arXiv subjects

Demosthenis Teneketzis

Publications and source records attributed to Demosthenis Teneketzis.

33 records · Page 2Linked to original sources

arXiv2013

On The Optimality of Myopic Sensing in Multi-State Channels

We consider the channel sensing problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and resource constrained jamming. The communication system consists of N channels. Each channel is modeled as a multi-state Markov chain (M.C.). At each time instant a user selects one channel to sense and uses it to transmit information. A reward depending on the state of the selected channel is obtained for each transmission. The objective is to design a channel sensing policy that maximizes the expected total reward collected over a finite or infinite horizon. This problem can be viewed as an instance of a restless bandit problem, for which the form of optimal policies is unknown in general. We discover sets of conditions sufficient to guarantee the optimality of a myopic sensing policy; we show that under one particular set of conditions the myopic policy coincides with the Gittins index rule.

eess.SY↗

arXiv2012

Decentralized Stochastic Control with Partial History Sharing: A Common Information Approach

A general model of decentralized stochastic control called partial history sharing information structure is presented. In this model, at each step the controllers share part of their observation and control history with each other. This general model subsumes several existing models of information sharing as special cases. Based on the information commonly known to all the controllers, the decentralized problem is reformulated as an equivalent centralized problem from the perspective of a coordinator. The coordinator knows the common information and select prescriptions that map each controller's local information to its control actions. The optimal control problem at the coordinator is shown to be a partially observable Markov decision process (POMDP) which is solved using techniques from Markov decision theory. This approach provides (a) structural results for optimal strategies, and (b) a dynamic program for obtaining optimal strategies for all controllers in the original decentralized problem. Thus, this approach unifies the various ad-hoc approaches taken in the literature. In addition, the structural results on optimal control strategies obtained by the proposed approach cannot be obtained by the existing generic approach (the person-by-person approach) for obtaining structural results in decentralized problems; and the dynamic program obtained by the proposed approach is simpler than that obtained by the existing generic approach (the designer's approach) for obtaining dynamic programs in decentralized problems.

eess.SY↗

arXiv2012

Local public good provisioning in networks: A Nash implementation mechanism

In this paper we study resource allocation in decentralized information local public good networks. A network is a local public good network if each user's actions directly affect the utility of an arbitrary subset of network users. We consider networks where each user knows only that part of the network that either affects or is affected by it. Furthermore, each user's utility and action space are its private information, and each user is a self utility maximizer. This network model is motivated by several applications including wireless communications and online advertising. For this network model we formulate a decentralized resource allocation problem and develop a decentralized resource allocation mechanism (game form) that possesses the following properties: (i) All Nash equilibria of the game induced by the mechanism result in allocations that are optimal solutions of the corresponding centralized resource allocation problem (Nash implementation). (ii) All users voluntarily participate in the allocation process specified by the mechanism (individual rationality). (iii) The mechanism results in budget balance at all Nash equilibria and off equilibrium.

cs.GT↗

arXiv2012

Optimal Strategies for Communication and Remote Estimation with an Energy Harvesting Sensor

We consider a remote estimation problem with an energy harvesting sensor and a remote estimator. The sensor observes the state of a discrete-time source which may be a finite state Markov chain or a multi-dimensional linear Gaussian system. It harvests energy from its environment (say, for example, through a solar cell) and uses this energy for the purpose of communicating with the estimator. Due to the randomness of energy available for communication, the sensor may not be able to communicate all the time. The sensor may also want to save its energy for future communications. The estimator relies on messages communicated by the sensor to produce real-time estimates of the source state. We consider the problem of finding a communication scheduling strategy for the sensor and an estimation strategy for the estimator that jointly minimize an expected sum of communication and distortion costs over a finite time horizon. Our goal of joint optimization leads to a decentralized decision-making problem. By viewing the problem from the estimator's perspective, we obtain a dynamic programming characterization for the decentralized decision-making problem that involves optimization over functions. Under some symmetry assumptions on the source statistics and the distortion metric, we show that an optimal communication strategy is described by easily computable thresholds and that the optimal estimate is a simple function of the most recently received sensor observation.

eess.SY↗

arXiv2012

Games on Social Networks: On a Problem Posed by Goyal

Within the context of games on networks S. Goyal (Goya (2007), pg. 39) posed the following problem. Under any arbitrary but fixed topology, does there exist at least one pure Nash equilibrium that exhibits a positive relation between the cardinality of a player's set of neighbors and its utility payoff? In this paper we present a class of topologies/games in which pure Nash equilibria with the above property do not exist.

cs.GT↗

arXiv2012

Power Allocation and Spectrum Sharing in Multi-User, Multi-Channel Systems with Strategic Users

We consider the decentralized power allocation and spectrum sharing problem in multi-user, multi-channel systems with strategic users. We present a mechanism/game form that has the following desirable features. (1) It is individually rational. (2) It is budget balanced at every Nash equilibrium of the game induced by the game form as well as off equilibrium. (3) The allocation corresponding to every Nash equilibrium (NE) of the game induced by the mechanism is a Lindahl allocation, that is a weakly Pareto optimal allocation. Our proposed game form/mechanism achieves all the above desirable properties without any assumption about, concavity, differentiability, monotonicity, or quasi-linearity of the users' utility functions.

math.OC↗

arXiv2011

Revenue Maximization in Spectrum Auctions for Dynamic Spectrum Access

We investigate revenue maximization problems in auctions for dynamic spectrum access. We consider the frequency division and spread spectrum methods of dynamic spectrum sharing. In the frequency division method, a primary spectrum user allocates portions of spectrum to different secondary users. In the spread spectrum method, the primary user allocates transmission powers to each secondary user. In both cases, we assume that a secondary user's utility function is linear in the rate it can achieve by using the available spectrum/power. Assuming strategic users, we present incentive compatible, individually rational and revenue-maximizing mechanisms for the two scenarios.

math.OC↗

arXiv2011

An Efficient Tatonnement Process for the Public Good Problem

We present a decentralized message exchange process (tatonnement process) for determining the level at which a certain public good will be provided to a set of individuals who finance the cost of attaining that level. The message exchange process we propose requires minimal coordination overhead and converges to the optimal solution of the corresponding centralized problem.

math.OC↗

arXiv2010

Decentralized Detection with Signaling

We consider a sequential problem in decentralized detection. Two observers can make repeated noisy observations of a binary hypothesis on the state of the environment. At any time, any of the two observers can stop and send a final message to the other observer or it may continue to take more measurements. After an observer has sent its final message, it stops operating. The other observer is then faced with a different stopping problem. At each time instant, it can decide either to stop and declare a final decision on the hypothesis or take another measurement. At each time, the system incurs an operating cost depending on the number of observers that are active at that time. A terminal cost that measures the accuracy of the final decision is incurred at the end. We show that, unlike in other sequential detection problems, stopping rules characterized by two thresholds on an observer's posterior belief no longer guarantee optimality in this problem. Thus the potential for signaling among observers alters the nature of optimal policies. We obtain a new parametric characterization of optimal policies for this problem.

math.OC↗

arXiv2010

Optimal Control Strategies in Delayed Sharing Information Structures

The $n$-step delayed sharing information structure is investigated. This information structure comprises of $K$ controllers that share their information with a delay of $n$ time steps. This information structure is a link between the classical information structure, where information is shared perfectly between the controllers, and a non-classical information structure, where there is no "lateral" sharing of information among the controllers. Structural results for optimal control strategies for systems with such information structures are presented. A sequential methodology for finding the optimal strategies is also derived. The solution approach provides an insight for identifying structural results and sequential decomposition for general decentralized stochastic control problems.

cs.OH↗

arXiv2009

A Sequential Problem in Decentralized Detection with Communication

A sequential problem in decentralized detection is considered. Two observers can make repeated noisy observations of a binary hypothesis on the state of the environment. At any time, observer 1 can stop and send a final binary message to observer 2 or it may continue to take more measurements. Every time observer 1 postpones its final message to observer 2, it incurs a penalty. Observer 2's operation under two different scenarios is explored. In the first scenario, observer 2 waits to receive the final message from observer 1 and then starts taking measurements of its own. It is then faced with a stopping problem on whether to stop and declare a decision on the hypothesis or to continue taking measurements. In the second scenario, observer 2 starts taking measurements from the beginning. It is then faced with a different stopping problem. At any time, observer 2 can decide whether to stop and declare a decision on the hypothesis or to continue to take more measurements and wait for observer 1 to send its final message. Parametric characterization of optimal policies for the two observers are obtained under both scenarios. A sequential methodology for finding the optimal policies is presented. The parametric characterizations are then extended to problem with increased communication alphabet for the final message from observer 1 to observer 2; and to the case of multiple peripheral sensors that each send a single final message to a coordinating sensor who makes the final decision on the hypothesis.

math.OC↗

arXiv2009

On the Structure of Real-Time Encoders and Decoders in a Multi-Terminal Communication System

A real-time communication system with two encoders communicating with a single receiver over separate noisy channels is considered. The two encoders make distinct partial observations of a Markov source. Each encoder must encode its observations into a sequence of discrete symbols. The symbols are transmitted over noisy channels to a finite memory receiver that attempts to reconstruct some function of the state of the Markov source. Encoding and decoding must be done in real-time, that is, the distortion measure does not tolerate delays. Under the assumption that the encoders' observations are conditionally independent Markov chains given an unobserved time-invariant random variable, results on the structure of optimal real-time encoders and the receiver are obtained. It is shown that there exist finite-dimensional sufficient statistics for the encoders. The problem with noiseless channels and perfect memory at the receiver is then considered. A new methodology to find the structure of optimal real-time encoders is employed. A sufficient statistic with a time-invariant domain is found for this problem. This methodology exploits the presence of common information between the encoders and the receiver when communication is over noiseless channels.

cs.IT↗

arXiv2007

On Real-Time Communication Systems with Noisy Feedback

We consider a real-time communication system with noisy feedback consisting of a Markov source, a forward and a backward discrete memoryless channels, and a receiver with finite memory. The objective is to design an optimal communication strategy (that is, encoding, decoding, and memory update strategies) to minimize the total expected distortion over a finite horizon. We present a sequential decomposition for the problem, which results in a set of nested optimality equations to determine optimal communication strategies. This provides a systematic methodology to determine globally optimal joint source-channel encoding and decoding strategies for real-time communication systems with noisy feedback.

cs.IT↗

arXiv2006

Optimal Performance of Feedback Control Systems with Limited Communication over Noisy Channels

A discrete time stochastic feedback control system with a noisy communication channel between the sensor and the controller is considered. The sensor has limited memory. At each time, the sensor transmits encoded symbol over the channel and updates its memory. The controller receives a noisy version of the transmitted symbol, and generates a control action based on all its past observations and actions. This control action action is fed back into the system. At each stage the system incurs an instantaneous cost depending on the state of the plant and the control action. The objective is to choose encoding, memory updating and control strategies to minimize the expected total costs over a finite horizon, or the expected discounted cost over an infinite horizon, or the expected average cost per unit time over an infinite horizon. For each case we obtain a sequential decomposition of the optimization problem. The results are extended to the case when the sensor makes an imperfect observation of the state of the system.

math.OC↗

arXiv2005

A Decision Theoretic Framework for Real-Time Communication

We consider a communication system in which the outputs of a Markov source are encoded and decoded in \emph{real-time} by a finite memory receiver, and the distortion measure does not tolerate delays. The objective is to choose designs, i.e. real-time encoding, decoding and memory update strategies that minimize a total expected distortion measure. This is a dynamic team problem with non-classical information structure [Witsenhausen:1971]. We use the structural results of [Teneketzis:2004] to develop a sequential decomposition for the finite and infinite horizon problems. Thus, we obtain a systematic methodology for the determination of jointly optimal encoding decoding and memory update strategies for real-time point-to-point communication systems.

cs.IT↗