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Multichannel Scheduling and Its Connection to Queueing Network Control Problem
"... Abstract—We consider the multichannel scheduling problem where the data rate offered by a channel is heterogeneous across users and the transmission time of a packet is random. The objective is to schedule the transmissions of all users over multiple channels to maximize the average weighted through ..."
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Abstract—We consider the multichannel scheduling problem where the data rate offered by a channel is heterogeneous across users and the transmission time of a packet is random. The objective is to schedule the transmissions of all users over multiple channels to maximize the average weighted throughput of the network. Based on a Markov Decision Process formulation, we show that this problem is in general EXPhard. To obtain computable performance benchmarks, we consider preemptive policies as well as the saturation mode in which a simple polynomial time algorithm is shown to be optimal. Both lead to upper bounds for the original problem. Low complexity approximation methods are then developed to achieve efficient solutions. Furthermore by drawing a connection to the queueing network control problem which is known to be EXPcomplete, we obtain sufficient conditions under which the complexity of the queueing network control problem is reduced to polynomialtime. Index Terms—Multiclass scheduling, closed queueing networks, complexity, Markov decision process (MDP). I.
AN OPPORTUNISTIC SCHEDULER FOR DENSE WLANS
"... The demand for bandwidth by multimedia applications remains unabated. This is particular critical given the growing number of devices with WiFi capability, and the ubiquity of Wireless Local Area Networks (WLANs). These trends have spurred researchers to develop lowcost and backward compatible solu ..."
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The demand for bandwidth by multimedia applications remains unabated. This is particular critical given the growing number of devices with WiFi capability, and the ubiquity of Wireless Local Area Networks (WLANs). These trends have spurred researchers to develop lowcost and backward compatible solutions to increase the capacity of WLANs. One approach is to deploy additional Access Points (APs), and strategies to manage channel, user, and transmit power. As a result, stations are likely to be near one or more APs, and therefore are more likely to experience high data rates. In this paper, we take advantage of this fact to increase the capacity of a dense WLAN further by transmitting packets to a station via a neighboring AP if its associated AP is occupied by another station. Our approach is novel as prior works have not exploited the density and diversity of APs when scheduling downstream traffic. From extensive simulation studies, we show its viability in varying traffic scenarios, and in particular, WLANs with a high number of APs.s.
1Throughput Optimal Scheduling over TimeVarying Channels in the presence of HeavyTailed Traffic
"... Abstract—We study the problem of scheduling over timevarying links in a network that serves both heavytailed and lighttailed traffic. We consider a system consisting of two parallel queues, served by a single server. One of the queues receives heavytailed traffic (the “heavy queue”), and the oth ..."
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Abstract—We study the problem of scheduling over timevarying links in a network that serves both heavytailed and lighttailed traffic. We consider a system consisting of two parallel queues, served by a single server. One of the queues receives heavytailed traffic (the “heavy queue”), and the other receives lighttailed traffic (the “light queue”). The queues are connected to the server through timevarying ON/OFF links, which model fading wireless channels. We first show that the policy that gives complete priority to the lighttailed traffic guarantees the best possible tail behavior of both queue backlog distributions, whenever the queues are stable. However, the priority policy is not throughput maximizing, and can cause undesirable instability effects in the heavy queue. Next, we study the class of throughput optimal maxweight scheduling policies. We discover a threshold phenomenon, and show that the steadystate light queue backlog distribution is heavytailed for arrival rates above a threshold value, and lighttailed otherwise. We also obtain the exact ‘tail coefficient ’ of the light queue backlog distribution under maxweight scheduling. Finally, we study a logmaxweight (LMW) scheduling policy, which is throughput optimal, and ensures that the light queue backlog distribution is lighttailed.
MultiUser Scheduling in Wireless Networks with QoS Constraints
"... Abstract — We consider a cellular network consisting of a base station and N receivers. The channel states of the receivers are assumed to be identical and independent of each other. The goal is to compare the throughput of two different scheduling policies (a queuelengthbased policy and a greedy ..."
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Abstract — We consider a cellular network consisting of a base station and N receivers. The channel states of the receivers are assumed to be identical and independent of each other. The goal is to compare the throughput of two different scheduling policies (a queuelengthbased policy and a greedy scheduling policy) given an upper bound on the queue overflow probability. We consider a multistate channel model, where each channel is assumed to be in one of L states. Given an upper bound on the queue overflow probability, we obtain a lower bound on the throughput of the queuelengthbased policy. For sufficiently large N, the lower bound is shown to be tight, strictly increasing with N, and strictly larger than the throughput of the greedy policy. I.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE/ACM TRANSACTIONS ON NETWORKING 1 Nonconcave Utility Maximization in Locally Coupled Systems, With Applica
"... Abstract—Motivated by challenging resource allocation issues arising in largescale wireless and wireline communication networks, we study distributed network utility maximization problems with a mixture of concave (e.g., besteffort throughputs) and nonconcave (e.g., voice/video streaming rates) u ..."
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Abstract—Motivated by challenging resource allocation issues arising in largescale wireless and wireline communication networks, we study distributed network utility maximization problems with a mixture of concave (e.g., besteffort throughputs) and nonconcave (e.g., voice/video streaming rates) utilities. In the first part of the paper, we develop our methodological framework in the context of a locally coupled networked system, where nodes represent agents that control a discrete local state. Each node has a possibly nonconcave local objective function, which depends on the local state of the node and the local states of its neighbors. The goal is to maximize the sum of the local objective functions of all nodes. We devise an iterative randomized algorithm, whose convergence and optimality properties follow from the classical framework of Markov Random Fields and Gibbs Measures via a judiciously selected neighborhood structure. The proposed algorithm is distributed, asynchronous, requires limited computational effort per node/iteration, and yields provable convergence in the limit. In order to demonstrate the scope of the proposed methodological framework, in the second part of the paper we show how the method can be applied to two different problems for which no distributed algorithm with provable convergence and optimality properties is available. Specifically, we describe how the proposed methodology provides a distributed mechanism for solving nonconcave utility maximization problems: 1) arising in OFDMA cellular networks, through power allocation and user assignment; 2) arising in multihop wireline networks, through explicit rate allocation. Several numerical experiments are presented to illustrate the convergence speed and performance of the proposed method. Index Terms—Constrained Gibbs sampler, interacting particle systems, locally coupled systems, multihop wireline networks, nonconcave utility maximization, OFDMA cellular networks. I.
On the Singular Behavior of a Queueing System with Random Connectivity
"... Abstract — In this paper, we study the stationary dynamics of a processing system comprised of several parallel queues and a single server of constant rate. The connectivity of the server to each queue is randomly modulated, taking values 1 (connected) or 0 (severed). At any given time, only the cur ..."
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Abstract — In this paper, we study the stationary dynamics of a processing system comprised of several parallel queues and a single server of constant rate. The connectivity of the server to each queue is randomly modulated, taking values 1 (connected) or 0 (severed). At any given time, only the currently connected queues may receive service. A key issue is how to schedule the server on the connected queues in order to maximize the system throughput. We investigate the behavior of two dynamic schedules, when the loading of the system exceeds its capacity. It is shown that unlike many other queueing systems that exhibit a binary behaviorglobal stability or global instability the system under consideration exhibits a much richer behavior, with several partial stability modes. These modes are fully determined by the underlying traffic loading. The results are obtained under very general stationary ergodic traffic flows and connectivity modulation. I.
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, 2008
"... We consider opportunistic communication over multiple channels where the state (“good ” or “bad”) of each channel evolves as independent and identically distributed Markov processes. A user, with limited channel sensing and access capability, chooses one channel to sense and subsequently access (bas ..."
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We consider opportunistic communication over multiple channels where the state (“good ” or “bad”) of each channel evolves as independent and identically distributed Markov processes. A user, with limited channel sensing and access capability, chooses one channel to sense and subsequently access (based on the sensed channel state) in each time slot. A reward is obtained whenever the user senses and accesses a “good ” channel. The objective is to design an optimal channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast as a Partially Observable Markov Decision Process (POMDP) or a restless multiarmed bandit process, to which optimal solutions are often intractable. We show in this paper that a myopic policy that maximizes the immediate onestep reward is always optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to 2 or 3, while presenting a counterexample for the case of 4 channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resourceconstrained jamming and antijamming.
Dynamic Markov Decision Policies for Delay Constrained Wireless Scheduling
"... Abstract — We consider a onehop wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput utility (to within a desired proximity), stabilizes ..."
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Abstract — We consider a onehop wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput utility (to within a desired proximity), stabilizes all queues, and satisfies the delay constraints. The problem is solved by reducing the constrained optimization to a set of weighted stochastic shortest path problems, which act as natural generalizations of maxweight policies to Markov decision networks. We also present approximation results for the corresponding shortest path problems, and discuss the additional complexity and delay incurred as compared to systems without delay constraints. The solution technique is general and applies to other constrained stochastic decision problems. Index Terms — Queueing systems, Network analysis and control, Markov processes I.
Occupancy
"... distributions of homogeneous queueing systems under opportunistic scheduling ∗ ..."
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distributions of homogeneous queueing systems under opportunistic scheduling ∗
A Large Deviations Analysis of Scheduling in Wireless Networks
"... Abstract — We consider a cellular network consisting of a base station and N receivers. The channel to each receiver is assumed to be in one of two states (ON or OFF) and the channel states of the receivers are assumed to be independent of each other. The goal is to compare the throughput of two dif ..."
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Abstract — We consider a cellular network consisting of a base station and N receivers. The channel to each receiver is assumed to be in one of two states (ON or OFF) and the channel states of the receivers are assumed to be independent of each other. The goal is to compare the throughput of two different scheduling policies given an upper bound on the queue overflow probability or the delay violation probability. The two scheduling policies that we consider are: (i) a greedy scheduling policy which chooses to serve any of the channels in the ON state, and (ii) a queuelengthbased policy which serves the longest queue connected to an ON channel. We show that the total network throughput of the queuelengthbased policy is no less than that of the greedy policy for all N and is strictly larger than the throughput of the greedy policy for large N. Further, given an upper bound on the delay violation probability, we show that the throughput of the queuelengthbased policy is an increasing function of N while the throughput of the greedy policy eventually decreases with increasing N and goes to zero. Given an upper bound on the queue overflow probability, we show that the throughput of the queuelengthbased policy is a strictly increasing function of N while the throughput of the greedy policy eventually goes to a constant. I.