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    • Learning-Based Resource Management in Integrated Sensing and Communication Systems

    Ziyang Lu,M. Cenk Gursoy,Chilukuri K. Mohan,Pramod K. Varshney

    IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, INFOCOM WKSHPS 2024（2024）

    Syracuse Univ

    Cited 1|Views4
    Abstract
    In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints.
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    Key words
    Resource Management,Learning-based Resource Management,Deep Learning,Resource Constraints,Dynamic Environment,Dwell Time,Quality Of Communication,Deep Reinforcement Learning,Time Allocation,Time Budget,Deep Reinforcement Learning Approach,Measurement Noise,Time Slot,Azimuth Angle,Path Loss,Efficient Allocation,Model Predictive Control,Target State,Reward Function,Radar System,Deep Q-network,Extended Kalman Filter,Dual Variables,Phase Tracking,Constrained Optimization Problem,Markov Decision Process,Deep Q-learning,Deep Reinforcement Learning Framework,Radar Technology,Target Tracking
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