Proactive Eavesdropping in Massive MIMO-OFDM Systems via Deep Reinforcement Learning

Abstract

It is challenging to monitor suspicious links in massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with directional beamforming. When the monitor and suspicious user are not in the coverage of the same beam, to implement legitimate monitoring, we propose the proactive eavesdropping scheme in which the monitor acts as a spoofing relay to realize beam misleading and data eavesdropping. In the phase of beam sweep, the beam misleading algorithm induces the transmitter to choose the beam which is beneficial to the monitor by optimizing the relay precoding matrices. In the phase of data transmission, the data eavesdropping algorithm elevates the monitoring rate by optimizing the relay power split factors and power gain factors of all subcarriers. Due to the unknown channel state information between suspicious pairs, we propose to find the optimal precoders and power split factors by the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The simulation results verify the effectiveness and superiority of the proposed eavesdropping scheme.