RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs
This work addresses robust and spectrum-efficient sensing-communication trade-offs for next-generation wireless networks, representing an incremental improvement with a novel RL-aided approach.
The paper tackles the problem of enhancing radar sensing in integrated sensing and communication systems under unknown dynamic disturbances, achieving significantly improved detection probability compared to baselines while maintaining competitive communication performance.
This paper proposes a reinforcement learning (RL)-aided cognitive framework for massive MIMO-based integrated sensing and communication (ISAC) systems employing a uniform planar array (UPA). The focus is on enhancing radar sensing performance in environments with unknown and dynamic disturbance characteristics. A Wald-type detector is employed for robust target detection under non-Gaussian clutter, while a SARSA-based RL algorithm enables adaptive estimation of target positions without prior environmental knowledge. Based on the RL-derived sensing information, a joint waveform optimization strategy is formulated to balance radar sensing accuracy and downlink communication throughput. The resulting design provides an adaptive trade-off between detection performance and achievable sum rate through an analytically derived closed-form solution. Monte Carlo simulations demonstrate that the proposed cognitive ISAC framework achieves significantly improved detection probability compared to orthogonal and non-learning adaptive baselines, while maintaining competitive communication performance. These results underline the potential of RL-assisted sensing for robust and spectrum-efficient ISAC in next-generation wireless networks.