This paper introduces two novel algorithms designed to address the challenge of super-resolution sensing parameter estimation in bistatic configurations within communication-centric integrated sensing and communication (ISAC) systems. Our approach leverages the estimated channel state information derived from reference symbols originally intended for communication to achieve super-resolution sensing parameter estimation. The first algorithm, IFFT-C2VNN, employs complex-valued convolutional neural networks to estimate the parameters of different targets, achieving significant reductions in computational complexity compared to traditional methods. The second algorithm, PARAMING, utilizes a parametric method that capitalizes on the knowledge of the system model, including the transmit and receive array geometries, to extract the sensing parameters accurately. Through a comprehensive performance analysis, we demonstrate the effectiveness and robustness of both algorithms across a range of signal-to-noise ratios, underscoring their applicability in realistic ISAC scenarios.

Driven by the compelling advantages of agility and cost-efficiency inherent in unmanned aerial vehicles (UAV s), this study introduces TANAGERS (emergenT communication for uA vs as flyinG passivE RadarS), an innovative communication-augmented multi-agent reinforcement learning algorithm (MARL) designed for the movement control of UAVs operating as flying passive radars in bistatic integrated sensing and communication scenarios. In this research, we employ the proposed MARL framework to address the sensing signal-to-noise ratio (SNR) maximization problem for targets within a given environment by leveraging signals from base stations, all while taking into account realistic communication channels between pairs of UAV s. Simulation results underscore the significant enhancement brought by our proposed algorithm in radar performance, as measured by the total achievable sensing SNR of the UAV s during their trajectory. The key strength lies in the algorithm's ability to learn a resilient communication protocol that effectively mitigates the stochastic and unreliable nature of channel links between UAV s.