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12
Delaybased network utility maximization
 in Proc. IEEE INFOCOM 2010
, 2010
"... Abstract—It is well known that maxweight policies based on a queue backlog index can be used to stabilize stochastic networks, and that similar stability results hold if a delay index is used. Using Lyapunov Optimization, we extend this analysis to design a utility maximizing algorithm that uses ex ..."
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Cited by 22 (1 self)
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Abstract—It is well known that maxweight policies based on a queue backlog index can be used to stabilize stochastic networks, and that similar stability results hold if a delay index is used. Using Lyapunov Optimization, we extend this analysis to design a utility maximizing algorithm that uses explicit delay information from the headofline packet at each user. The resulting policy is shown to ensure deterministic worstcase delay guarantees, and to yield a throughpututility that differs from the optimally fair value by an amount that is inversely proportional to the delay guarantee. Our results hold for a general class of 1hop networks, including packet switches and multiuser wireless systems with time varying reliability. I.
Opportunistic cooperation in cognitive femtocell networks
 the IEEE Journal on Selected Areas in Communications
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 14 (2 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Dynamic optimization and learning for renewal systems
 Proc. Asilomar Conf. on Signals, Systems, and Computers
, 2010
"... Abstract—This paper considers optimization of time averages in systems with variable length renewal frames. Applications include poweraware and profitaware scheduling in wireless networks, peertopeer networks, and transportation systems. Every frame, a new policy is implemented that affects the ..."
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Cited by 12 (6 self)
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Abstract—This paper considers optimization of time averages in systems with variable length renewal frames. Applications include poweraware and profitaware scheduling in wireless networks, peertopeer networks, and transportation systems. Every frame, a new policy is implemented that affects the frame size and that creates a vector of attributes. The policy can be a single decision in response to a random event observed on the frame, or a sequence of such decisions. The goal is to choose policies on each frame in order to maximize a time average of one attribute, subject to additional time average constraints on the others. Two algorithms are developed, both based on Lyapunov optimization concepts. The first makes decisions to minimize a “driftpluspenalty ” ratio over each frame. The second is similar but does not involve a ratio. For systems that make only a single decision on each frame, both algorithms are shown to learn efficient behavior without apriori statistical knowledge. The framework is also applicable to large classes of constrained Markov decision problems. Such problems are reduced to finding an approximate solution to a simpler unconstrained stochastic shortest path problem on each frame. Approximations for the simpler problem may still suffer from a curse of dimensionality for systems with large state space. However, our results are compatible with any approximation method, and demonstrate an explicit tradeoff between performance and convergence time. Index Terms—Stochastic processes, Markov decision problems I.
Exploiting Channel Memory for MultiUser Wireless Scheduling without Channel Measurement: Capacity Regions and Algorithms
 in IEEE Int. Symp. Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt
, 2010
"... Abstract—We study the fundamental network capacity of a multiuser wireless downlink under two assumptions: (1) Channels are not explicitly measured and thus instantaneous states are unknown, (2) Channels are modeled as ON/OFF Markov chains. This is an important network model to explore because cha ..."
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Cited by 10 (3 self)
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Abstract—We study the fundamental network capacity of a multiuser wireless downlink under two assumptions: (1) Channels are not explicitly measured and thus instantaneous states are unknown, (2) Channels are modeled as ON/OFF Markov chains. This is an important network model to explore because channel probing may be costly or infeasible in some contexts. In this case, we can use channel memory with ACK/NACK feedback from previous transmissions to improve network throughput. Computing in closed form the capacity region of this network is difficult because it involves solving a high dimension partially observed Markov decision problem. Instead, in this paper we construct an inner and outer bound on the capacity region, showing that the bound is tight when the number of users is large and the traffic is symmetric. For the case of heterogeneous traffic and any number of users, we propose a simple queuedependent policy that can stabilize the network with any data rates strictly within the inner capacity bound. The stability analysis uses a novel framebased Lyapunov drift argument. The outerbound analysis uses stochastic coupling and state aggregation to bound the performance of a restless bandit problem using a related multiarmed bandit system. Our results are useful in cognitive radio networks, opportunistic scheduling with delayed/uncertain channel state information, and restless bandit problems. Index Terms—stochastic network optimization, Markovian channels, delayed channel state information (CSI), partially observable Markov decision process (POMDP), cognitive radio, restless bandit, opportunistic spectrum access, queueing theory, Lyapunov analysis. I.
EnergyDelay Tradeoff and Dynamic Sleep Switching for BluetoothLike BodyArea Sensor Networks
"... Wireless technology enables novel approaches to healthcare, in particular the remote monitoring of vital signs and other parameters indicative of people’s health. This paper considers a system scenario relevant to such applications, where a smartphone acts as a datacollecting hub, gathering data f ..."
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Cited by 1 (0 self)
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Wireless technology enables novel approaches to healthcare, in particular the remote monitoring of vital signs and other parameters indicative of people’s health. This paper considers a system scenario relevant to such applications, where a smartphone acts as a datacollecting hub, gathering data from a number of wirelesscapable body sensors, and relaying them to a healthcare provider host through standard existing cellular networks. Delay of critical data and sensors ’ energy efficiency are both relevant and conflicting issues. Therefore, it is important to operate the wireless bodyarea sensor network at some desired point close to the optimal energydelay tradeoff curve. This tradeoff curve is a function of the employed physicallayer protocol: in particular, it depends on the multipleaccess scheme and on the coding and modulation schemes available. In this work, we consider a protocol closely inspired by the widelyused Bluetooth standard. First, we consider the calculation of the minimum energy function, i.e., the minimum sum energy per symbol that guarantees the stability of all transmission queues in the network. Then, we apply the general theory developed by Neely to develop a dynamic scheduling policy that approaches the optimal energydelay tradeoff for the network at hand. Finally, we examine the queue dynamics and propose a novel policy that adaptively switches between connected and disconnected (sleeping) modes. We demonstrate that the proposed policy can achieve significant gains in the realistic case where the control “NULL ” packets necessary to maintain the connection alive, have a nonzero energy cost, and the data arrival statistics corresponding to the sensed physical process are bursty.
Dynamic Server Allocation Over TimeVarying Channels With Switchover Delay
"... Abstract—We consider a dynamic server allocation problem over parallel queues with randomly varying connectivity and server switchover delay between the queues. At each time slot, the server decides either to stay with the current queue or switch to another queue based on the current connectivity an ..."
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Abstract—We consider a dynamic server allocation problem over parallel queues with randomly varying connectivity and server switchover delay between the queues. At each time slot, the server decides either to stay with the current queue or switch to another queue based on the current connectivity and the queue length information. Switchover delay occurs in many telecommunications applications and is a new modeling component of this problem that has not been previously addressed. We show that the simultaneous presence of randomly varying connectivity and switchover delay changes the system stability region and the structure of optimal policies. In the first part of this paper, we consider a system of two parallel queues, and develop a novel approach to explicitly characterize the stability region of the system using stateaction frequencies which are stationary solutions to a Markov decision process formulation. We then develop a framebased dynamic control (FBDC)
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.
1Delay and PowerOptimal Control in MultiClass Queueing Systems
"... Abstract—We consider optimizing average queueing delay and average power consumption in a nonpreemptive multiclass M/G/1 queue with dynamic power control that affects instantaneous service rates. Four problems are studied: (1) satisfying perclass average delay constraints; (2) minimizing a separa ..."
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Abstract—We consider optimizing average queueing delay and average power consumption in a nonpreemptive multiclass M/G/1 queue with dynamic power control that affects instantaneous service rates. Four problems are studied: (1) satisfying perclass average delay constraints; (2) minimizing a separable convex function of average delays subject to perclass delay constraints; (3) minimizing average power consumption subject to perclass delay constraints; (4) minimizing a separable convex function of average delays subject to an average power constraint. Combining an achievable region approach in queueing systems and the Lyapunov optimization theory suitable for optimizing dynamic systems with time average constraints, we propose a unified framework to solve the above problems. The solutions are variants of dynamic cµ rules, and implement weighted priority policies in every busy period, where weights are determined by past queueing delays in all job classes. Our solutions require limited statistical knowledge of arrivals and service times, and no statistical knowledge is needed in the first problem. Overall, we provide a new set of tools for stochastic optimization and control over multiclass queueing systems with time average constraints. I.