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.     

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.     

Stochastic Traffic Engineering in Multihop Cognitive Wireless Mesh Networks

In this work, the stochastic traffic engineering problem in multihop cognitive wireless mesh networks is addressed. The challenges induced by the random behaviors of the primary users are investigated in a stochastic network utility maximization framework. For the convex stochastic traffic engineering problem, we propose a fully distributed algorithmic solution which provably converges to the global optimum with probability one. We next extend our framework to the cognitive wireless mesh networks with nonconvex utility functions, where a decentralized algorithmic solution, based on learning automata techniques, is proposed. We show that the decentralized solution converges to the global optimum solution asymptotically.     

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.     

Competition in Parallel-Serial Networks

We study the efficiency implications of competition among profit-maximizing service providers in communication networks. Service providers set prices for transmission of flows through their (sub)network. The central question is whether the presence of prices will help or hinder network performance. We investigate this question by considering the difference between users' willingness to pay and delay costs as the efficiency metric. Previous work has demonstrated that in networks consisting of parallel links, efficiency losses from competition are bounded. Nevertheless, parallel-link networks are special, and in most networks, traffic has to simultaneously traverse links (or subnetworks) operated by independent service providers. The simplest network topology allowing this feature is the parallel-serial structure, which we study in this paper. In contrast to existing results, we show that in the presence of serial links, the efficiency loss relative to the social optimum can be arbitrarily large. The reason for this degradation of performance is the double marginalization problem, whereby each serial provider charges high prices not taking into account the effect of this strategy on the profits of other providers along the same path. Nevertheless, when there are no delay costs without transmission (i.e., latencies at zero are equal to zero), irrespective of the number of serial and parallel providers, the efficiency of strong oligopoly equilibria can be bounded by 1/2, where strong oligopoly equilibria are equilibria in which each provider plays a strict best response and all of the traffic is transmitted. However, even with strong oligopoly equilibria, inefficiency can be arbitrarily large when the assumption of no delay costs without transmission is relaxed.     

A scalable network resource allocation mechanism with bounded efficiency loss

The design of pricing mechanisms for network resource allocation has two important objectives: 1) a simple and scalable end-to-end implementation and 2) efficiency of the resulting equilibria. Both objectives are met by certain recently proposed mechanisms when users are price taking, but not when users can anticipate the effects of their actions on the resulting prices. In this paper, we partially close this gap, by demonstrating an alternative resource allocation mechanism which is scalable and guarantees a fully efficient allocation when users are price taking. In addition, when links have affine marginal cost, this mechanism has efficiency loss bounded by 1/3 when users are price anticipating. These results are derived by studying Cournot games, and in the process we derive the first nontrivial constant factor bounds on efficiency loss in these well-studied economic models.     

Proportional Fairness in Multi-Channel Multi-Rate Wireless Networks-Part I: The Case of Deterministic Channels with Application to AP Association Problem in Large-Scale WLAN

This is Part I of a two-part paper series that studies the use of the proportional fairness (PF) utility function as the basis for resource allocation and scheduling in multichannel multi-rate wireless networks. The contributions of Part I are threefold. (i) We present the fundamental properties and physical/economic interpretation of PF optimality. We show that PF leads to equal airtime allocation to users for the singlechannel case; and equal equivalent airtime allocation to users for the multi-channel case. In addition, we also establish the Pareto efficiency of joint-channel PF optimal solution (the formulation of interest to us in this paper), and its superiority over the individual-channel PF optimal solution in that the individual user throughputs of the former are all equal to or greater than the corresponding user throughputs of the latter. (ii) Second, we derive characteristics of joint-channel PF optimal solutions useful for the construction of PF-optimization algorithms. In particular, we show that a PF solution typically consists of many zero airtime assignments when the difference between the number of users U and the number of channels S, |U ? S|, is large. We present several PF-optimization algorithms, including a fast algorithm that is amenable to parallel implementation. (iii) Third, we study the use of PF utility for resource allocation in large-scale WiFi networks consisting of many adjacent wireless LANs. We find that the PF solution simultaneously achieves higher system throughput, better fairness, and lower outage probability with respect to the default solution given by today?s 802.11 commercial products. Part II of this paper series extends our investigation to the time-varying-channel case in which the data rates enjoyed by users over the channels vary dynamically over time.     

Application-Oriented Flow Control: Fundamentals, Algorithms and Fairness

This paper is concerned with flow control and resource allocation problems in computer networks in which real-time applications may have hard quality of service (QoS) requirements. Recent optimal flow control approaches are unable to deal with these problems since QoS utility functions generally do not satisfy the strict concavity condition in real-time applications. For elastic traffic, we show that bandwidth allocations using the existing optimal flow control strategy can be quite unfair. If we consider different QoS requirements among network users, it may be undesirable to allocate bandwidth simply according to the traditional max-min fairness or proportional fairness. Instead, a network should have the ability to allocate bandwidth resources to various users, addressing their real utility requirements. For these reasons, this paper proposes a new distributed flow control algorithm for multiservice networks, where the application's utility is only assumed to be continuously increasing over the available bandwidth. In this, we show that the algorithm converges, and that at convergence, the utility achieved by each application is well balanced in a proportionally (or max-min) fair manner     

Stackelberg Game Based Incentive Mechanisms for Multiple Collaborative Tasks in Mobile Crowdsourcing

In this paper, we tackle the problem of stimulating users to join mobile crowdsourcing applications with personal devices such as smartphones and tablets. Wireless personal networks facilitate to exploit the communication opportunity and makes diverse spare-resource of personal devices utilized. However, it is a challenge to motivate sufficient users to provide their resource of personal devices for achieving good quality of service. To address this problem, we propose an incentive framework based on Stackelberg game to model the interaction between the server and users. Traditional incentive mechanisms are applied for either single task or multiple dependent tasks, which fails to consider the interrelation among various tasks. In this paper, we focus on the common realistic scenario with multiple collaborative tasks, where each task requires a group of users to perform collaboratively. Specifically, participants would consider task priority and the server would design suitable reward functions to allocate the total payment. Considering the information of users’ costs and the types of tasks, four incentive mechanisms are presented for various cases to the above problem, which are proved to have the Nash equilibrium solutions in all cases for maximizing the utility of the server. Moreover, online incentive mechanisms are further proposed for real time tasks. Through both rigid theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms have good performance and high computational efficiency in real world applications.     

Sequential Bandwidth and Power Auctions for Distributed Spectrum Sharing

We study a sequential auction for sharing a wireless resource (bandwidth or power) among competing transmitters. The resource is assumed to be managed by a spectrum broker (auctioneer), who collects bids and allocates discrete units of the resource via a sequential second-price auction. It is well known that a second price auction for a single indivisible good has an efficient dominant strategy equilibrium; this is no longer the case when multiple units of a homogeneous good are sold in repeated iterations. For two users with full information, we show that such an auction has a unique equilibrium allocation. The worst-case efficiency of this allocation is characterized under the following cases: (i) both bidders have a concave valuation for the spectrum resource, and (ii) one bidder has a concave valuation and the other bidder has a convex valuation (e.g., for the other user?s power). Although the worst-case efficiency loss can be significant, numerical results are presented, which show that for randomly placed transmitter-receiver pairs with rate utility functions, the sequential second-price auction typically achieves the efficient allocation. For more than two users it is shown that this mechanism always has a pure strategy equilibrium, but in general there may be multiple equilibria. We give a constructive procedure for finding one equilibrium; numerical results show that when all users have concave valuations the efficiency loss decreases with an increase in the number of users.     

Bandwidth Allocation Strategies in Wide-Band Integrated Networks

A common digital transmission facility in a wide-band integrated service digital network (ISDN) provides shared access to a community of heterogeneous users. Traffic demands from these users vary in their arrival rate, their service time, and their bit rate. In order for this type of communication system to handle its traffic demands with high efficiency and flexibility, a close control of access to the shared bandwidth is required. We model the system by a general multiserver queueing system where customers demand service from a random number of servers. If no waiting is allowed, this queueing model is readily analyzed, and various server allocation strategies can be studied. If the various access requests are queued for service, then the system calls for efficient strategies for allocating servers to waiting customers. In this case, exact analysis of the underlying queueing model becomes quite difficult. For this case, we present some analytic and simulation results of the performance of the system under several server allocation policies.     

On the teletraffic capacity of CDMA cellular networks

The aim of this paper is to contribute to the understanding of the teletraffic behavior of code-division multiple-access (CDMA) cellular networks. In particular, we examine a technique to assess the reverse link traffic capacity and its sensitivity to various propagation and system parameters. We begin by discussing methods of characterizing interference from other users in the network. These methods are extremely important in the development of the traffic models. We begin with a review of several existing approaches to the problem of handling other-cell interference before presenting a novel characterization of the interference in the form of an analytic expression for the interference distribution function in the deterministic propagation environment. We then look at extending the capacity analyses that assume a fixed and equal number of users in every cell to handle the random nature of call arrivals and departures. The simplest way to do this is by modeling each cell of the network as an independent M/G/x∞ queue. This allows us to replace the deterministic number of users in each cell by an independent Poisson random variable for each cell. The resulting compound Poisson sums have some very nice properties that allow us to calculate an outage probability by analyzing a single random sum. This leads to a very efficient technique for assessing the reverse link traffic capacity of CDMA cellular networks     

Modeling Best-Effort and FEC Streaming of Scalable Video in Lossy Network Channels

Video applications that transport delay-sensitive multimedia over best-effort networks usually require special mechanisms that can overcome packet loss without using retransmission. In response to this demand, forward-error correction (FEC) is often used in streaming applications to protect video and audio data in lossy network paths; however, studies in the literature report conflicting results on the benefits of FEC over best-effort streaming. To address this uncertainty, we start with a baseline case that examines the impact of packet loss on scalable (FGS-like) video in best-effort networks and derive a closed-form expression for the loss penalty imposed on embedded coding schemes under several simple loss models. Through this analysis, we find that the utility (i.e., usefulness to the user) of unprotected video converges to zero as streaming rates become high. We then study FEC-protected video streaming, re-derive the same utility metric, and show that for all values of loss rate inclusion of FEC overhead substantially improves the utility of video compared to the best-effort case. We finish the paper by constructing a dynamic controller on the amount of FEC that maximizes the utility of scalable video and show that the resulting system achieves a significantly better PSNR quality than alternative fixed-overhead methods     

Double auction mechanisms for resource allocation in autonomous networks

Auction mechanisms are used for allocating a resource among multiple agents with the objective to maximize social welfare. What makes auctions attractive is that they are agnostic to utility functions of agents. Auctions involve a bidding method by agents-buyers, which is then mapped by a central controller to an allocation and a payment for each agent. In autonomic networks comprising self-interested nodes with different needs and utility functions, each entity possesses some resource and can engage in transactions with others to achieve its needs. In fact, efficient network operation relies on node synergy and multi-lateral resource trading. Nodes face the dilemma of devoting their limited resource to their own benefit versus acting altruistically and anticipating to be aided in the future. Wireless ad-hoc networks, peer-to-peer networks and disruption-tolerant networks are instances of autonomic networks where the challenges above arise and the traded resource is energy, bandwidth and storage space respectively. Clearly, the decentralized complex node interactions and the double node role as resource provider and consumer amidst resource constraints cannot be addressed by single-sided auctions and even more by mechanisms with a central controller. We introduce a double-sided auction market framework to address the challenges above. Each node announces one bid for buying and one for selling the resource.We prove that there exist bidding and charging strategies that maximize social welfare and we explicitly compute them. We generalize our result to a generic network objective. Nodes are induced to follow these strategies, otherwise they are isolated by the network. Furthermore, we propose a decentralized realization of the double-sided auction with lightweight network feedback. Finally, we introduce a pricing method which does not need a charging infrastructure. Simulation results verify the desirable properties of our approach.     

Auction-Based Effective Bandwidth Allocation Mechanism

Several auctions have been proposed and applied to perform contract negotiation and resource allocation in reservation-based networks. The methods proposed by these works perceive resources as single items with multiple units and place importance on a limited efficiency inside each node. However, as a user evaluates resources not individually, but rather as a whole set of required resources, the economical efficiency of the overall network cannot be achieved by these methods. To solve this problem, we propose a bandwidth allocation system using GVA (Generalised Vickrey Auction). Network resources, which are composed of many links at various bandwidths, are regarded by the proposed method as multiple items with multiple units. We describe how to apply GVA protocol to bandwidth allocations among multiple users. We investigate algorithmic and accounting problems inside multiple nodes using an end-to-end approach. We evaluate the proposed method's performance from various viewpoints: the utilisation of resources, profits of the telecommunications carriers, users' utility and users' satisfaction. We show that, by adopting GVA, the total utility of users can be maximised and the revenues of networks can also be improved.     

Up- and Downlink Admission/Congestion Control and Maximal Load in Large Homogeneous CDMA Networks

This paper proposes scalable admission and congestion control schemes that allow each base station to decide independently of the others what set of voice users to serve and/or what bit rates to offer to elastic traffic users competing for bandwidth. These algorithms are primarily meant for large CDMA networks with a random but homogeneous user distribution. They take into account in an exact way the influence of geometry on the combination of inter-cell and intra-cell interferences as well as the existence of maximal power constraints of the base stations and users. We also study the load allowed by these schemes when the size of the network tends to infinity and the mean bit rate offered to elastic traffic users. By load, we mean here the number of voice users that each base station can serve.     

Scheduling Algorithms and Throughput Maximization for the Downlink of Packet-Data Cellular Systems with Multiple Antennas at the Base Station

The capacity-achieving coding scheme for the multiple-input multiple-output (MIMO) broadcast channel is dirty-paper coding. With this type of transmission scheme the optimal number of active users that receive data and the optimal power allocation strategy are highly dependent on the structure of the channel matrix and on the total transmit power available. In the context of packet-data access with adaptive transmission where mobile users are equipped with a single receive antenna and the base station has multiple transmit antennas, we study the optimal number of active users and the optimal power allocation. In the particular case of two transmit antennas, we prove that the optimal number of active users can be a non-monotonic function of the total transmit power. Thus not only the number of users that should optimally be served simultaneously depends on the user channel vectors but also on the power available at the base station transmitter. The expected complexity of optimal scheduling algorithms is thus very high. Yet we then prove that at most as many users as the number of transmit antennas are allocated a large amount of power asymptotically in the high-power region in order to achieve the sum-capacity. Simulations confirm that constraining the number of active users to be no more than the number of transmit antennas incurs only a marginal loss in spectral efficiency. Based on these observations, we propose low-complexity scheduling algorithms with sub-optimal transmission schemes that can approach the sum-capacity of the MIMO broadcast channel by taking advantage of multiuser diversity. The suitability of known antenna selection algorithms is also demonstrated. We consider the cases of complete and partial channel knowledge at the transmitter. We provide simulation results to illustrate our conclusions.     

Inefficient Noncooperation in Networking Games of Common-Pool Resources

We study in this paper a noncooperative approach for sharing resources of a common pool among users, wherein each user strives to maximize its own utility. The optimality notion is then a Nash equilibrium. First, we present a general framework of systems wherein a Nash equilibrium is Pareto inefficient, which are similar to the `tragedy of the commons? in economics. As examples that fit in the above framework, we consider noncooperative flow-control problems in communication networks where each user decides its throughput to optimize its own utility. As such a utility, we first consider the power which is defined as the throughput divided by the expected end-to-end packet delay, and then consider another utility of additive costs. For both utilities, we establish the non-efficiency of the Nash equilibria.     

Stability and performance analysis of networks supporting elasticservices

We consider the stability and performance of a model for networks supporting services that adapt their transmission to the available bandwidth. Not unlike real networks, in our model, connection arrivals are stochastic, each has a random amount of data to send, and the number of ongoing connections in the system changes over time. Consequently, the bandwidth allocated to, or throughput achieved by, a given connection may change during its lifetime as feedback control mechanisms react to network loads. Ideally, if there were a fixed number of ongoing connections, such feedback mechanisms would reach an equilibrium bandwidth allocation typically characterized in terms of its “fairness” to users, e.g., max-min or proportionally fair. We prove the stability of such networks when the offered load on each link does not exceed its capacity. We use simulation to investigate performance, in terms of average connection delays, for various fairness criteria. Finally, we pose an architectural problem in TCP/IPs decoupling of the transport and network layer from the point of view of guaranteeing connection-level stability, which we claim may explain congestion phenomena on the Internet     

Joint Receiver and Transmitter Optimization for Energy-Efficient CDMA Communications

This paper focuses on the cross-layer issue of joint multiuser detection and resource allocation for energy efficiency in wireless code-division multiple-access (CDMA) networks. In particular, assuming that a linear multiuser detector is adopted in the uplink receiver, the situation considered is that in which each terminal is allowed to vary its transmit power, spreading code, and uplink receiver in order to maximize its own utility, which is defined as the ratio of data throughput to transmit power. Applying a game-theoretic formulation, a non-cooperative game for utility maximization is formulated, and it is proved that a unique Nash equilibrium exists, which, under certain conditions, is also Pareto-optimal. Theoretical results concerning the relationship between the problems of signal-to-interference-plus noise ratio (SINR) maximization and mean-square error (MSE) minimization are given, and, by applying the tools of large system analysis, a new distributed power control algorithm is implemented, based on very little prior information about the user of interest. The utility profile achieved by the active users in a large CDMA system is also computed, and, moreover, the centralized socially optimal solution is analyzed. Considerations concerning the extension of the proposed framework to a multi-cell scenario are also briefly detailed. Simulation results confirm that the proposed non-cooperative game largely outperforms competing alternatives, and that it exhibits negligible performance loss with respect to the socially optimal solution, and only in the case in which the number of users exceeds the processing gain. Finally, results also show an excellent agreement between the theoretical closed-form formulas based on large system analysis and the outcome of numerical experiments.