矩阵中筛选用户,并优化接入用户的发射功率矩阵,得到最优子信道分配矩阵和功率矩阵。仿真结果表明,优化框架提高了双层异构网络中飞蜂窝网络用户的吞吐量和接入率,降低了异构网中跨层干扰。     

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.     

多主多从Stackelberg博弈下的边缘缓存资源分配算法

为鼓励视频服务提供商参与到缓存过程中,本文提出一种基于Stackelberg博弈的激励缓存资源分配算法。与传统激励缓存资源分配方案不同,本文考虑同时存在多个网络运营商和多个视频服务提供商,视频服务提供商从网络运营商处购买存储空间以缓存热门视频。针对该场景,本文将该激励缓存模型建模为多主多从Stackelberg博弈问题,分别构建主方和从方的效用函数,证明了在网络运营商价格确定的情况下,视频服务提供商之间的非合作博弈存在纳什均衡。文章利用分布式迭代算法对该博弈模型进行求解,获得了视频服务提供商的最优缓存策略和网络运营商的最优价格策略。仿真结果表明,本文提出的激励缓存机制可使视频服务提供商获得比其他缓存分配算法更高的单位成本收益。      

Utility optimization scheduling for multi-point video surveillance in ubiquitous network

Resource allocation is an important problem in ubiquitous network. Most of the existing resource allocation methods considering only wireless networks are not suitable for the ubiquitous network environment, and they will harm the interest of individual users with instable resource requirements. This paper considers the multi-point video surveillance scenarios in a complex network environment with both wired and wireless networks. We introduce the utility estimated by the total costs of an individual network user. The problem is studied through mathematical modeling and we propose an improved problem-specific branch-and-cut algorithm to solve it. The algorithm follows the divide-and-conquer principle and fully considers the duality feature of network selection. The experiment is conducted by simulation through C and Lingo. And it shows that compared with a centralized random allocation scheme and a cost greed allocation scheme, the proposed scheme has better per- formance of reducing the total costs by 13.0% and 30.6% respectively for the user.     

An econometric model for resource management in competitive wireless data networks

This article investigates the role and importance of the economic aspects that are vital to the success of wireless services deployment and provider selection by users in a competitive environment. We show how some of the econometric measures can meaningfully capture the user decisions/actions (e.g., churning) that can potentially be utilized by the providers in managing radio resources (e.g., bandwidth) in wireless data networks. In particular, by modeling the interaction between a service provider and its customers (or users) as a non-cooperative game, we propose a novel cross-layer resource management framework for integrated admission and rate control in CDMA networks. Analytical and simulation results demonstrate how the proposed framework can help minimize customer churning and maximize revenue for the wireless operators, yet optimizing customer satisfaction by providing differentiated quality of service to different classes of users.     

基于平衡定价和成本梯度的科学工作流调度策略

 科学工作流调度在自治网格和竞争市场环境下变得极具挑战性.为了兼顾科学用户和服务提供者的需要,提出效用网格环境下市场驱动的科学工作流调度框架:使用平衡定价机制推导出多个资源约束情况下的服务价格,最大化服务提供者的利润和实现资源的最优分配;成本优化映射策略引入成本梯度因子作为服务选择的标准,提高调度算法的优化能力和优化速度.性能模拟表明该框架不仅实现了较高的资源节点收益和资源利用率,还可在保证用户QoS要求的前提下优化不同类型科学工作流的执行成本.     

ARC: an integrated admission and rate control framework for competitive wireless CDMA data networks using noncooperative games

The competition among wireless data service providers brings in an option for the unsatisfied customers to switch their providers, which is called churning. The implementation of Wireless Local Number Portability (WLNP) is expected to further increase the churn rate (the probability of users switching the provider). However, the existing resource management algorithms for wireless networks fail to fully capture the far-reaching impact of this unforeseen competitiveness. From this perspective, we first formulate noncooperative games between the service providers and the users. A user's decision to leave or join a provider is based on a finite set of strategies. A service provider can also construct its game strategy set so as to maximize their utility (revenue) considering the churn rate. Based on the game theoretic framework, we propose an integrated admission and rate control (ARC) framework for CDMA-based wireless data networks. The admission control is at the session (macro) level while the rate control is at the link layer packet (micro) level. Two admission control modes are considered - one-by-one mode and batch processing mode, in which multiple users are admitted at a time. We show that: 1) for the one-by-one mode, the Nash equilibrium using pure strategy can be established for both under-loaded and fully-loaded systems and 2) for batch processing mode, there is either an equilibrium in pure strategy or a dominant strategy exists for the service provider. Therefore, the providers have clearly defined admission criteria as outcome of the game. Users are categorized into multiple classes and offered differentiated services based on the price they pay and the service degradation they can tolerate. We show that the proposed ARC framework significantly increases the provider's revenue and also successfully offers differentiated QoS to the users.     

Responsive pricing modeled with Stackelberg game for next-generation networks

In this paper, we study the problem of resource allocation between users in next-generation networks. The starting assumption is that the service provider tries to come up with convincing offers of service level to subscribers, based on demand responsive pricing scheme, while trying to maximize its profit. We developed two algorithms with different usage-based pricing strategies. Both algorithms solve pricing optimization problem using Stackelberg game with service provider acting as a leader and users behaving as followers. We developed the bandwidth management server to perform automatic optimal bandwidth allocation to each user's session and maximize its expected utility while maximizing the overall service provider's revenue. For both algorithms, we also performed the procedure for optimization of the capacity offered to users.     

A hierarchical approach for resource allocation in hybrid cloud environments

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.     

时分波分无源光网络与云无线接入网联合架构中负载平衡的用户关联与资源分配策略

在时分波分无源光网络(TWDM-PON)与云无线接入网(C-RAN)的联合架构中,由于无线域的负载不均衡问题,限制了网络整体的传输效率。为了充分利用TWDM-PON与C-RAN联合架构的网络资源,并保证用户的服务质量(QoS),该文提出一种负载平衡的用户关联与资源分配算法(LBUARA)。首先根据不同用户的服务质量需求以及分布式无线射频头端(RRH)的负载对用户的影响,构建用户收益函数。进而,在保证用户服务质量的前提下,根据网络状态建立随机博弈模型,并基于多智能体Q学习提出负载均衡的用户关联和资源分配算法,从而获得最优的用户关联与资源分配方案。仿真结果表明,所提的用户关联和资源分配策略能够实现网络的负载均衡,保证用户的服务质量,并提高网络吞吐量。     

Auction-Based Resource Reservation in 2.5/3G Networks

We consider UMTS networks in which users request services other than telephony that last for long time intervals: e.g., video clips that last for several minutes. The duration of network time-slots over which resource units are allocated is much shorter. This complicates consistent reservation of resources over longer time scales, where consistent reservation is required to ensure that service quality is constant throughout the entire service session. In this paper, we define an auction-based mechanism for nearly consistent reservation of the resources of a UMTS (or GPRS) network by the users that value them the most, in order to satisfy the longer time scale requirements of their service sessions. Each of these sessions has a fixed target bit-rate. The mechanism is based on a series of Generalized Vickrey Auctions and a set of predefined user utility functions that we propose. Bidding is performed automatically on behalf of the users on the basis of each user's selection of one of these utility functions and his declaration of a total willingness to pay. We argue that under our mechanism the user does not have a clear incentive of not performing a truthful selection of a bidding function according to his own utility. The utility functions we define express appropriately the preferences of the users with respect to the resource allocation pattern in the cases where perfectly consistent allocation cannot be attained. We also provide a mapping of these functions to the UMTS service classes. The effectiveness of our resource reservation mechanism is demonstrated by means of experiments. It appears that most of the users either are served very satisfactorily or essentially are not served at all. The mechanism is implemented at the network base station, and is applicable in practical cases of networks with large numbers of users whose sessions last for many slots.     

效用max—min公平准则及其在ABR业务中的应用

本文提出一种新的基于效用函数的max-min(UMM）公平准则,它实现的是用户效用之间的公平分配。在ABR业务下,本文提出UMM公平性的另外两个等价定义,考察了用户的最小需求和最大需求。为了求解UMM公平分配,文中给出集中式的UMM公平分配算法及其数学证明。文章从保证分配效率的角度出发赋予峰值信元位率PCR以新的含义。UMM公平性不仅是对以往ABR业务中max-min公平性的概括,还具有很好的推广前景,特别适用于多应用类型的网络资源分配。     

一种动态频谱分配的多层博弈模型构建分析

采用随机几何和博弈论相结合的方法,研究了蜂窝网络中用户对于移动网络运营商(Wireless Service Provider, WSP)的选择与WSP频谱分配方案的制定之间的联系;构建了一个层次化的博弈框架来模拟用户和WSP之间的复杂交互,采用演化博弈模型来描述用户之间的竞争,用非合作博弈模型来描述WSP之间的竞争,用多领导者多跟随者的Stackelberg博弈模型来描述用户和运营商之间的循环依赖问题;分析了演化博弈模型中演化均衡的存在及其渐近稳定性,并在此基础上证明了非合作博弈模型中纳什均衡的存在。     

User QoE Influenced Spectrum Trade,Resource Allocation,and Network Selection

In future wireless networks, we envision more dynamic telecommunication paradigm, where the dynamics may be translated into dynamic service offerings and user profiles etc. We further expect that the wireless communication market will be influenced when the user-centric network selection vision is realized. By the user-centric network selection vision, we mean that users will be free to select any available network operator or service provider on short term contractual basis. This dictates that operators will compete for their share of a common user pool on much smaller time quanta when compared with the current long term user contacts with the operators. One intuitive strategy of operators will be to incentivize users by offering different QoS and service price offers. As the operators’ offers are influenced by their incurring costs. This necessitates to study the market behavior at different levels and investigate the operator and user behavior at these level. In this paper, we categorize and position the communication players and model the interaction between players at different levels. We introduce the learning aspects in the interaction and investigate the equilibrium strategies of involved stake-holders i.e., users and operators. We also model the utility functions of all the involved stake-holders. We also examine the risk-sensitive utility functions in order to cover both risk-seeking and risk-averse in the user QoEs. We implement the user-centric approach and compare it against our proposed network-centric resource utilization and call blocking.     

Max-utility subcarrier allocation for heterogeneous services in wireless OFDM system

In future wireless network, one user will require multiple homogeneous or heterogeneous services simultaneously. Then, the scheduling algorithm is not only responsible for assigning a resource block to different users but also sharing the assigned resource block among multiple services for one user. Most of the traditional scheduling algorithms are designed to serve one service per user, and cannot be applied directly to this scenario because of the fairness criterion. This article focuses on adaptive resource allocation for multiple services per user at the downlink of orthogonal frequency division multiplexing (OFDM) based system. This article addresses this integrative resource scheduling problem based on utility function. First, the optimal algorithm for dynamic subcarrier allocation and share is deduced for homogeneous best-effort service system. Then the algorithm is extended to heterogeneous services system by classifying the delay sensitive service according to the head-of-line packet delay. The design goal is to maximize aggregate utility function to exploit multiuser diversity gain to the greatest extent even as guaranteeing quality of service (QoS) for delay sensitive service.     

基于非合作博弈论的多小区OFDMA系统动态资源分配算法研究

该文采用非合作博弈论的方法研究了多小区OFDMA系统中的动态资源分配问题,首先将各基站的发射功率平均分配给各子载波,然后由所有小区在每个子载波上独立地进行资源分配博弈,给出了用户调度与功率分配联合博弈框架。为了进一步简化,将用户调度和资源分配分开完成,通过将信道增益引入到定价函数中,提出了一种新的定价机制,建立了用户确定时的非合作功率分配博弈模型,分析了其纳什均衡的存在性和唯一性,并设计了具体的博弈算法。仿真结果表明,所提算法在保证吞吐量性能的同时,进一步提升了系统的公平性。     

无线视频通信中带宽分配策略

本文针对无线网络中多用户传输视频时带宽分配问题,提出了一种基于微观经济学中供需平衡的带宽分配策略。该方法充分考虑了运营商的收益,在运营商和用户收益同时最大且供需平衡时,所求得的频谱价格即为运营商的出售价格。同时给出了针对不同用户等级有不同的用户优先级因子,据运营商提出的带宽价格,彼此相互博弈,最终使得自身效用最大。仿真结果表明:本文提出的博弈论方法存在纳什均衡解和均衡单价;用户等级因子能按照用户的不同等级来分配带宽;系统传输质量相比以往方法有所提高。      

Joint frequency and time resource partitioning for OFDM-based small cell networks

This paper investigates the problem of distributed joint channel and time slot selection for interference mitigation in the multiple microcells system, where each microcell user determines which channel it should occupy and when it should get access to the serving microcell base station. Considering the characteristics of local interference, a distributed inter-cell time–frequency resource scheduling scheme is proposed, where neighboring users can get access to the same channel at different time slots or different channels at the same time slot. Then, a game is formulated to model the problem, in which the utility function of a individual user is the number of neighboring users occupying the same channel at the same time slot. It is proved to be an exact potential game with at least one pure strategy Nash Equilibrium (NE) point. Moreover, in order to achieve global interference minimization via local information, we propose the Heterogeneous Independent Revision Process (HIRP) to obtain the NE, which allows all users to update their selections simultaneously. Finally, numerical results show that our proposed HIRP guarantees that the interference minimization game converges to the NE and the proposed scheme acquires better performance compared with other existing interference management scheme.