Achieving Pareto optimal equilibria in energy efficient clustered ad hoc networks

Abstract

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.

A satisfaction game for heating, ventilation and air conditioning control of smart buildings

“…Alternatively, reducing the value of β leads the player to focus more on the reduction of the individual operating cost. This utility function was first proposed in [15] for the case of decentralized radio resource allocation in wireless networks.…”

Performance Analysis of Trial and Error Algorithms

“…Decentralized resource allocation approaches have been used [6], [7] to respectively share the resources among femtocells or wifi access points. In ad hoc networks, the network is infrastructure-less which makes decentralized learning solutions suited in such contexts [8], [9], [10], [11].…”

“…The trial and error algorithms, proposed in [4], [12] and then applied to various resource sharing problems e.g. [8], [9], [10], [11], are very appealing in these contexts.…”

Algorithms for stochastic games on interference channels

“…A 2-user PGIC is considered in [9] and proposes a channel selection game and finds its NE. In [3] and [10], authors consider minimizing power consumption subject to quality of service (QoS) constraints. In [10], authors find NE and in [3], when strategy space is limited to finite power levels, Pareto optimal points are proposed as a solution of the game.…”

“…In [3] and [10], authors consider minimizing power consumption subject to quality of service (QoS) constraints. In [10], authors find NE and in [3], when strategy space is limited to finite power levels, Pareto optimal points are proposed as a solution of the game. In [6], authors formulate the problem of interference channels as a Stackelberg game and study its equilibrium.…”