Resource Allocation in Multi-Cell Integrated Sensing and Communication Systems: A DRL Approach

Integrated sensing and communication (ISAC) has been seen as a promising technology to satisfy the dual requirements of communication and sensing for the emerging applications in the next-generation wireless networks. In this paper, we research one down-link multi-cell orthogonal frequency division multiple access (OFDMA) ISAC system, in which a group of collaborative ISAC base stations send signals to their corresponding communication users, and concurrently work with multiple sensing receivers to estimate locations of multiple targets. Specifically, we investigate the joint sub-channel assignment and power allocation for users and targets to maximize the sum-rate, while ensuring the minimal signal-to-interference-plus-noise ratio (SINR) constraint for each user and the maximal Cramer-Rao lower bound (CRLB) requirement for each target. We propose a deep reinforcement learning (DRL) approach to address the above sub-channel assignment and power allocation problems. In our approach, we adopt the dueling deep Q network (DDQN) and the deep deterministic policy gradient (DDPG) network to output the sub-channel assignment policy and power allocation policy separately. Simulation results aim to prove the effectiveness of our proposed algorithm.