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.
To improve the dynamic performances of a 2-poles small single phase brushless permanent magnet motor (SP-BLPMM), suitably shaped auxiliary PM (APM) are proposed in the paper. The electromechanical model of SP-BLPMM – integrated in a small pump application without APM – is then preliminary assessed by the experimental data elaboration. Afterwards, the benefits due to the APM are analyzed by both preliminary magneto-static finite element analysis (FEAs) and transient solutions. By optimizing the APM geometry and magnetization pattern the starting torque is appreciably increased and speed oscillations at load are reduced
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