We study a wireless jamming problem consisting of the competition between a legitimate receiver and a jammer, modeled as a zero-sum game where the value to maximize/minimize is the channel capacity at the receiver's side. Most approaches 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 concerning 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. Because dynamic games, although more realistic, have a significantly larger strategy space, we use reinforcement learning to obtain efficient strategies that lead 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 settings.

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