DRL-enabled secure uplink transmission for MIMO-OFDM IoT systems against intelligent eavesdroppers

Physical layer security has emerged as a promising approach to counter eavesdropping threats in the Internet of Things (IoT). With the advancement of technology, eavesdroppers (Eves) are increasingly sophisticated, exacerbating the difficulties in ensuring the security of uplink transmissions. In situations where transmitters are unable to deter Eves from approaching at close range, these intelligent Eves can attain substantial channel gains, thereby posing significant security threats. To address these challenges, we first propose a time-domain artificial noise (AN) scheme to eliminate interference at the legitimate receiver in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. Then, we model the adversarial interaction between legitimate transceivers and intelligent Eves as a game-theoretic problem. To solve the dynamic game problem, we introduce a deep deterministic policy gradient (DDPG)-based framework, where an optimal beamforming fractional programming algorithm (OBFP) and a lower-complexity zero-forcing beamforming difference of convex algorithm (ZFDC) are designed to ascertain the most effective strategy for legitimate transceivers. Simulation results validate the effectiveness of our proposed algorithms and highlight the critical role of time-domain AN in combating intelligent eavesdropping.