Utility‐optimal cross‐layer resource allocation in distributed wireless cooperative networks

In this paper, we propose a distributed cross‐layer resource allocation algorithm for wireless cooperative networks based on a network utility maximization framework. The algorithm provides solutions to relay selections, flow pass probabilities, transmit rate, and power levels jointly with optimal congestion control and power control through balancing link and physical layers such that the network‐wide utility is optimized. Via dual decomposition and subgradient method, we solve the utility‐optimal resource allocation problem by subproblems in different layers of the protocol stack. Furthermore, by introducing a concept of pseudochannel gain, we model both the primal direct logical link and its corresponding cooperative transmission link as a single virtual direct logical link to simplify our network utility framework. Eventually, the algorithm determines its primal resource allocation levels by employing reverse‐engineering of the pseudochannel gain model. Numerical experiments show that the convergence of the proposed algorithm can be obtained and the performance of the optimized network can be improved significantly. Copyright © 2012 John Wiley & Sons, Ltd.     

Rate control-based framework and algorithm for optimal provisioning

Ideally, networks should be designed to accommodate a variety of different traffic types, while at the same time maximizing its efficiency and utility. Network utility maximization (NUM) serves as an effective approach for solving the problem of network resource allocation (NRA) in network analysis and design. In existing literature, the NUM model has been used to achieve optimal network resource allocation such that the network utility is maximized. This is important, since network resources are at premium with the exponential increase in Internet traffic. However, most research work considering network resource allocation does not take into consideration key issues, such as routing and delay. A good routing policy is the key to efficient network utility, and without considering the delay requirements of the different traffic, the network will fail to meet the user’s quality of service (QoS) constraints. In this paper, we propose a new NUM framework that achieves improved network utility while taking into routing and delay requirements of the traffic. Then, we propose a decomposition technique-based algorithm, D-NUM, for solving this model efficiently. We compare our approach with existing approaches via simulations and show that our approach performs well.     

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.     

A globally stable adaptive congestion control scheme for Internet-style networks with delay

In this paper, we develop, analyze and implement a congestion control scheme in a noncooperative game framework, where each user's cost function is composed of a pricing function proportional to the queueing delay experienced by the user, and a fairly general utility function which captures the user demand for bandwidth. Using a network model based on fluid approximations and through a realistic modeling of queues, we establish the existence of a unique equilibrium as well as its global asymptotic stability for a general network topology, where boundary effects are also taken into account. We also provide sufficient conditions for system stability when there is a bottleneck link shared by multiple users experiencing nonnegligible communication delays. In addition, we study an adaptive pricing scheme using hybrid systems concepts. Based on these theoretical foundations, we implement a window-based, end-to-end congestion control scheme, and simulate it in ns-2 network simulator on various network topologies with sizable propagation delays.     

5G mobile terminals with advanced QoS-based user-centric aggregation (AQUA) for heterogeneous wireless and mobile networks

In this paper we present an advanced QoS provisioning module with vertical multi-homing framework for future fifth generation (5G) mobile terminals with radio network aggregation capability and traffic load sharing in heterogeneous mobile and wireless environments. The proposed 5G mobile terminal framework is leading to high performance utility networks with high QoS provisioning for any given multimedia service, higher bandwidth utilization and multi-RAT capabilities. It is using vertical multi-homing and virtual QoS routing algorithms within the mobile terminal, that is able to handle simultaneously multiple radio network connections via multiple wireless and mobile network interfaces. Our 5G proposal is user-centric, targeted to always-on connectivity, maximal network utilization, maximal throughput, seamless handovers and performances improvement by using vertical multi-homing, as well as session continuity. The performance of our proposed mobile terminal framework for 5G is evaluated using simulations and analysis with multimedia traffic in heterogeneous mobile and wireless scenarios with coexistence of multiple radio access technologies, such as 3G, 4G as well as future 5G radio access networks.     

Cross-layer network planning for multi-radio multi-channel cognitive wireless networks

We propose a general network planning framework for multi-radio multi-channel wireless networks. Under this framework, data routing, resource allocation, and scheduling are jointly designed to maximize a network utility function. We first treat such a cross-layer design problem with fixed radio distributions across the nodes and formulate it as a large-scale convex optimization problem. A primal-dual method together with the column-generation technique is proposed to efficiently solve this problem. We then consider the radio allocation problem, i.e., the optimal placement of radios within the network to maximize the network utility function. This problem is formulated as a large- scale combinatorial optimization problem. We derive the necessary conditions that the optimal solution should satisfy, and then develop a sequential optimization scheme to solve this problem. Simulation studies are carried out to assess the performance of the proposed cross-layer network planning framework. It is seen that the proposed approach can significantly enhance the overall network performance.     

无线传感器网络中基于效用模型的分布式功率控制机制

本文将效用模型引入无线传感器网络的功率控制设计中,提出了一种基于效用模型的分布式功率控制机制(简称UMDPC).该机制建立了网络中所有传感器节点的功率与效用模型的对应关系,将链路可靠性、网络能耗归纳到统一的网络效用优化框架中,并证明该效用优化问题是凸优化问题,构造基于对偶分解的分布式的优化算法,获得网络效用最大化条件下各节点的优化发射功率.最后,通过模拟实验对所提机制及其实现算法的性能进行比较和评价.实验结果表明,本文所提机制最大化了网络的效用,提高了网络的能量利用效率.     

A scalable low-overhead rate control algorithm for multirate multicast sessions

In multirate multicasting, different users (receivers) within the same multicast group can receive service at different rates, depending on the user requirements and the network congestion level. Compared with unirate multicasting, this provides more flexibility to the user and allows more efficient usage of the network resources. We address the rate control problem for multirate multicast sessions, with the objective of maximizing the total receiver utility. This aggregate utility maximization problem not only takes into account the heterogeneity in user requirements, but also provides a unified framework for diverse fairness objectives. We propose an algorithm for this problem and show, through analysis and simulation, that it converges to the optimal rates. In spite of the nonseparability of the problem, the solution that we develop is completely decentralized, scalable and does not require the network to know the receiver utilities. The algorithm requires very simple computations both for the user and the network, and also has a very low overhead of network congestion feedback.     

Mathematical Decomposition Techniques for Distributed Cross-Layer Optimization of Data Networks

Network performance can be increased if the traditionally separated network layers are jointly optimized. Recently, network utility maximization has emerged as a powerful framework for studying such cross-layer issues. In this paper, we review and explain three distinct techniques that can be used to engineer utility-maximizing protocols: primal, dual, and cross decomposition. The techniques suggest layered, but loosely coupled, network architectures and protocols where different resource allocation updates should be run at different time-scales. The decomposition methods are applied to the design of fully distributed protocols for two wireless network technologies: networks with orthogonal channels and network-wide resource constraints, as well as wireless networks where the physical layer uses spatial-reuse time-division multiple access. Numerical examples are included to demonstrate the power of the approach.     

Cross-Layer Optimization for Data Rate Utility Problem in UWB-based Ad Hoc Networks

There is growing interest in employing ultra-wideband (UWB) communication systems at the physical layer for multihop wireless networks. Recent efforts show that networking problems involving UWB systems should follow a cross-layer approach with consideration at multiple layers. Due to the nonlinear nature of the optimization problem, there are very limited theoretical results for this important problem. In this paper, we address this problem by considering a UWB-based ad hoc network. We study how to maximize capacity (in the form of a data rate utility) for a set of communication sessions. Via a cross-layer approach, we formulate this utility maximization problem into a nonlinear programming (NLP) problem, which takes into consideration routing, scheduling, and power control. We develop a solution procedure based on the so-called branch-and-bound framework. Within this framework, we employ a powerful optimization technique called reformulation linearization technique (RLT). We use numerical results to validate the efficacy of this solution procedure and offer insights on UWB-based ad hoc networks. This work provides a theoretical result for the achievable performance bound for a UWB-based ad hoc network.     

Balancing transport and physical Layers in wireless multihop networks: jointly optimal congestion control and power control

In a wireless network with multihop transmissions and interference-limited link rates, can we balance power control in the physical layer and congestion control in the transport layer to enhance the overall network performance while maintaining the architectural modularity between the layers? We answer this question by presenting a distributed power control algorithm that couples with existing transmission control protocols (TCPs) to increase end-to-end throughput and energy efficiency of the network. Under the rigorous framework of nonlinearly constrained utility maximization, we prove the convergence of this coupled algorithm to the global optimum of joint power control and congestion control, for both synchronized and asynchronous implementations. The rate of convergence is geometric and a desirable modularity between the transport and physical layers is maintained. In particular, when congestion control uses TCP Vegas, a simple utilization in the physical layer of the queueing delay information suffices to achieve the joint optimum. Analytic results and simulations illustrate other desirable properties of the proposed algorithm, including robustness to channel outage and to path loss estimation errors, and flexibility in trading off performance optimality for implementation simplicity. This work presents a step toward a systematic understanding of "layering" as "optimization decomposition," where the overall communication network is modeled by a generalized network utility maximization problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as the optimization variables coordinating the subproblems. In the case of the transport and physical layers, link congestion prices turn out to be the optimal "layering prices.".     

Opportunistic call admission control for wireless broadband cognitive networks

Wireless Broadband Cognitive Networks (WBCN) are new trend to better utilization of spectrum and resources. However, in multiservice WBCN networks, call admission control (CAC) is a challenging point to effectively control different traffic loads and prevent the network from being overloaded and thus provide promised quality of service. In this paper, we propose a CAC framework and formulate it as an optimization problem, where the demands of both WBCN service providers and cognitive subscribers are taken into account. To solve the optimization problem, we developed an opportunistic multivariate CAC algorithm based on a joint optimization of utility, weighted fairness, and greedy revenue algorithms. Extensive simulation results show that, the proposed call admission control framework can meet the expectations of both service providers and subscribers in wireless broadband cognitive networks.     

Pricing for enabling forwarding in self-configuring ad hoc networks

The assumption that all nodes cooperate to relay packets for each other may not be realistic for commercial wireless ad hoc networks. An autonomous (selfish) node in a wireless network has two disincentives for forwarding for others: energy expenditure (real cost) and possible delays for its own data (opportunity cost). We introduce a mechanism that "fosters cooperation through bribery" in the context of forwarding in ad hoc networks. Using a microeconomic framework based on game theory, we design and analyze a pricing algorithm that encourages forwarding among autonomous nodes by reimbursing forwarding. Taking a joint network-centric and user-centric approach, the revenue maximizing network and utility (measured in bits-per-Joule) maximizing nodes interact through prices for channel use, reimbursements for forwarding, transmitter power control, as well as forwarding and destination preferences. In a three-node (two-sources, one-access-point) network, the network converges to an architecture that induces forwarding only when the network geometries are such that forwarding is likely to increase individual benefits (network revenue and node utilities). For other geometries, the network converges to architectures that do not favor forwarding. We then generalize to a multinode network, where it is seen that the nodes' willingness to forward decrease for large ratios of the average internodal distance to the smallest distance between the access point and any source node. Pricing with reimbursement generally improves the network aggregate utility (or aggregate bits-per-Joule), as well as utilities and revenue compared with the corresponding pricing algorithm without reimbursement.     

Energy-efficient utility maximization for wireless networks with/without multipath routing

In wireless networks, end-to-end communication depends on link capacities which, in turn, are determined by transmit powers of interfering links. Optimal network performance and energy efficiency can be achieved by jointly optimizing congestion control and power control. In this paper, we study this joint optimization problem by formulating it into convex programming, i.e., we maximize a compound function which is a network utility function minus a factor, named tradeoff factor, of the associated power cost. We prove that this tradeoff factor is essential for good energy efficiency while maintaining the network throughput at a satisfactory level. The problem is solved by a distributed dual-decomposition based algorithm energy efficient jointly optimal congestion and power control (EJOC). EJOC tackles the power control problem in a recursive manner, operating as easily as the steepest descent method but converging much faster. This optimization framework is further extended to networks where each data source may have multiple alternative paths to its destination. Simulation results show that the proposed algorithm converges faster than other algorithm and is capable of significantly improving the energy efficiency of the network.     

A Game-Theoretic Framework for Medium Access Control

In this paper, we generalize the random access game model, and show that it provides a general gametheoretic framework for designing contention based medium access control. We extend the random access game model to the network with multiple contention measure signals, study the design of random access games, and analyze different distributed algorithms achieving their equilibria. As examples, a series of utility functions is proposed for games achieving the maximum throughput in a network of homogeneous nodes. In a network with n traffic classes, an N-signal game model is proposed which achieves the maximum throughput under the fairness constraint among different traffic classes. In addition, the convergence of different dynamic algorithms such as best response, gradient play and Jacobi play under propagation delay and estimation error is established. Simulation results show that game model based protocols can achieve superior performance over the standard IEEE 802.11 DCF, and comparable performance as existing protocols with the best performance in literature.     

A cross-layer framework for optimal delay-margin,network lifetime and utility tradeoff in wireless visual sensor networks

For wireless multimedia sensor networks a distributed cross-layer framework is proposed, which not only achieves an optimal tradeoff between network lifetime and its utility but also provides end-to-end delay-margin. The delay-margin, defined as the gap between maximum end-to-end delay threshold and the actual end-to-end delay incurred by the network, is exploited by the application layer to achieve any desired level of delay quality-of-service. For optimal performance tradeoff an appropriate objective function for delay-margin is required, which is obtained by employing sensitivity analysis. Sensitivity analysis is performed by incorporating delay-margin in the end-to-end delay constraints while penalizing its price in the objective function. For distributed realization of proposed cross-layer framework, the optimal tradeoff problem is decomposed into network lifetime, utility and delay-margin subproblems coupled through dual variables. The numerical results for performance evaluation show that compromising network utility does not guarantee both lifetime and delay-margin improvement, simultaneously, for the set of operating points. Performance evaluation results also reveal that the fairness among different delay-margins, corresponding to different source–destination node pairs, can be improved by relaxing the end-to-end delay threshold.     

Maximization of user satisfaction in OFDMA systems using utility‐based resource allocation

In order to keep and/or expand its share of the wireless communication market and decrease churn, it is important for network operators to keep their users (clients) satisfied. The problem to be solved is how to increase the number of satisfied non‐real time (NRT) and real time (RT) users in the downlink of the radio access network of an orthogonal frequency division multiple access system. In this context, the present work proposes a method to solve the referred problem using a unified radio resource allocation (RRA) framework based on utility theory. This unified RRA framework is particularized into two RRA policies that use sigmoidal utility functions based on throughput or delay and are suitable for NRT and RT services, respectively. It is demonstrated by means of system‐level simulations that a step‐shaped sigmoidal utility function combined with a channel‐aware opportunistic scheduling criterion is effective toward the objective of user satisfaction maximization. Copyright © 2014 John Wiley & Sons, Ltd.     

Cooperation and Fairness for Slotted Aloha

Wireless access based on slotted Aloha with selfish users may result in very inefficient use of the system resources. To impose cooperation and fairness in such systems, we propose an optimal pricing strategy, based on which the service provider can regulate the overall network behavior. As the users’ utility incorporates the price paid for using the spectrum, by striving to improve their own performance, the users act to optimize the overall network performance. Our analysis is based on a game theoretic framework, and we consider both the simple collision model for packet reception, as well as multipacket reception capabilities for the physical layer. The proposed pricing strategy enforces fairness under the constraint of an equal access probability.     

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.