Dynamic conjectures in random access networks using bio-inspired learning

—Inspired by the biological entities’ ability to achieve reciprocity in the course of evolution, this paper considers a conjecture-based distributed learning approach that enables autonomous nodes to independently optimize their transmission probabilities in random access networks. We model the interaction among multiple self-interested nodes as a game. It is well-known that the Nash equilibria in this game result in zero throughput for all the nodes if they take myopic best-response, thereby leading to a network collapse. This paper enables nodes to behave as intelligent entities which can proactively gather information, form internal conjectures on how their competitors would react to their actions, and update their beliefs according to their local observations. In this way, nodes are capable to autonomously “learn” the behavior of their competitors, optimize their own actions, and eventually cultivate reciprocity in the random access network. To characterize the steady-state...

• Type: Journal
• Year: 2010
• Where: JSAC
• Authors: Yi Su, Mihaela van der Schaar
• Keywords: Conjectural Equilibrium, JSAC 2010, Multiple Self-interested Nodes, Random Access Network