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1 Satisfaction Equilibrium: A General Framework for QoS Provisioning in SelfConfiguring Networks
"... Abstract—This paper is concerned with the concept of equilibrium and quality of service (QoS) provisioning in selfconfiguring wireless networks with noncooperative radio devices (RD). In contrast with the Nash equilibrium (NE), where RDs are interested in selfishly maximizing its QoS, we present a ..."
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Abstract—This paper is concerned with the concept of equilibrium and quality of service (QoS) provisioning in selfconfiguring wireless networks with noncooperative radio devices (RD). In contrast with the Nash equilibrium (NE), where RDs are interested in selfishly maximizing its QoS, we present a concept of equilibrium, named satisfaction equilibrium (SE), where RDs are interested only in guaranteing a minimum QoS. We provide the conditions for the existence and the uniqueness of the SE. Later, in order to provide an equilibrium selection framework for the SE, we introduce the concept of effort or cost of satisfaction, for instance, in terms of transmit power levels, constellation sizes, etc. Using the idea of effort, the set of efficient SE (ESE) is defined. At the ESE, transmitters satisfy their minimum QoS incurring in the lowest effort. We prove that contrary to the (generalized) NE, at least one ESE always exists whenever the network is able to simultaneously support the individual QoS requests. Finally, we provide a fully decentralized algorithm to allow selfconfiguring networks to converge to one of the SE relying only on local information. I.
On the Base Station Selection and Base Station Sharing in SelfConfiguring Networks
"... We model the interaction of several radio devices aiming to obtain wireless connectivity by using a set of base stations (BS) as a noncooperative game. Each radio device aims to maximize its own spectral efficiency (SE) in two different scenarios: First, we let each player to use a unique BS (BS se ..."
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We model the interaction of several radio devices aiming to obtain wireless connectivity by using a set of base stations (BS) as a noncooperative game. Each radio device aims to maximize its own spectral efficiency (SE) in two different scenarios: First, we let each player to use a unique BS (BS selection) and second, we let them to simultaneously use several BSs (BS Sharing). In both cases, we show that the resulting game is an exact potential game. We found that the BS selection game posses multiple Nash equilibria (NE) while the BS sharing game posses a unique one. We provide fully decentralized algorithms which always converge to a NE in both games. We analyze the price of anarchy and the price of stability for the case of BS selection. Finally, we observed that depending on the number of transmitters, the BS selection technique might provide a better global performance (network spectral efficiency) than BS sharing, which suggests the existence of a Braess type paradox.
How Many Parallel TCP Sessions to Open: A Pricing Perspective
"... Abstract. TCP is one of the main transmission protocols used in the Internet. It has also been recently observed that opening parallel TCP sessions might be of interest for a user in order to increase his overall average throughput. We suggest in this paper to charge users per TCP session, and we in ..."
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Abstract. TCP is one of the main transmission protocols used in the Internet. It has also been recently observed that opening parallel TCP sessions might be of interest for a user in order to increase his overall average throughput. We suggest in this paper to charge users per TCP session, and we investigate the resulting game in a homogeneous context: how many sessions should each user open? Given the discrete (and even finite) space of strategies, we propose to implement a probabilistic adaptation algorithm, analyze its theoretical properties and provide numerical illustrations. 1
HOW CAN IGNORANT BUT PATIENT COGNITIVE TERMINALS LEARN THEIR STRATEGY AND UTILITY?
"... This paper aims to contribute to bridge the gap between existing theoretical results in distributed radio resource allocation policies based on equilibria in games (assuming complete information and rational players) and practical design of signal processing algorithms for selfconfiguring wireless ..."
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This paper aims to contribute to bridge the gap between existing theoretical results in distributed radio resource allocation policies based on equilibria in games (assuming complete information and rational players) and practical design of signal processing algorithms for selfconfiguring wireless networks. For this purpose, the framework of learning theory in games is exploited. Here, a new learning algorithm based on mild information assumptions at the transmitters is presented. This algorithm possesses attractive convergence properties not available for standard reinforcement learning algorithms and in addition, it allows each transmitter to learn both its optimal strategy and the values of its expected utility for all its actions. A detailed convergence analysis is conducted. In particular, a framework for studying heterogeneous wireless networks where transmitters do not learn at the same rate is provided. The proposed algorithm, which can be applied to any wireless network verifying the information assumptions stated, is applied to the case of multiple access channels in order to provide some numerical results. 1.
LEARNING DISTRIBUTED POWER ALLOCATION POLICIES IN MIMO CHANNELS
"... In this paper 1, we study the discrete power allocation game for the fast fading multipleinput multipleoutput multiple access channel. Each player or transmitter chooses its own transmit power policy from a certain finite set to optimize its individual transmission rate. First, we prove the existe ..."
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In this paper 1, we study the discrete power allocation game for the fast fading multipleinput multipleoutput multiple access channel. Each player or transmitter chooses its own transmit power policy from a certain finite set to optimize its individual transmission rate. First, we prove the existence of at least one pure strategy Nash equilibrium. Then, we investigate two learning algorithms that allow the players to converge to either one of the NE states or to the set of correlated equilibria. At last, we compare the performance of the considered discrete game with the continuous game in [7]. 1.
Game Theoretic Approaches to Spectrum Sharing in Decentralized SelfConfiguring Networks
, 2012
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Using Model Checking for Analyzing Distributed Power Control Problems
, 2010
"... Model checking (MC) is a formal verification technique which has been known and still knows a resounding success in the computer science community. Realizing that the distributed power control (PC) problem can be modeled by a timed game between a given transmitter and its environment, the authors ..."
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Model checking (MC) is a formal verification technique which has been known and still knows a resounding success in the computer science community. Realizing that the distributed power control (PC) problem can be modeled by a timed game between a given transmitter and its environment, the authors wanted to know whether this approach can be applied to distributed PC. It turns out that it can be applied successfully and allows one to analyze realistic scenarios including the case of discrete transmit powers and games with incomplete information. The proposed methodology is as follows. We state some objectives a transmitterreceiver pair would like to reach. The network is modeled by a game where transmitters are considered as timed automata interacting with each other. The objectives are then translated into timed alternatingtime temporal logic formulae and MC is exploited to know whether the desired properties are verified and determine a winning strategy.
Opportunistic Spectrum Access in Unknown Dynamic Environment: A GameTheoretic Stochastic Learning Solution
"... Abstract—We investigate the problem of distributed channel selection using a gametheoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel sele ..."
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Abstract—We investigate the problem of distributed channel selection using a gametheoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is timevarying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genieaided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual actionreward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness. Index Terms—Cognitive radio networks, opportunistic spectrum access, distributed channel selection, exact potential game, stochastic learning automata. I.
Using Model Checking for Analyzing Distributed 1 Power Control Problems
, 2010
"... Model checking (MC) is a formal verification technique which has known and still knows a resounding success in the computer science community. Realizing that the distributed power control (PC) problem can be modeled by a timed game between a given transmitter and its environment the authors wanted t ..."
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Model checking (MC) is a formal verification technique which has known and still knows a resounding success in the computer science community. Realizing that the distributed power control (PC) problem can be modeled by a timed game between a given transmitter and its environment the authors wanted to know whether this approach can be applied to distributed PC. It turns out that it can be applied successfully and allows one to analyze realistic scenarii including the case of discrete transmit powers and games with incomplete information. The proposed methodology is as follows. We state some objectives a transmitterreceiver pair would like to reach. The network is modeled by a game where transmitters are considered as timed automata interacting with each other. The objectives are then translated into timed alternatingtime temporal logic formulae and MC is exploited to know whether the desired properties are verified and determine a winning strategy. Index Terms Distributed power control, game theory, interference channel, model checking, timed games, verification.
Reinforcement Learning based Routing Protocols in WSNs: A Survey
"... Abstract — Advances in the technology along with reduction in processor size, its memory, and wireless antenna size has facilitated the construction of low cost, low powered and multifunctional Sensor nodes which in turn led to high demand for development of Wireless Sensor Networks. A lot of resear ..."
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Abstract — Advances in the technology along with reduction in processor size, its memory, and wireless antenna size has facilitated the construction of low cost, low powered and multifunctional Sensor nodes which in turn led to high demand for development of Wireless Sensor Networks. A lot of research work has been done regarding the development of routing protocols for WSNs. This paper provides a brief overview of the routing protocols using Reinforcement learning approach for WSNs.