🤖 AI Summary
This paper investigates a zero-sum game between a mobile legitimate receiver and an interferer in wireless communications, using channel capacity as the payoff function—departing from prior works that assume static nodes. It introduces one-dimensional linear mobility as a core dynamic variable in the game formulation, establishing two scenario classes: static position configurations and dynamic position evolution, under three information structures—complete, incomplete, and delayed information. Theoretically, it derives closed-form solutions for static Nash equilibria and proposes a general design principle: “static solutions guide dynamic strategies.” Methodologically, it integrates game-theoretic analysis with reinforcement learning (RL) to efficiently approximate equilibria in high-dimensional dynamic strategy spaces. Experimental results demonstrate that the proposed RL-based policies exhibit robustness and practicality across diverse mobility patterns and information constraints.
📝 Abstract
We study a wireless jamming problem consisting of the competition between a legitimate receiver and a jammer, as a zero-sum game with the value to maximize/minimize being the channel capacity at the receiver's side. Most of the approaches found in the literature consider the two players to be stationary nodes. Instead, we investigate what happens when they can change location, specifically moving along a linear geometry. We frame this at first as a static game, which can be solved in closed form, and subsequently we extend it to a dynamic game, under three different versions for what concerns completeness/perfection of mutual information about the adversary's position, corresponding to different assumptions of concealment/sequentiality of the moves, respectively. We first provide some theoretical conditions that hold for the static game and also help identify good strategies valid under any setup, including dynamic games. Since dynamic games, although more realistic, are characterized by an exploding strategy space, we exploit reinforcement learning to obtain efficient strategies leading to equilibrium outcomes. We show how theoretical findings can be used to train smart agents to play the game, and validate our approach in practical setups.