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Reinforcement Learning based Location Verification in the MIMO System

In this paper, we investigate the location verification which exploits the physical signal to verify the transmitter’s location information in the multiple-input multiple-output (MIMO) system. Specifically, the transmitter intermittently spoofs the location information for its selfish purpose, whereas the LVS verifies the received signal and accepts the location information that passed the hypothesis test. We formulate the interaction between the transmitter and LVS as a zero-sum game, where the LVS chooses the test threshold of the hypothesis test, while the transmitter chooses its spoofing rate and spoofed locations. We prove the conditions for the existence of the equilibrium for the static game, and propose a reinforcement learning algorithm based on Q-learning for the LVS to achieve the optimal test threshold in the dynamic environment without knowing the exact channel conditions and the spoofing cost. The simulation results show that the proposed approach outperforms the baseline by reducing the location verification error rates, and increasing the utility for the LVS significantly.