RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs

• URL: http://arxiv.org/abs/2511.02672v1
• Date: Tue, 04 Nov 2025 15:49:51 GMT
• Title: RL-Aided Cognitive ISAC: Robust Detection and Sensing-Communication Trade-offs
• Authors: Adam Umra, Aya M. Ahmed, Aydin Sezgin,
• Abstract summary: This paper proposes a reinforcement learning-aided cognitive framework for integrated sensing and communication.<n>A Wald-type detector is employed for robust target detection under non-Gaussian clutter.<n>A SARSA-based RL algorithm enables adaptive estimation of target positions without prior environmental knowledge.
• Score: 4.7199341867016456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization [12.38744459760065]
  This paper introduces an unmanned aerial vehicle (UAV) to enhance system flexibility and proposes an optimization design for the spectrum efficiency of the STAR-RIS-UAV-assisted wireless communication system.<n>We present a deep reinforcement learning (DRL) algorithm capable of iteratively optimizing beamforming, phase shifts, and UAV positioning to maximize the system's sum rate through continuous interactions with the environment.
  arXiv  Detail & Related papers  (2025-12-01T02:36:00Z)
• Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks [83.84711311344918]
  Integrated sensing, communications, and computing (I SCC) is one of the core parts of Low-Altitude Wireless Networks (LAWNs)<n>This paper studies joint performance optimization of I SCC while considering secrecyness of the communications.<n>We propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives.
  arXiv  Detail & Related papers  (2025-11-03T11:06:41Z)
• Green Learning for STAR-RIS mmWave Systems with Implicit CSI [53.03358325565645]
  Green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) broadcasting systems.<n>Motivated by the emphasis on environmental sustainability in future 6G networks, this work adopts a transmission framework for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption.
  arXiv  Detail & Related papers  (2025-09-08T15:56:06Z)
• Anti-Jamming Sensing with Distributed Reconfigurable Intelligent Metasurface Antennas [11.159180009266489]
  The sensing accuracy of traditional RF sensing methods is often affected by adverse propagation channels from the transmitter to the receiver, such as fading and noise.<n>In this paper, we propose employing distributed Reconfigurable Intelligent Meta Antennas (RIMSA) to detect the presence and location of objects.<n>We introduce a deep reinforcement learning (DRL) algorithm aimed at calculating the optimal beamforming patterns and a neural network aimed at converting received signals into sensing outcomes.
  arXiv  Detail & Related papers  (2025-08-07T01:33:38Z)
• Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
  Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
  arXiv  Detail & Related papers  (2025-07-12T17:11:06Z)
• Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar [8.674241138986925]
  The system employs a planar array configuration and adapts its transmitted waveforms and beamforming patterns to optimize detection performance.<n>A robust Wald-type detector is integrated with a SARSA-based RL algorithm, enabling the radar to learn and adapt to complex clutter environments.
  arXiv  Detail & Related papers  (2025-02-07T14:31:58Z)
• A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication [52.40430937325323]
  We consider a point-to-point integrated sensing and communication (ISAC) system, where a transmitter conveys a message to a receiver over a channel with memory.<n>We formulate the capacity-distortion tradeoff for the ISAC problem when sensing is performed in an online fashion.
  arXiv  Detail & Related papers  (2024-12-02T03:30:50Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Rethinking the Tradeoff in Integrated Sensing and Communication: Recognition Accuracy versus Communication Rate [21.149708253108788]
  Integrated sensing and communication (ISAC) is a promising technology to improve the band-utilization efficiency. There exists a tradeoff between the sensing and communication performance. This paper formulates and solves a multi-objective optimization problem which simultaneously maximizes the recognition accuracy and the communication data rate.
  arXiv  Detail & Related papers  (2021-07-20T17:00:35Z)
• Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments [8.103366584285645]
  A radar learns to vary the bandwidth and center frequency of its linear frequency modulated (LFM) waveforms to mitigate mutual interference with other systems. We extend the DQL-based approach to incorporate Double Q-learning and a recurrent neural network to form a Double Deep Recurrent Q-Network. Our experimental results indicate that the proposed Deep RL approach significantly improves radar detection performance in congested spectral environments.
  arXiv  Detail & Related papers  (2020-06-23T17:21:28Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)
• A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar [12.982044791524494]
  This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR) We propose a reinforcement learning (RL) based algorithm for cognitive multi-target detection in the presence of unknown disturbance statistics. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments.
  arXiv  Detail & Related papers  (2020-05-10T16:29:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.