Integrated Sensing and Communication

Also known as ISAC

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 27

Oct 8, 2026cs.LG

SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing

Future 6G networks aim to expose sensing as a native service by reusing communication infrastructure. We study respiration sensing on a cell-free massive multiple-input multiple-output (MIMO) base station, where a 64-antenna channel must be fused into a breathing waveform. The state-of-the-art hand-crafted fusion is near-optimal in benign conditions. It collapses, however, under strong in-band motion interference, whose frequency falls inside the respiration band. We show that a learned complex-weight beamformer recovers respiration by spatial nulling, and that the remaining gap to a per-recording oracle can be closed at deployment by label-free test-time adaptation. Crucially, we identify which label-free signal makes this work. Frequency- and variance-based criteria cannot separate an in-band interferer from breathing. Our spatial-redundancy test-time adaptation (SR-TTA), which maximizes consistency across random antenna subsets under an out-of-band spectral veto, preserves benign performance in our tests. The respiration-rate error drops from 5.8 to 0.8 breaths per minute (bpm) under simulated in-band interference, and the pipeline maps onto the Open Radio Access Network (O-RAN) architecture as O-RAN distributed-unit (O-DU) range-gating, an adaptation xApp, and a calibration rApp. On real testbed recordings, a one-time cross-subject calibration plus SR-TTA reduces failures from 47% to 7%, drawing level with the hand-crafted combiner using label-free test-time adaptation.

Jingyuan Liu, Zheng Chang, Haoqiu Xiong +2

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · WaveCoRe, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium

Test-Time Adaptation Integrated Sensing and Communication Wireless Communications

Oct 8, 2026eess.SP

An Embodied Multiagent Framework Based on Token Communications for Cooperative ISAC

The emerging low-altitude economy demands unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) for reliable connectivity and environmental awareness. In particular, embodied UAV agents offer a promising means of supporting autonomous operations through a closed loop linking perception, decision-making, and physical actions. However, each UAV has access only to local observations, and effective cooperation requires exchanging local states and intentions. Directly sharing such information can incur substantial signaling overhead and hinder timely coordination in dynamic environments. To deal with this problem, this paper investigates a cooperative ISAC network of embodied UAV agents and formulates a joint token communications (TokCom) and physical control problem to minimize total propulsion energy subject to communication and sensing rate requirements. Then, we propose a state--intent TokCom (SI-TokCom) framework driven by multi-agent embodied policy learning. Specifically, separate pretrained codebooks enable compact exchanges of local states and intentions, while UAV agents jointly learn to select and compose tokens and determine physical actions based on local observations and received tokens. Simulation results show that SI-TokCom achieves 98.9% and 99.1% of the centralized baseline's communication and sensing rates, respectively. Compared with the local baseline, it improves the corresponding rates by 5.0% and 43.6%, respectively, with essentially unchanged propulsion energy. These results highlight the potential of TokCom for communication-efficient cooperation among embodied UAV agents in ISAC systems.

Jiahe Guo, Jun Du, Chunxiao Jiang +1

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China · State Key Laboratory of Space Network and Communications, Tsinghua University, Beijing 100084, China · Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

Integrated Sensing and Communication Multi-Agent Communication Aerial Robotics

Sep 30, 2026cs.AI

AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC

Multi-modal integrated sensing and communication (ISAC) enables environmental perception and reliable connectivity for intelligent wireless networks. Data-driven multi-modal ISAC models depend heavily on annotated real-world data to learn relationships across sensing and wireless observations, thereby constraining scalable deployment. Although synthetic data generation reduces the burden, adapting existing simulation pipelines to a target deployment requires consistent scene, sensing, wireless, and learning configurations, while mismatches among these coupled components impair sim-to-real transferability. To address the challenge, we propose an agentic artificial intelligence (AI) framework for sim-to-real multi-modal ISAC, named AIMS. Given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, AIMS derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model. A two-agent architecture coordinates scene construction with task learning. A scene construction agent generates geographically grounded, synchronized sensing and wireless records from shared physical states, while a scene understanding agent configures task-relevant modalities and mixture-of-experts (MoE) learning for zero-shot inference or few-shot adaptation. Structured domain knowledge guides dependency-aware planning, while validation evidence supports feedback-driven revision of affected decisions. Experiments on the real-world DeepSense 6G dataset demonstrate improved vehicle detection and beam prediction over the considered simulation and fusion baselines. A separate orchestration benchmark evaluates task interpretation, dependency reasoning, and feedback-driven replanning across diverse deployment requests, showing improved plan correctness with structured domain knowledge and validation feedback.

Yijie Bian, Kai Zhang, Wei Guo +4

Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

Integrated Sensing and Communication Sim-to-Real Transfer Multimodal Agents Multimodal Sensor Fusion

Sep 27, 2026cs.LG

Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach

Integrated sensing and communication (ISAC) enables joint communication and sensing using a shared waveform, but its signal design is challenging due to the inherent trade-off between the two objectives, particularly in the short blocklength regime. This paper proposes an autoencoder (AE)-based framework for ISAC waveform design in noncoherent settings. We derive a modified Cramér-Rao bound for multi-target delay estimation and analyze the maximum-likelihood decoding rule for noncoherent communication under correlated fading. These results reveal structural connections and trade-offs between communication and sensing objectives in waveform design. Based on this analysis, the AE learns waveform representations that jointly optimize both functionalities, with a tunable parameter controlling the trade-off. Simulation results show that the proposed design outperforms conventional schemes in both communication reliability and sensing accuracy, especially under short blocklength and fading conditions.

Muah Kim, Shuangyang Li, Tayyebeh Jahani-Nezhad +2

Faculty of Electrical and Computer Engineering, Dresden University of Technology, Dresden 01069, Germany · Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin 10587, Germany

Integrated Sensing and Communication Wireless Communications

Sep 27, 2026cs.LG

StarBOA: Real-Time Mamba State-Space Unrolling for Sparse Radar Micro-Doppler in ISAC Networks

In Integrated Sensing and Communications (ISAC), radar sensing must operate under chirp subsampling with up to 90% missing data. An attention-based baseline, limited to a 52ms buffer, collapses toward maximum uniform entropy ( $H=2.584$ bits) as sparsity increases, failing to capture long-range gait-cycle context. We propose StarBOA, which replaces attention with a causal Mamba state-space model that updates incrementally on a per-window basis without re-scanning past reconstructions. By maintaining a persistent state, StarBOA integrates over $100\times$ more temporal history at no additional per-step computational cost. StarBOA outperforms the baseline's published results across all sparsity levels, with SSIM gains increasing from $+0.0379$ at 50% missing data to $+0.2472$ at 90%. Each window is processed in 1.53ms with zero lookahead, demonstrating efficient causal reconstruction under extreme chirp subsampling.

Mustafa Bora Çelik, Ceren Çelik, Orhan Gazi

Sparse Recovery Integrated Sensing and Communication

Sep 9, 2026cs.LG

Robust Beam Prediction for V2X Networks with Multi-Modal Sensing

Integrated sensing and communication (ISAC) provides a promising foundation for beam prediction in future vehicle-to-everything (V2X) networks. However, existing sensing-assisted beamforming methods still rely heavily on radio-frequency sensing, which may become unreliable in complex vehicular environments. Meanwhile, the growing availability of heterogeneous sensors, such as cameras and LiDAR, offers new opportunities to improve beam prediction through richer environmental perception. Motivated by this, this paper proposes a multi-modal beam prediction framework for V2X networks. Specifically, we develop BeamTransFuser, a hierarchical Transformer-based architecture that progressively fuses camera, LiDAR, radar, and GPS observations for robust beam prediction. In addition, to handle possible missing modalities in practical deployment, we introduce a generative module that reconstructs missing modality features from the available observations. Experimental results on a real-world multi-modal V2X dataset show that the proposed framework consistently outperforms representative baselines, while the generative module further improves robustness under incomplete sensing conditions.

Chen Shang, Dinh Thai Hoang, Diep N. Nguyen +1

School of Electrical and Data Engineering, University of Technology Sydney, Australia · Internet of Things Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

Integrated Sensing and Communication V2X Communication Multimodal Sensor Fusion

Sep 2, 2026cs.NI

Feasible but Not Safe: Constraint Violations and Report-Channel Attacks in Learned Cell-Free ISAC Association

Learning-based schedulers have been proposed to provide real-time user, target, and access point (AP) association in distributed cell-free integrated sensing and communication systems. In a typical approach, a graph neural network (GNN), trained on labels from a mixed-integer linear program, maps lightweight per-AP statistics to decisions on AP clustering, user and target scheduling, and mode selection in one forward pass. Such solutions assume that hard constraints, enforced only as soft training penalties, hold at inference, and that the self-reported statistics are truthful. Using our ASSENT algorithm as an example, we find that despite high

F_1

scores, many solutions violate at least one hard constraint, demonstrating that high prediction accuracy does not ensure joint feasibility. Projecting the GNN output onto a feasible solution restores constraint satisfaction with low utility loss, even with a simple greedy repair procedure. We further show that feasibility alone does not guarantee robustness to false data injection attacks. A single malicious AP that reports false information cannot substantially increase its user associations, but can greatly increase the rate of infeasible solutions. The effect of such attacks depends on the type of information being falsified. Misreporting information that affects the objective can largely be mitigated through feasibility projection, whereas falsifying information that affects the constraints cannot. The latter can, however, be detected using a low-complexity cross-AP consistency check. These results show that learned ISAC schedulers should be evaluated using constraint-aware feasibility metrics in addition to conventional accuracy measures.

Mehdi Zafari, Iman Mohammadi, A. Lee Swindlehurst

University of California Irvine · Irvine, CA, USA

Graph Neural Networks Integrated Sensing and Communication False Data Injection Attacks Learning to Optimize Wireless Resource Allocation

Aug 6, 2026cs.AI

When Agentic AI Meets Integrated Sensing and Communication

Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC. Existing work on learning-based sensing, resource allocation, reconfigurable intelligent surfaces (RIS), edge intelligence, multi-agent coordination, and resilient networking has developed largely in isolation. This survey unifies the literature within a six-stage closed-loop framework comprising observation, contextualization, reasoning and prediction, planning and orchestration, execution and collaboration, and feedback and resilience. It also introduces five levels of agentic maturity, ranging from physical-layer primitives to fully closed-loop agentic ISAC. We use this framework to review advances in multimodal intelligence, large language models, reinforcement learning, federated learning, RIS-assisted control, Unmanned Aerial Vehicle (UAV) and vehicular networks, and AI-native network management, and analyze privacy, security, resilience, and sustainability as cross-cutting requirements of the full perception-reasoning-action loop. An audit of representative studies against nine agentic-specific evaluation criteria shows that no system reports more than one or two of them, exposing a gap between claimed and demonstrated agentic maturity. We identify open challenges in physical-to-semantic grounding, predictive world models, real-time agent-PHY interaction, safe tool use, heterogeneous multi-agent collaboration, benchmarking, and resource-efficient autonomy.

Kai Li, Conggai Li, Sarah Ali Siddiqui +4

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg · Technology, CSIRO, Sydney, NSW, Australia · College of Computer, Qassim University, Buraydah, Saudi Arabia +1

Integrated Sensing and Communication AI Agent Evaluation

Aug 5, 2026cs.LG

A 6G Integrated Sensing and Communication Framework for Railway Intrusion Detection and Collision Prediction

Integrated Sensing and Communication (ISAC) combines sensing and communication to efficiently utilize wireless resources and is emerging as a key paradigm for next-generation wireless networks. By leveraging the wide bandwidth, high frequencies, and massive antenna arrays of 5G-Advanced and 6G systems, ISAC enables physical-layer sensing using Channel State Information (CSI). The 3rd Generation Partnership Project (3GPP) Release 19 identifies 32 potential ISAC use cases, with particular emphasis on detecting and tracking moving objects. In this work, we address the Sensing for Railway Intrusion Detection use case, where intruders, including wildlife, entering a railway track can pose serious collision risks. We generated 22,695 CSI matrices with corresponding ground truth using a 3D-rendered railway environment and the Sionna radio simulator. We developed a machine learning model combining a three-dimensional Convolutional Neural Network (3D CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network to detect intruders in the track danger zone and estimate their real-time position relative to the train, velocity, and time to collision. On synthetic CSI data, the model achieves 99.57% intruder-detection accuracy on a balanced test set and a combined Mean Absolute Error (MAE) of 0.4240 for position, velocity, and time-to-collision prediction. These results demonstrate the potential of CSI-based ISAC sensing with machine learning for reliable railway intrusion detection. The complete codebase for CSI generation, preprocessing, and model development is publicly available at https://github.com/EdgeIntelligenceLab/6g-isac-railway-intrusion-detection.

Ajeet Kumar Yadav, Sankaran Balasubramaniam, Aritra Chatterjee +3

Department of Cyber Physical Systems, Indian Institute of Science, Bengaluru, India · Nokia Solutions and Networks, Bengaluru, India · Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India

Integrated Sensing and Communication Intelligent Transportation Systems

Aug 4, 2026eess.SP

Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks

Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference, creating a need for compact, task-oriented feedback on the SE-SF interface. Forwarding the raw channel frequency response or full per-link delay-Doppler-azimuth-elevation (DDAE) tensor is prohibitively expensive, while peak-only reporting discards target-discriminative structure under clutter. We propose a learning-based coarse-to-fine sensing pipeline with candidate-latent feedback for single-target estimation. At the SE, a lightweight convolutional scorer produces a dense delay-Doppler proposal map from pilot-based OFDM channel estimates, and a learned encoder constructs K compact C-dimensional candidate tokens by fusing per-candidate azimuth-elevation patches, normalized position, and confidence cues. The latents are uniformly quantized post-training to b bits and transmitted under a finite budget B_fb = bKC + 18K + 16 bits to the SF, which performs cross-candidate refinement, reranking, and joint four-parameter estimation. On a ray-traced urban scene with static and dynamic clutter, three operating points in the (K, C, b) design space achieve 96.33-98.88% detection at 107-806 bytes per coherent processing interval, compression ratios of 1.2-9.2 x 10^4 over the 8-bit DDAE magnitude tensor, reducing the SE-SF interface from multi-Gbit/s to sub-Mbit/s rates. Cross-scene evaluation on an independent campus-scale environment achieves 98.79-99.50% detection and at-or-better angular accuracy without retraining, indicating that the learned representation captures target-relevant structure that transports across scenes of comparable or lower clutter density.

Shiv Shankar, Radha Krishna Ganti, J Klutto Milleth

Indian Institute of Technology (IIT), Madras, Chennai India · Centre of Excellence in Wireless Technology (CEWiT), Chennai India

Integrated Sensing and Communication

Jul 21, 2026cs.IT

CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System

In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cramér-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.

Yi Yang, Qianqian Zhang, Huaxia Wang

Department of Electrical and Computer Engineering, Rowan University, NJ, USA

Trajectory Optimization Integrated Sensing and Communication Wireless Communications UAV Trajectory Optimization

Jul 16, 2026cs.RO

Goal-Oriented Semantic Communication for Distributed ISAC-Enabled Vehicle Coordination

Vehicle coordination at unsignalized intersections relies on accurate real-time vehicle state acquisition and reliable command-and-control (C&C) signal delivery. However, existing studies typically treat sensing, communication, and control separately, which may lead to redundant transmissions, outdated state information, and unreliable vehicle coordination. In this paper, we investigate a new scenario of distributed integrated sensing and communication (ISAC)-enabled vehicle coordination at intersections, where multiple roadside units (RSUs) collaboratively transmit sensing signals for vehicle state acquisition and C&C signals for vehicle movement control under the management of a central base station (BS). To improve signaling efficiency, we propose a unified goal-oriented semantic communication (GSC) framework, which transmits sensing and C&C signals only when they are semantically important for improving intersection traffic throughput. Specifically, an extended Kalman filter (EKF) is adopted to predict vehicle states and fuse distributed sensing measurements. A masked hybrid proximal policy optimization (MHPPO) framework is then developed to jointly determine sensing transmission decisions, C&C transmission decisions, and C&C signal contents based on a value-of-information (VoI) reward. Furthermore, we propose an uncertainty-aware transmission design (UTD), including robust beamforming and VoI-based time-division power allocation, to improve sensing and communication reliability under vehicle state uncertainty and inter-RSU interference. Simulation results show that our proposed framework achieves 100% collision-free vehicle coordination with significantly reduced signaling overhead compared with predictive ISAC baselines adapted from state-of-the-art related studies and several ablation baselines.

Wenjie Liu, Yansha Deng

Department of Engineering, King’s College London, Strand, London WC2R 2LS, U.K.

Multi-Agent Coordination Integrated Sensing and Communication Intelligent Transportation Systems Semantic Communication Task-Oriented Semantic Communication

Jul 14, 2026cs.IT

Robust Design of Integrated Sensing and Communication in LEO Satellite Systems

With the growing demand for satellite sensing and communication, the limited wireless resources are difficult to support multiple satellite systems. Therefore, it is desired to investigate integrated sensing and communication (ISAC) in low Earth orbit (LEO) satellite systems to enable multi-functionality within a single satellite, thereby saving both spectrum and orbital resources. In this paper, a framework for ISAC in LEO satellite systems is established, where a satellite can simultaneously sense multiple targets and serve multiple communication users (CUs) over the same spectrum. Considering the limited onboard energy of satellite, a novel robust beamforming design algorithm is developed with the goal of minimizing total transmit power while satisfying the mean squared error (MSE) requirements for sensing and signal-to-interference-plus-noise ratio (SINR) requirements for communication in presence of channel phase uncertainty which exacerbates the cross-functional interference. According to theoretical analysis, the proposed algorithm for ISAC in LEO satellite systems is effective. Moreover, extensive simulations confirm the superiority of the proposed algorithm over baselines.

Hezhen Yang, Xiaoming Chen, Qi Wang

College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Integrated Sensing and Communication Robust Optimization Wireless Communications LEO Satellite Networks Wireless Resource Allocation

Jul 11, 2026cs.NI

CSI-Assisted Edge SLAM Testbed Platform for 5G Connected Unmanned Autonomous Vehicles

The evolution from 5G towards 6G reinforces interest in connected robotics, where mobile robots offload compute-intensive tasks to edge servers over ultra-reliable low-latency communication (URLLC) links. Simultaneous localization and mapping (SLAM), a fundamental yet demanding robotics function, is increasingly considered for edge deployment within mobile edge computing (MEC) frameworks. In parallel, integrated sensing and communications (ISAC) enables the use of radio channel information, such as channel state information (CSI), as an additional sensing modality in radio-based SLAM. In this paper, we design and implement a CSI-assisted Edge SLAM testbed integrating a custom unmanned ground vehicle (UGV), a ROS2-based SLAM framework, and a 5G Open Radio Access Network (O-RAN) system. The proposed architecture provides an end-to-end, cross-layer view of ROS2 sensor data streaming over 5G, explicitly enabling CSI exposure and integration into the SLAM pipeline. We analyze ROS2 DDS communication, RTPS packetization, and 5G user-plane transport, and discuss mechanisms for CSI extraction and delivery via O-RAN components. The platform enables realistic experimentation with communication-aware SLAM and reveals key challenges related to latency, data streaming, synchronization, and cross-system integration, providing insights for future 6G-enabled robotic platforms.

Boris Radovanovic, Sasa Talosi, Srdjan Sobot +1

Faculty of Technical Sciences, University of Novi Sad, Serbia

Simultaneous Localization and Mapping Integrated Sensing and Communication Edge Computing Wireless Communications

Jul 7, 2026cs.IT

6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection

In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment. This leads to the concept of integrated sensing and communication (ISAC), where the same wireless infrastructure is used for both communication and environmental sensing. Thus, ISAC enables the system to transmit information efficiently and observe and interpret channel variations and user behavior. Motivated by this capability, this work focuses on detecting an active attacker in an urban environment scenario, where the attacker intentionally manipulates beamforming directions to increase interference and mislead the transmitter into allocating the main lobe of beam toward itself instead of legitimate users. We apply game-theoretic approaches to model the interaction between legitimate users and the attacker, and integrate the resulting utility-based formulation into a reinforcement learning (RL) framework. Simulation results demonstrate that the proposed method effectively addresses security challenges in dynamic 6G ISAC systems.

Parmida Geranmayeh, Onur Günlü

Lehrstuhl für Nachrichtentechnik, Technische Universität Dortmund, Germany · †Information Theory and Security Laboratory (ITSL), Linköping University, Sweden

Integrated Sensing and Communication 6G Networks Wireless Communications

Jul 1, 2026cs.IT

QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks

Static radio maps provide location-dependent propagation priors, but they cannot capture short-term blockage caused by moving objects. Direct sensing-assisted beam prediction is also limited because a beam index discards SINR margins, MCS thresholds, BS alternatives, and communication-equivalent neighboring beams. This paper proposes QuaMoE-DRF, a quality-aware multimodal dynamic radio map forecasting framework for proactive beam and rate adaptation in ISAC networks. Its core representation is a future beam-SINR field. We show that the full multi-BS beam-SINR field is sufficient for finite-codebook threshold-rate BS, beam, MCS, goodput, and outage decisions. For tractability, the implemented model learns a compact reference-BS local field, complemented by BS-level supervision, joint BS--beam supervision, and latent network context; we also clarify that this compact projection alone is not sufficient for BS association. QuaMoE-DRF fuses static geometry, event-like motion observations, structured sensing states, and wireless history through a quality-aware mixture-of-experts module motivated by inverse-variance fusion under heteroscedastic modality errors. It jointly predicts communication-oriented map channels and proactive BS, beam, and MCS decisions. On a dynamic multi-BS and multi-UE urban benchmark, QuaMoE-DRF achieves 402.5 Mbps effective rate, 0.0417 outage probability, and 0.1836 map RMSE, improving the effective rate by 5.67% and reducing outage by 8.35% over the strongest completed effective-rate baseline. The current validation uses labels from a compact blockage/path-loss simulator, with ray tracing used only for calibration and sanity checking.

Zhihan Zeng, Kaihe Wang, Zhongpei Zhang +1

National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China · University of Electronic Science and Technology of China, Chengdu 611731, China · College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China +2

Integrated Sensing and Communication Wireless Communications Radio Propagation Modeling Wireless Resource Allocation Spatiotemporal Forecasting Multimodal Fusion

Jun 22, 2026eess.SP

Integrated Sensing and Communications for Real-time Avatar Control in XR over 5G

Extended Reality (XR) presents a challenging use case for 5G and 6G networks, requiring high data-rates and lowlatency communication to deliver a truly immersive experience. Moreover, in order to seamlessly translate physical actions to the virtual world, accurate gesture recognition and pose estimation are required. Current XR interaction solutions based on handheld controllers and cameras cannot easily capture full-body poses, inhibit the free use of hands, and require good visibility and a clear line of sight. In this work, we propose a multimodal sensing architecture for XR that combines 5G MillimeterWave (mmWave) Integrated sensing and communication (ISAC) and surface electromyography (sEMG) signals. 5G mmWave ISAC cannot only be used to deliver content wirelessly to the Head-mounted display (HMD), but also the same communication signals can be used to derive coarse body-level gestures and poses of the user, to support real-time avatar control. For fine-grained finger-level gestures, our architecture leverages lightweight sEMG sensors that capture forearm muscle activity. To illustrate the need of both modalities, we present evaluations of both sensing technologies. At the body level (5G), our architecture relies on power-per-beam-pair (PPBP), which can be computed from standard beam management or beam sweeping procedures of the 5G NR standard. PPBP-based sensing achieves 82.2

\pm

5.9% average accuracy when evaluated on users not seen during training. For fine-grained finger-level interactions, we show that surface electromyography (sEMG) carries strong discriminative information achieving consistent promising performance across different movement settings. Thus, combining the two modalities enables multi-scale gesture recognition, at the body level via existing 5G signals and finger level via lightweight sEMG sensors, forming a complete XR framework.

Nabeel Nisar Bhat, Javad Sameri, Rreze Halili +3

IDLab, University of Antwerp - imec, Antwerp, Belgium · IDLab, Ghent University - imec, Ghent, Belgium · Light and Lighting Technology Lab, KU Leuven, Ghent, Belgium

Integrated Sensing and Communication Wireless Communications Gesture Recognition Multimodal Sensor Fusion

Jun 22, 2026eess.SP

AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey

Zero-energy Internet of Things (IoT) enables passive or near-passive devices to operate on harvested energy rather than batteries. Backscatter communication (BackCom) supports this vision by enabling tags to transmit data via reflection and modulation of incident RF signals, but it suffers from weak reflections, double-path loss, limited coverage, direct-link interference, and dependence on external RF sources. Unmanned aerial vehicles (UAVs) can mitigate these limitations by acting as mobile carrier emitters, data collectors, relays, aerial receivers, mobile anchors, sensing platforms, and edge-intelligence nodes. Integrated sensing and communication (ISAC) further enables the sharing of wireless resources for data transmission, localization, target sensing, and environmental awareness. This article surveys RF-based AI-empowered UAV-assisted backscatter localization and ISAC for zero-energy IoT. It reviews enabling technologies, presents a structured PRISMA-informed methodology, and develops a unified taxonomy covering network architectures, UAV roles, backscatter modes, RF sources, localization and sensing functions, AI techniques, and performance metrics. It also discusses UAV-assisted BackCom, passive localization, ISAC-enabled UAV-backscatter systems, and AI-driven optimization through comparative tables, quantitative trend analysis, coverage evaluation, and tutorial-style numerical illustrations. Finally, it identifies open challenges and future directions in realistic channel modeling, energy-neutral operation, benchmarking, reproducibility, scalable and trustworthy AI, security, privacy, hardware validation, and integration with RIS, MEC, digital twins, and 6G technologies.

Ruhul Amin Khalil

Engineering Requirements Unit (ERU), College of Engineering, United Arab Emirates University, Al Ain 15551, UAE

Integrated Sensing and Communication Internet of Things Wireless Communications

Jun 20, 2026cs.CV

Resolving Multi-Target Association in OFDM-based ISAC via Vision-aided Multi-Modal Learning

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems commonly extract target parameters by peak-searching a delay-Doppler map (DDM) constructed from reflected pilots. In multi-target scenarios, this results in ambiguity: the DDM does not reveal which physical target produced which peak, and two targets within the same delay-Doppler resolution cell cannot be separated. We propose a vision-assisted OFDM-ISAC framework that resolves both limitations by fusing wireless and visual modalities. The transmitter encodes an onboard street-view image with deep joint source-channel coding (DeepJSCC) and transmits it over the same OFDM waveform used for sensing; the receiver reconstructs the image, runs a fine-tuned YOLOv5 detector and fuses the resulting per-target features (bounding-box coordinates and class labels) with the DDM and transmitter-receiver geometry through a learned multi-modal network. To stabilize training of the high dimensional delay and Doppler classifiers, we introduce a Kullback Leibler loss against triangular soft labels centered on the ground-truth bin. On a Blender-rendered vehicular testbed, the proposed framework achieves a 16 cm localization root mean square error (RMSE) and a 10.8 ns delay RMSE. An ablation study confirms that removing the visual modality causes a 60x degradation in localization. These results highlight the potential of vision to overcome the data-association and resolution limits of single-modality ISAC.

Meng Hua, Chenghong Bian, Deniz Gunduz

Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. · Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong.

Integrated Sensing and Communication Wireless Communications Multimodal Sensor Fusion

Jun 5, 2026stat.AP

An Integrated Roadside Sensing and Communication Framework for Vulnerable Road User Safety at Signalized Intersections

Vulnerable road users (VRUs) account for approximately half of urban traffic deaths globally, with intersections concentrating a disproportionate share of these casualties. Recent reviews of sensing technology for VRU protection have cataloged dozens of single-sensor and dual-sensor deployments, yet none of the surveyed systems couples multi-modal sensing with edge-side near-miss analytics and bidirectional vehicle-to-everything (V2X) and pedestrian-to-everything (P2X) messaging in a single intersection cabinet. This paper presents an integrated framework for VRU protection at signalized intersections, combining LiDAR, radar, RGB camera, and thermal camera at the perception layer, edge-based prediction and surrogate-safety analytics at the computation layer, V2X and P2X messaging at the communication layer, and adaptive signal control at the actuation layer. The framework is grounded in an empirical case study using R-LiViT, the first publicly released roadside LiDAR-Visual-Thermal dataset, which provides 200 multi-modal sequences and 2,400 annotated RGB-T frames at three German intersections. Analysis of 53,319 detection annotations reveals that VRUs comprise approximately 49% of all road-user observations, that day-to-night density drops by 38% for pedestrians and 45% for vehicles while the night distribution shows a higher close-proximity share, that per-frame close-proximity event counts vary approximately 10-fold across the eight unique locations at three intersections, and that 83% of pedestrian bounding boxes are small in image space, indicating that VRUs are typically far from any single sensor. These findings support multi-modal sensing, edge-side analytics, and adaptive context-sensitive deployment rather than uniform single-sensor solutions.

Parvez Anowar

PhD Student, Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Orlando, FL 32816, United States

Integrated Sensing and Communication Intelligent Transportation Systems Multimodal Sensor Fusion

Jun 3, 2026cs.IT

X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks

Uncrewed aerial vehicles (UAVs) are increasingly considered as aerial platforms capable of providing both sensing and communication services, representing a promising paradigm for intelligent transportation systems. This paper investigates the optimal time allocation for a UAV-enabled integrated sensing and communication (ISaC) system operating in the X-band for vehicular networks. We analyze the trade-off between sensing accuracy and communication performance under practical UAV constraints and fading effects, considering both single-shadowing and double-shadowing channel models. An optimization framework is developed to allocate time between sensing and communication while guaranteeing minimum communication rates and sufficient sensing reliability. Simulation results demonstrate adaptive time allocation strategies, highlighting how UAV-to-ground channel conditions and target distances influence the balance between sensing and communication in smart mobility scenarios.

Remon Polus, Soumaya Cherkaoui

Department of Computer and Software Engineering Polytechnique Montréal Montréal, Canada

Integrated Sensing and Communication Wireless Communications V2X Communication Wireless Resource Allocation

May 28, 2026cs.IT

CRB-Guided Framework Design and Resource Allocation for Indoor mmWave ISCC Systems

Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications, short-term human pose prediction facilitates continuous human tracking and resource allocation in advance. In this paper, we propose a Cramer-Rao bound (CRB) guided resource allocation framework for indoor mmWave ISCC systems to minimize the human pose prediction error under communication, latency, and energy constraints. We characterize the impact of sensing power on range-estimation uncertainty and point-cloud perturbation based on the CRB. To capture the impact of computation resources on prediction performance, we adopt an adaptive-depth Mamba-based pose prediction model, where lightweight prediction heads are attached after every layer to enable inference with different model depths. With this unified sensing-computation modeling, we establish a quantitative relationship among sensing power, model depth, and prediction error. Furthermore, we formulate a joint resource allocation problem to minimize the pose prediction error. To solve this problem efficiently, we develop an alternating optimization (AO)-based algorithm, where closed-form solutions are derived for the sensing power and model depth update steps. Simulation results show that the proposed scheme significantly reduces pose prediction error compared with baseline methods, validating its effectiveness for resource-constrained indoor human-centric ISCC systems.

Zhonghao Liu, Yahao Ding, Yinchao Yang +1

∗King’s College London

Integrated Sensing and Communication Wireless Resource Allocation Inference-Time Compute Allocation

May 28, 2026cs.IT

Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation

This paper investigates a multi-user indoor integrated sensing and communication (ISAC) system operating in the terahertz (THz) band, designed for adaptive communication based on gesture recognition. Leveraging gesture tracking through an extended Kalman filter (EKF), the access point (AP) dynamically adjusts resource allocation in response to detected gesture variations, thereby improving sensing accuracy. Based on the gesture recognition results, the AP further updates the communication quality requirements of different users, enabling efficient resource allocation. To this end, an adaptive joint optimization algorithm for power allocation and beamforming is developed to maximize the overall sensing signal-to-interference-plus-noise ratio (SINR) while satisfying the gesture-dependent communication quality of service (QoS) constraints. Simulation results demonstrate that the proposed method effectively responds to gesture dynamics, achieving superior sensing accuracy and communication performance compared with conventional single-variable optimization baselines.

Zhonghao Liu, Yinchao Yang, Yahao Ding +2

King’s College London

Integrated Sensing and Communication Wireless Communications Wireless Resource Allocation

May 21, 2026cs.AI

A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing

The detection of non-cooperative unmanned aerial vehicles (UAVs) presents significant challenges for Integrated Sensing and Communication (ISAC) systems due to the inherent limitations of single-modal perception and the competition for shared communication and sensing resources. To address these challenges, this paper proposes a novel Camera-Cooperative ISAC (CC-ISAC) framework that employs multimodal sensing to enable efficient UAV beam steering and tracking. The proposed framework employs cameras for coarse-grained airspace monitoring and utilizes ISAC for fine-grained, high-precision sensing, forming a complementary perception loop that enhances both sensing accuracy and resource efficiency. Within this framework, two key modules are developed: (1) a Vision-to-Echo Data Alignment (V2EDA) model that aligns visual and echo-domain features through cross-attention mechanisms, and (2) a Multimodal Fusion-Based Estimation (MMFE) model that integrates historical multimodal data with current observations for robust state estimation. Extensive evaluations conducted on the DeepSense 6G dataset demonstrate that the proposed framework achieves an average reduction of 71% in beam steering overhead and 1.69-11.15% in tracking overhead while maintaining high angular estimation accuracy. The CC-ISAC framework effectively mitigates resource contention between sensing and communication, enabling reliable UAV surveillance while freeing substantial system resources for additional communication tasks, thereby representing a practical advancement in ISAC system design.

Wenfeng Wu, Luping Xiang, Kun Yang

State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, 210008, China · Institute of Intelligent Networks and Communications (NINE) and School of Intelligent Software and Engineering, Nanjing University (Suzhou Campus), Suzhou, 215163, China

Camera-Radar Fusion Integrated Sensing and Communication Wireless Communications Aerial Object Detection

Apr 28, 2026cs.RO

Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC

This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but can only opportunistically reuse irregular 5G/6G reference signals (RSs). We show that the velocity profile induced by such irregular time-domain patterns can be decomposed into a periodic-peak component and an amplitude-shaping (weighting) component. Leveraging this structure, we propose a multi-periodogram velocity estimation algorithm that is standard-compliant and does not require new sensing-dedicated RSs or 3GPP modifications. Simulation results demonstrate that, compared with conventional periodogram processing, the proposed method improves low-SNR robustness by achieving a 3 dB SNR gain at the 10% missed-detection rate and reducing false alarms by 51%.

Yi Geng, Pan Cao, Ting Zeng +1

Cictmobile, China · University of Hertfordshire, UK · Beihang University, China +1

Integrated Sensing and Communication Wireless Communications

Dec 17, 2025cs.AI

Agentic AI for ISAC: Analysis, Framework, and Case Study

Integrated sensing and communication (ISAC) has emerged as a key development direction in the sixth-generation (6G) era, which provides essential support for the collaborative sensing and communication of future intelligent networks. However, as wireless environments become increasingly dynamic and complex, ISAC systems require more intelligent processing and more autonomous operation to maintain efficiency and adaptability. Meanwhile, agentic artificial intelligence (AI) offers a feasible solution to address these challenges by enabling continuous perception-reasoning-action loops in dynamic environments to support intelligent, autonomous, and efficient operation for ISAC systems. As such, we delve into the application value and prospects of agentic AI in ISAC systems in this work. Firstly, we provide a comprehensive review of agentic AI and ISAC systems to demonstrate their key characteristics. Secondly, we show several common optimization approaches for ISAC systems and highlight the significant advantages of generative artificial intelligence (GenAI)-based agentic AI. Thirdly, we propose a novel agentic ISAC framework and prensent a case study to verify its superiority in optimizing ISAC performance. Finally, we clarify future research directions for agentic AI-based ISAC systems.

Wenwen Xie, Geng Sun, Chuang Zhang +2

College of Computer Science and Technology, Jilin University, Changchun 130012, China · Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea

Integrated Sensing and Communication Wireless Communications Agentic AI

Dec 14, 2025eess.SP

FuseFi: Combining Irregularly Sampled CSI from Diverse Communication Packets and Frequency Bands for Wi-Fi Sensing

Existing Wi-Fi sensing systems rely on injecting high-rate probing packets to extract channel state information (CSI), leading to communication degradation and limited deployment flexibility. Although Integrated Sensing and Communication (ISAC) is a promising direction, existing solutions still rely on auxiliary packet injection because they exploit only uniform CSI from a single frame type, discarding approximately 70% of naturally available packets. We present FuseFi, a novel Wi-Fi-based ISAC framework that directly exploits irregularly sampled CSI from diverse communication packets across multiple frequency bands, eliminating intrusive packet injection and introducing no sensing-specific communication overhead. FuseFi integrates a CSI sanitization pipeline to harmonize heterogeneous packets and remove burst-induced redundancy, together with a time-aware attention model that learns directly from non-uniform CSI sequences without resampling. We further introduce CommCSI-HAR, a new dataset with irregularly sampled CSI from real-world dual-band communication traffic. Extensive evaluations on this dataset and, separately, on four public sensing tasks across three benchmark datasets show that FuseFi achieves state-of-the-art accuracy with a compact model size, while fully preserving communication throughput.

Gaofeng Dong, Kang Yang, Mani Srivastava

Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA

Integrated Sensing and Communication Wireless Communications Wi-Fi Sensing