Distributed Power Allocation with SINR Constraints Using Trial and Error Learning

978-1-4673-0436-8International audienceIn this paper, we address the problem of global transmit power minimization in a self-configuring 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

Joint Channel and Power Allocation in Tactical Cognitive Networks: Enhanced Trial and Error

National audienceIn tactical networks the presence of a central controller (e.g., a base station) is made impractical by the unpredictability of the nodes' positions and by the fact that its presence can be exploited by hostile entities. As a consequence, self-configuring networks are sought for military and emergency communication networks. In such networks, the transmission parameters, most notably the transmission channel and the power level, are set by the devices following specific behavioural rules. In this context, an algorithm for self-configuring wireless networks is presented, analysed and enhanced to meet the specific needs of tactical networks. Such an algorithm, based on the concept of trial and error, is tested under static and mobile situations, and different metrics are considered to show its performance. In particular, the stability and performance improvements with respect to previously proposed versions of the algorithm are detailed

Achieving Pareto Optimal Equilibria in Energy Efficient Clustered Ad Hoc Networks

International audienceIn this paper, a decentralized iterative algorithm able to achieve a Pareto optimal working point in a clustered ad hoc network is analysed. Here, radio devices are assumed to operate above a minimal signal to interference plus noise ratio (SINR) threshold while minimizing the global power consumption. A distributed algorithm, namely the optimal dynamic learning (ODL), is presented and shown to be able to dynamically steer the network to an efficient working point, by exploiting only minimal amount of information. This algorithm aims at implementing a Pareto optimal solution for a large proportion of the time, with high probability. Conversely, existing solutions aim at achieving individually optimal solutions (Nash equilibria), which might be globally inefficient. The gain is shown to be larger when the amount of available radio resource is scarce. 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 performance of ODL and those of a Nash equilibrium reaching algorithm are numerically compared, and their performance as a function of available resources studied

Channel and power allocation algorithms for ad hoc clustered networks

978-1-4673-1422-0International audienceIn the context of mobile clustered ad hoc networks, this paper proposes and studies a self-configuring algorithm which is able to jointly set the channel frequency and power level of the transmitting nodes, by exploiting one bit of feedback per receiver. This algorithm is based upon a learning algorithm, namely trial and error, that is cast into a game theoretical framework in order to study its theoretical performance. We consider two different feedback solutions, one based on the SINR level estimation, and one based on the outcome of a CRC check. We analytically prove that this algorithm selects a suitable configuration for the network, and analyse its performance through numerical simulations under various scenarios