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15
Distributed power allocation with sinr constraints using trial and error learning
 in Military Communications and Information Systems Conference, MCC, Gdańsk
, 2012
"... Abstract—In this paper, we address the problem of global transmit power minimization in a selfconfiguring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we int ..."
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Cited by 8 (7 self)
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Abstract—In this paper, we address the problem of global transmit power minimization in a selfconfiguring network where radio devices are subject to operate at a minimum signal to interference plus noise ratio (SINR) level. We model the network as a parallel Gaussian interference channel and we introduce a fully decentralized algorithm (based on trial and error) able to statistically achieve a configuration where the performance demands are met. Contrary to existing solutions, our algorithm requires only local information and can learn stable and efficient working points by using only one bit feedback. We model the network under two different game theoretical frameworks: normal form and satisfaction form. We show that the converging points correspond to equilibrium points, namely Nash and satisfaction equilibrium. Similarly, we provide sufficient conditions for the algorithm to converge in both formulations. Moreover, we provide analytical results to estimate the algorithm’s performance, as a function of the network parameters. Finally, numerical results are provided to validate our theoretical conclusions.
Game Theoretic Approaches to Spectrum Sharing in Decentralized SelfConfiguring Networks
, 2012
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Achieving pareto optimal equilibria in energy efficient clustered ad hoc networks
 in Military Communication Conference, Milcom
, 2012
"... Abstract—In this paper, a decentralized iterative algorithm, namely the optimal dynamic learning (ODL) algorithm, is analysed. The ability of this algorithm of achieving a Pareto optimal working point exploiting only a minimal amount of information is shown. The algorithm performance is analysed in ..."
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Cited by 3 (2 self)
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Abstract—In this paper, a decentralized iterative algorithm, namely the optimal dynamic learning (ODL) algorithm, is analysed. The ability of this algorithm of achieving a Pareto optimal working point exploiting only a minimal amount of information is shown. The algorithm performance is analysed in a clustered ad hoc network, where radio devices are assumed to operate above a minimal signal to interference plus noise ratio (SINR) threshold while minimizing the global power consumption. Sufficient analytical conditions for ODL to converge to the desired working point are provided, moreover through numerical simulations the ability of the algorithm to configure an interference limited network is shown. The performances of ODL and of a Nash equilibrium reaching algorithm are numerically compared, and their performance as a function of available resources is studied. The gain of ODL is shown to be larger when the amount of available radio resources is scarce.
Perfect Output Feedback in the TwoUser Decentralized Interference Channel
, 2014
"... In this paper, the Nash equilibrium (NE) region of the twouser Gaussian interference channel (IC) with perfect output feedback is characterized to within 2 bits/s/Hz. The relevance of the NEregion is that it provides the set of ratepairs that are achievable and stable in the IC when both transmit ..."
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Cited by 3 (2 self)
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In this paper, the Nash equilibrium (NE) region of the twouser Gaussian interference channel (IC) with perfect output feedback is characterized to within 2 bits/s/Hz. The relevance of the NEregion is that it provides the set of ratepairs that are achievable and stable in the IC when both transmitterreceiver pairs autonomously tune their own transmit/receive configurations seeking an optimal individual transmission rate. Therefore, any rate tuple outside the NE region is not stable as there always exists at least one link able to increase its own transmission rate by updating its own transmit/receive configuration. The main conclusions of this paper are: (i) The NE region achieved with feedback is strictly larger than the NE region without feedback. More importantly, all the rate pairs uniquely achievable using feedback are at least weakly Pareto superior to those achievable without feedback. (ii) The use of feedback allows the achievability of all the strictly Pareto optimal rate pairs of the (approximate) capacity region of the Gaussian IC with feedback even when the network is fully decentralized.
Energyefficient power control for contentionbased synchronization in OFDMA systems with discrete powers and limited feedback
 EURASIP J. Wireless Commun. and Networking (JWCN
, 2013
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Game theory for signal processing in networks,”
 IEEE Signal Process. Mag.,
, 2015
"... Abstract In this tutorial, the basics of game theory are introduced along with an overview of its most recent and emerging applications in signal processing. One of the main features of this contribution is to gather in a single paper some fundamental gametheoretic notions and tools which, over th ..."
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Cited by 1 (0 self)
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Abstract In this tutorial, the basics of game theory are introduced along with an overview of its most recent and emerging applications in signal processing. One of the main features of this contribution is to gather in a single paper some fundamental gametheoretic notions and tools which, over the past few years, have become widely spread over a large number of papers. In particular, both strategicform and coalitionform games are described in details while the key connections and differences between them are outlined. Moreover, a particular attention is also devoted to clarify the connections between strategicform games and distributed optimization and learning algorithms. Beyond an introduction to the basic concepts and main solution approaches, several carefully designed examples are provided to allow a better understanding of how to apply the described tools.
Author manuscript, published in "ICC 2013, Budapest: Hungary (2013)" DOI: 10.1109/ICC.2013.6654723 Achieving Pareto Optimal Equilibria in Energy Efficient Clustered Ad Hoc Networks
, 2014
"... Abstract—In this paper, a decentralized iterative algorithm, namely the optimal dynamic learning (ODL) algorithm, is analysed. The ability of this algorithm of achieving a Pareto optimal working point exploiting only a minimal amount of information is shown. The algorithm performance is analysed in ..."
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Abstract—In this paper, a decentralized iterative algorithm, namely the optimal dynamic learning (ODL) algorithm, is analysed. The ability of this algorithm of achieving a Pareto optimal working point exploiting only a minimal amount of information is shown. The algorithm performance is analysed in a clustered ad hoc network, where radio devices are assumed to operate above a minimal signal to interference plus noise ratio (SINR) threshold while minimizing the global power consumption. Sufficient analytical conditions for ODL to converge to the desired working point are provided, moreover through numerical simulations the ability of the algorithm to configure an interference limited network is shown. The performances of ODL and of a Nash equilibrium reaching algorithm are numerically compared, and their performance as a function of available resources is studied. The gain of ODL is shown to be larger when the amount of available radio resources is scarce.
Learning to Use the Spectrum in SelfConfiguring Heterogenous Networks: A Logit Equilibrium Approach
"... In this paper, we study the particular scenario where several transmitterreceiver pairs communicate subject to mutual interference due to the usage of the same frequency bands. In particular, we focus on the case of heterogeneous networks, where radio devices have different interests (utility func ..."
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In this paper, we study the particular scenario where several transmitterreceiver pairs communicate subject to mutual interference due to the usage of the same frequency bands. In particular, we focus on the case of heterogeneous networks, where radio devices have different interests (utility functions), transmit configurations (sets of actions), as well as different signal processing and calculation capabilities. The underlying assumptions of this work are the followings: (i) the network is described by a set of states, for instance, the channel realization vector; (ii) radio devices are interested in their longterm average performance rather than instantaneous performance; (iii) each radio device is able to obtain a measure of its achieved performance at least once after updating its transmission configuration. Considering these conditions, we model the heterogenous network by a stochastic game. Our main contribution consists of a family of behavioral rules that allow radio devices to achieve an epsilonNash equilibrium of the corresponding stochastic game, namely a logit equilibrium. A thorough analysis of the convergence properties of these behavioral rules is presented. Finally, our approach is used in the context of a classical parallel interference channel in order to compare with existing results. 1.
1Equilibria of Channel Selection Games in Parallel Multiple Access Channels
"... In this paper, the parallel multiple access channel (MAC) is studied under the assumption that transmitters maximize their individual spectral efficiency by selfishly tuning their power allocation policy. Two particular scenarios are studied: (a) transmitters are allowed to use all the available cha ..."
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In this paper, the parallel multiple access channel (MAC) is studied under the assumption that transmitters maximize their individual spectral efficiency by selfishly tuning their power allocation policy. Two particular scenarios are studied: (a) transmitters are allowed to use all the available channels; and (b) transmitters are constrained to use a single channel. Both scenarios are modeled by oneshot games and the corresponding sets of Nash equilibria (NE) are fully characterized under the assumption that the receiver treads the multiple access interference as noise. In both cases, the set of NE is nonempty. In the case in which transmitters use a single channel, an upper bound of the cardinality of the NE set is provided in terms of the number of transmitters and number of channels. In particular, it is shown that in fully loaded networks, the sum spectral efficiency at the NE in scenario (a) is at most equal to the sum spectral efficiency at the NE in scenario (b). A formal proof of this observation, known in general as a Braess Paradox, is provided in the case of 2 transmitters and 2 channels. In general scenarios, we conjecture that the same effect holds as long as the network is kept fully loaded, as shown by numerical examples. Moreover, the price of anarchy and the price of stability in both games is also studied. Interestingly, under certain conditions on the channel gains, Pareto optimality can be achieved at some NE if and only if the number of channels equals or exceeds the number of transmitters. Finally, simulations are presented to verify the theoretical results. I.