text: We investigate the performance of First-In, First-Out (FIFO) queues over wireless networks. We characterize the stability region of a general scenario where an arbitrary number of FIFO queues, which are served by a wireless medium, are shared by an arbitrary number of flows. In general, the stability region of this system is non-convex. Thus, we develop a convex inner-bound on the stability region, which is provably tight in certain cases. The convexity of the inner bound allows us to develop a resource allocation scheme; dFC. Based on the structure of dFC, we develop a stochastic flow control and scheduling algorithm; qFC. We show that qFC achieves optimal operating point in the convex inner bound. Simulation results show that our algorithms significantly improve the throughput of wireless networks with FIFO queues, as compared to the well-known queue-based flow control and max-weight scheduling.
  @cite_7: We consider schemes for reuse-efficient packet access in wireless data networks. We show that computing the maximum ergodic packet arrival rate is NP-hard. We give an upper bound on the maximum ergodic throughput in terms of the eigenvalues of matrices related to the path-gain matrix. We present simple, practical heuristic algorithms which exhibit good throughput and packet delay and report on results of preliminary simulations. More sophisticated algorithms that yield optimal throughput are also presented. A recent result of McKeown, Anantharam and Walrand (1996) on scheduling of input-queued switches is obtained as a by-product.
  @cite_21: 
  @cite_3: A communication network with tine-varying topology is considered. The network consists of M receivers and N transmitters that, in principle, may access every receiver. An underlying network state process with Markovian statistics is considered that reflects the physical characteristics of the network affecting the link service capacity. The transmissions are scheduled dynamically, based on information about the link capacities and the backlog in the network. The region of achievable throughputs is characterized. A transmission scheduling policy is proposed that utilizes current topology state information and achieves all throughput vectors achievable by any anticipative policy. The changing topology model applies to networks of low-Earth orbit (LEO) satellites, meteor-burst communication networks, and networks with mobile users.
  @cite_6: A model for wireless networks with slotted-Aloha-type random access and with multihop flow routes is considered. The goal is to devise distributed algorithms for utility-optimal end-to-end throughput allocation and queueing stability. A class of queue back-pressure random access algorithms (QBRAs), in which actual queue lengths of the flows in each node's close neighborhood are used to determine the nodes' channel access probabilities, is studied. This is in contrast to some previously proposed algorithms, which are based on deterministic optimization formulations and are oblivious to actual queues. QBRA is also substantially different from the well-studied ldquoMaxWeightrdquo type scheduling algorithms, even though both use the concept of back-pressure. For the model with infinite backlog at each flow source, it is shown that QBRA, combined with simple congestion control local to each source, leads to optimal end-to-end throughput allocation within the network saturation throughput region achievable by random access, without end-to-end message passing. This scheme is generalized to the case with minimum flow rate constraints. For the model with stochastic exogenous arrivals, it is shown that QBRA ensures stability of the queues as long as nominal loads of the nodes are within the saturation throughput region. Simulation comparison of QBRA and the queue oblivious random-access algorithms, shows that QBRA reduces end-to-end delays.
  @cite_0: This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are provided to illustrate the cost of approaching optimality. This theory is also applicable to problems in operations research and economics, where energy-efficient and profit-maximizing decisions must be made without knowing the future. Topics in the text include the following: - Queue stability theory - Backpressure, max-weight, and virtual queue methods - Primal-dual methods for non-convex stochastic utility maximization - Universal scheduling theory for arbitrary sample paths - Approximate and randomized scheduling theory - Optimization of renewal systems and Markov decision systems Detailed examples and numerous problem set questions are provided to reinforce the main concepts. Table of Contents: Introduction Introduction to Queues Dynamic Scheduling Example Optimizing Time Averages Optimizing Functions of Time Averages Approximate Scheduling Optimization of Renewal Systems Conclusions
  @cite_23: We propose an efficient way to support quality of service of multiple real-time data users sharing a wireless channel. We show how scheduling algorithms exploiting asynchronous variations of channel quality can be used to maximize the channel capacity (i.e., maximize the number of users that can be supported with the desired QoS).
  @cite_2: We consider dynamic routing and power allocation for a wireless network with time-varying channels. The network consists of power constrained nodes that transmit over wireless links with adaptive transmission rates. Packets randomly enter the system at each node and wait in output queues to be transmitted through the network to their destinations. We establish the capacity region of all rate matrices ( spl lambda sub ij ) that the system can stably support-where spl lambda sub ij represents the rate of traffic originating at node i and destined for node j. A joint routing and power allocation policy is developed that stabilizes the system and provides bounded average delay guarantees whenever the input rates are within this capacity region. Such performance holds for general arrival and channel state processes, even if these processes are unknown to the network controller. We then apply this control algorithm to an ad hoc wireless network, where channel variations are due to user mobility. Centralized and decentralized implementations are compared, and the stability region of the decentralized algorithm is shown to contain that of the mobile relay strategy developed by Grossglauser and Tse (2002).
  @cite_16: The stability of a queueing network with interdependent servers is considered. The dependency among the servers is described by the definition of their subsets that can be activated simultaneously. Multihop radio networks provide a motivation for the consideration of this system. The problem of scheduling the server activation under the constraints imposed by the dependency among servers is studied. The performance criterion of a scheduling policy is its throughput that is characterized by its stability region, that is, the set of vectors of arrival and service rates for which the system is stable. A policy is obtained which is optimal in the sense that its stability region is a superset of the stability region of every other scheduling policy, and this stability region is characterized. The behavior of the network is studied for arrival rates that lie outside the stability region. Implications of the results in certain types of concurrent database and parallel processing systems are discussed. >
  @cite_10: We consider optimal control for general networks with both wireless and wireline components and time varying channels. A dynamic strategy is developed to support all traffic whenever possible, and to make optimally fair decisions about which data to serve when inputs exceed network capacity. The strategy is decoupled into separate algorithms for flow control, routing, and resource allocation, and allows each user to make decisions independent of the actions of others. The combined strategy is shown to yield data rates that are arbitrarily close to the optimal operating point achieved when all network controllers are coordinated and have perfect knowledge of future events. The cost of approaching this fair operating point is an end-to-end delay increase for data that is served by the network.
  @cite_20: In Stolyar (Queueing Systems 50 (2005) 401---457) a dynamic control strategy, called greedy primal-dual (GPD) algorithm, was introduced for the problem of maximizing queueing network utility subject to stability of the queues, and was proved to be (asymptotically) optimal. (The network utility is a concave function of the average rates at which the network generates several "commodities.") Underlying the control problem of Stolyar (Queueing Systems 50 (2005) 401---457) is a convex optimization problem subject to a set of linear constraints. In this paper we introduce a generalized GPD algorithm, which applies to the network control problem with additional convex (possibly non-linear) constraints on the average commodity rates. The underlying optimization problem in this case is a convex problem subject to convex constraints. We prove asymptotic optimality of the generalized GPD algorithm. We illustrate key features and applications of the algorithm on simple examples.
  @cite_17: 
summary: In this work, our goal is to understand FIFO queues in wireless networks and develop efficient flow control and scheduling policies for such a setup. In the seminal paper @cite_21 , the authors analyze FIFO queues in an input queued switch. They show that the use of FIFO queues in that context limits the throughput to approximately 58 Backpressure routing and scheduling framework has emer-ged from the pioneering work @cite_16 @cite_17 , which has generated a lot of research interest @cite_0 ; especially for wireless ad-hoc networks @cite_3 @cite_7 @cite_23 @cite_2 @cite_20 @cite_6 . Furthermore, it has been shown that backpressure can be combined with flow control to provide utility-optimal operation guarantee @cite_10 @cite_20 . Such previous work mainly considered per-flow queues. However, FIFO queueing structure, which is the focus of this paper, is not compatible with the per-flow queueing requirements of these routing and scheduling schemes.