arXiv subjects

André B. J. Kokkeler

Publications and source records attributed to André B. J. Kokkeler.

3 recordsLinked to original sources

arXiv2026

Resource allocation in Distributed ISAC Systems: A Location Sensitive Mode Selection and Task Assignment Framework

In this work we propose a region of interest (ROI)-aware mode selection and task assignment (RAMSTA) framework for distributed integrated sensing and communication (ISAC) systems. In realistic ISAC use-cases, the sensing performance requirement can be location dependent. Vehicular targets at a traffic intersection, unmanned aerial vehicle approaching a restricted airspace or a sparsely populated areas may require different levels of detection or localization accuracy. This warrants the design of location sensitive resource allocation to account for the varying sensing performance requirements. Thus, we design a sigmoid-based ROI proximity parameter to tune the communication-sensing trade-off in a weighted sum rate and position posterior Cramér-Rao lower bound minimization problem. The resulting mixed-integer non-linear program is solved by applying a penalized convex-concave procedure. We show that the ROI sensitivity thresholds allows the adaptive variation of the communication-sensing trade-off with respect to the predicted location of the target and relative to a predefined ROI.

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arXiv2025

Dynamic Beamforming and Power Allocation in ISAC via Deep Reinforcement Learning

Integrated Sensing and Communication (ISAC) is a key enabler in 6G networks, where sensing and communication capabilities are designed to complement and enhance each other. One of the main challenges in ISAC lies in resource allocation, which becomes computationally demanding in dynamic environments requiring real-time adaptation. In this paper, we propose a Deep Reinforcement Learning (DRL)-based approach for dynamic beamforming and power allocation in ISAC systems. The DRL agent interacts with the environment and learns optimal strategies through trial and error, guided by predefined rewards. Simulation results show that the DRL-based solution converges within 2000 episodes and achieves up to 80\% of the spectral efficiency of a semidefinite relaxation (SDR) benchmark. More importantly, it offers a significant improvement in runtime performance, achieving decision times of around 20 ms compared to 4500 ms for the SDR method. Furthermore, compared with a Deep Q-Network (DQN) benchmark employing discrete beamforming, the proposed approach achieves approximately 30\% higher sum-rate with comparable runtime. These results highlight the potential of DRL for enabling real-time, high-performance ISAC in dynamic scenarios.

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arXiv2025

Performance Analysis of Sub-band Full-duplex Cell-free Massive MIMO JCAS Systems

In-band Full-duplex joint communication and sensing systems require self interference cancellation as well as decoupling of the mutual interference between UL communication signals and radar echoes. We present sub-band full-duplex as an alternative duplexing scheme to achieve simultaneous uplink communication and target parameter estimation in a cell-free massive MIMO system. Sub-band full-duplex allows uplink and downlink transmissions simultaneously on non-overlapping frequency resources via explicitly defined uplink and downlink sub-bands in each timeslot. Thus, we propose a sub-band full-duplex cell-free massive MIMO system with active downlink sensing on downlink sub-bands and uplink communication on uplink sub-band. In the proposed system, the target illumination signal is transmitted on the downlink (radar) sub-band whereas uplink users transmit on the uplink (communication) sub-band. By assuming efficient suppression of inter-sub-band interference between radar and communication sub-bands, uplink communication and radar signals can be efficiently processed without mutual interference. We show that each AP can estimate sensing parameters with high accuracy in SBFD cell-free massive MIMO JCAS systems.

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