Achieving Pareto Optimal Equilibria in Energy Efficient Clustered Ad Hoc Networks
In this paper, a decentralized iterative algorithm able to achieve a Pareto optimal working point in a clustered ad hoc network is analyzed. 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.