Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth

Abstract

Sensor fusion has attracted a lot of research atten tion during the last years. Recently, a new research direction has emerged dealing with sensor fusion without knowledge of the ground truth. In this paper, we present a novel solution to the latter pertinent problem. In contrast to the first reported solutions to this problem, we present a solution that does not involve any assumption on the group average reliability which makes our 1·esults more general than previous works. We devise a strategic game where we show that a perfect partitioning of the sensors into reliable and unreliable groups corresponds to a Nash equilibrium of the game. Furthermore, we give sound theoretical results that prove that those equilibria are indeed the 1111ique Nash equilibria of the game. We then propose a solution involving a team of Learning Automata (LA) to unveil the identity of each sensor, whether it is reliable or unreliable, using game-theoretic learning. Experimental results show the accuracy of our solution and its ability to deal with settings that are unsolvable by legacy works.

A Reinforcement Learning based Game Theoretic Approach for Distributed Power Control in Downlink NOMA

“…It is worth mentioning that game theory has found a plethora of applications within the field of wireless networks, for a comprehensive survey we refer the reader to a book by Han et al [12]. On the other hand, some Reinforcement Learning (RL) strategies, such as Learning Automata (LA) [14], would ultimately yield the optimum strategy as the learning parameter gets sufficiently small [15]- [17]. LA is one of the simplest and yet efficient RL schemes that are shown to reach Nash Equilibrium (NE) in a large set of games [15].…”