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Open problems in Sensing communication

Generalization to unseen attacks in multispectral iris PAD1 paper raises it · 1 addresses it
Current methods fail to reliably detect new presentation attack instruments not seen during training.
- WaveSpec: Multi-Spectral Iris Presentation Attack Detection via Wavelength-Conditioned Spectral Coherence Adaptationraises · addresses
Realistic benchmarks for indoor 5G localization1 paper raises it · 1 addresses it
Existing datasets do not capture severe multipath and NLOS conditions of industrial environments, limiting evaluation of AI methods.
- RAIL-5G: A Challenging Real-World Dataset and Benchmark for AI-Based 5G Indoor Localizationraises · addresses
Setup-agnostic channel charting for label-free indoor localization1 paper raises it · 1 addresses it
Channel charting methods often rely on setup-specific dissimilarity metrics and precomputed distance matrices, limiting scalability and transferability.
- ChronoChart: Setup-Agnostic Channel Charting for Label-Free Indoor Localizationraises · addresses
Reliable RF fingerprinting under domain shift1 paper raises it · 1 addresses it
RFF models degrade in performance when faced with changes in receiver, distance, or environment, and confidence metrics are insufficient for trustworthy deployment.
- TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shiftraises · addresses
Hardware-in-the-loop validation for wireless embedded systems1 paper raises it · 1 addresses it
LLM agents cannot directly verify wireless link functionality without hardware access, limiting their use in embedded development.
- LoopHIL: Looping Hardware into LLM-assisted Agents for Wireless Embedded Systemsraises · addresses

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sensing communication · channel state information · mimo70 papers14 this week

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arXiv cs.AIOct 6
Agentic Semantic Sensing for Resource-Adaptive AI-RAN
Zhongqin Wang, Xiaoqi Zhang, Nan Yang, Kai Wu, J. Andrew Zhang +1
Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information. Existing SemS formulations typically operate open loop: sensing configurations and observation schedules are fixed before inference and cannot respond to evolving task-level evidence. We propose Agentic SemS, a closed-loop framework for AI-enabled radio access networks (AI-RANs) that controls sensing within a communication-feasible profile set. A profile-conditioned causal Transformer updates the semantic belief from streaming observations, while key-value caching enables efficient state updates across profile changes without repeatedly processing the complete history. A semantic utility network estimates the task-level benefit of acquiring the next observation block under each feasible profile after accounting for sensing cost. The resulting continuation utilities jointly support next-profile selection and semantic early exit, adapting sensing configuration and duration to evolving evidence. The expected semantic gain is further related to conditional mutual information, providing a value-of-information interpretation of continued online sensing. Experiments on…
PDF arXiv
arXiv cs.LGOct 5
Trajectory-Guided Tokenization of Complex CSI for Wi-Fi Sensing
Ziyi Wang, Kenuo Xu, Jichu Jiang, Yumeng Yang, Zheng Chen +6
Wi-Fi channel state information (CSI) enables contactless presence detection and gesture recognition. Its high-dimensional complex-valued time series require input representations that preserve informative temporal variations during compression. We propose Trajectory-Guided Tokenization (TGT), which combines complex trajectory decomposition with asymmetric attention to construct compact continuous tokens. For each antenna link and subcarrier, an orthonormal Helmert transform decomposes short, ordered temporal patches into local-center and centered-trajectory coordinates. Keys are learned from the centered-trajectory coordinates, while values retain both components. Learnable queries aggregate subcarriers into frequency slots, which are fused into temporal tokens. Trained jointly from scratch, TGT with TokenMLP achieves the highest mean accuracy of 92.83% among all evaluated frontend-backend combinations on the self-collected dataset. Experiments on EHUNAM and Widar further support the applicability of TGT to cross-domain presence detection and gesture recognition.
PDF arXiv
arXiv eess.SPOct 5
Combining Improvements in Uplink AI-RAN
Petteri Kela, Dani Korpi, Mikko Honkala
One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network (RAN). While most physical-layer AI features have so far been evaluated in isolation using link-level simulations, their combined behavior in a realistic multi-cell, multi-UE deployment has remained largely unexplored. In this paper, we present system-level performance results when multiple uplink AI features are enabled together, achieved by integrating accurate link-level and system-level simulators. To infer state-of-the-art deep-learning-aided Multiple Input Multiple Output (MIMO) receivers under the dynamic allocations produced by a realistic uplink scheduler, we propose a mirrored data augmentation method that decouples receiver performance from scheduled allocation size. In addition to these Physical Layer (PHY) receiver features, we combine several recent advances in deep reinforcement learning to train uplink power control and link adaptation that outperform a heuristic baseline and further boost the gains obtainable from the AI receiver alone. The system-level results show that the combined AI features improve the mean…
PDF arXiv
arXiv cs.LGOct 4
Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design
Avi Epstein, Snir Nehemia, Haim Suchowski, Lior Wolf
Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction and inverse design. We present a mesh-native, physics-augmented graph-learning framework that treats radiation-pattern prediction as signal reconstruction on an irregular surface mesh. For the forward problem, a GPS graph transformer is trained with Physics-Augmented Intermediate Supervision (PAIS), an auxiliary node-level objective that predicts complex surface currents, the physical intermediate linking geometry to radiation. PAIS improves multiple GNN backbones at no inference-time cost, while shuffled-current and non-physical controls show the gain comes from physical correspondence. Direction-conditioned decoding and a differentiable radiation-integral consistency loss further exploit this structure. On an 80,000-sample CST benchmark, GPS+PAIS reaches MSE 0.17 / PSNR 19.67, generalizes to a PCA split, and transfers zero-shot to canonical patches. For inverse design, surrogate-filtered diffusion beats nearest-neighbor retrieval by 32% relative MSE.
PDF arXiv
arXiv cs.LGOct 4
SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems
Amr M. Zaki, Farhoud Jafari, Honggeun Ji, Komal Sarda, Marin Litoiu
Industrial Internet of Things (IIoT) systems generate high-dimensional sensor telemetry from interconnected components, where faults can propagate across the system. To address these challenges, we propose SFlexRCA (Scalable and Flexible Root Cause Analysis), a topology-free RCA framework designed for resource-constrained IIoT environments. SFlexRCA transforms multivariate telemetry into compact orthogonal representations and applies shared lightweight linear modeling, avoiding explicit graph construction, message passing, and per-variable or lag-specific parameter growth. We evaluate SFlexRCA on three publicly available IIoT datasets, BATADAL, SWaT, and WADI, spanning different numbers of monitored variables, temporal characteristics, and training-data regimes. SFlexRCA is compared with 10 statistical, causal, and non-causal baselines in terms of RCA accuracy, training efficiency, inference latency, and memory consumption. In addition, inference efficiency and memory consumption are evaluated on Raspberry Pi 3 and Raspberry Pi 5, while energy consumption is additionally measured on Raspberry Pi 5. We further investigate temporalcontext sensitivity, architectural and loss…
PDF arXiv
arXiv cs.LGOct 3
Multi-Agent Spectrum Sharing
Job Elliott, Graduate Student Member, IEEE, Justin G. Metcalf, Golnaz Habibi
This project explores how multiple cognitive radars can learn to share limited wireless spectrum with other radio users without interfering with one another. Using machine learning (ML), each device independently decides where and how widely to transmit within a fixed 100 MHz band. The system analyzes real or simulated signal activity to detect which parts of the spectrum are currently in use and which are open. Based on this information, the devices adapt their transmission choices to avoid crowded frequencies while making efficient use of available space. The goal is to develop a flexible, scalable approach to spectrum sharing that could support future wireless communication systems. Experimental results using both over-the-air software-defined radio (SDR) recordings and simulated environments demonstrate that the proposed meta-learning approach consistently balances competing objectives better than conventional reinforcement learning (RL) methods in multi-agent spectrum-sharing scenarios. Across five multi-agent benchmark environments, our proposed method achieved the highest average reward among the primary baseline algorithms while simultaneously maintaining low collision…
PDF arXiv

Papers

Topics in this field

sensing communication · channel state information · mimo70 papers14 this week

Recent papers

All in Research →

arXiv cs.AIOct 6
Agentic Semantic Sensing for Resource-Adaptive AI-RAN
Zhongqin Wang, Xiaoqi Zhang, Nan Yang, Kai Wu, J. Andrew Zhang +1
Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information. Existing SemS formulations typically operate open loop: sensing configurations and observation schedules are fixed before inference and cannot respond to evolving task-level evidence. We propose Agentic SemS, a closed-loop framework for AI-enabled radio access networks (AI-RANs) that controls sensing within a communication-feasible profile set. A profile-conditioned causal Transformer updates the semantic belief from streaming observations, while key-value caching enables efficient state updates across profile changes without repeatedly processing the complete history. A semantic utility network estimates the task-level benefit of acquiring the next observation block under each feasible profile after accounting for sensing cost. The resulting continuation utilities jointly support next-profile selection and semantic early exit, adapting sensing configuration and duration to evolving evidence. The expected semantic gain is further related to conditional mutual information, providing a value-of-information interpretation of continued online sensing. Experiments on…
PDF arXiv
arXiv cs.LGOct 5
Trajectory-Guided Tokenization of Complex CSI for Wi-Fi Sensing
Ziyi Wang, Kenuo Xu, Jichu Jiang, Yumeng Yang, Zheng Chen +6
Wi-Fi channel state information (CSI) enables contactless presence detection and gesture recognition. Its high-dimensional complex-valued time series require input representations that preserve informative temporal variations during compression. We propose Trajectory-Guided Tokenization (TGT), which combines complex trajectory decomposition with asymmetric attention to construct compact continuous tokens. For each antenna link and subcarrier, an orthonormal Helmert transform decomposes short, ordered temporal patches into local-center and centered-trajectory coordinates. Keys are learned from the centered-trajectory coordinates, while values retain both components. Learnable queries aggregate subcarriers into frequency slots, which are fused into temporal tokens. Trained jointly from scratch, TGT with TokenMLP achieves the highest mean accuracy of 92.83% among all evaluated frontend-backend combinations on the self-collected dataset. Experiments on EHUNAM and Widar further support the applicability of TGT to cross-domain presence detection and gesture recognition.
PDF arXiv
arXiv eess.SPOct 5
Combining Improvements in Uplink AI-RAN
Petteri Kela, Dani Korpi, Mikko Honkala
One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network (RAN). While most physical-layer AI features have so far been evaluated in isolation using link-level simulations, their combined behavior in a realistic multi-cell, multi-UE deployment has remained largely unexplored. In this paper, we present system-level performance results when multiple uplink AI features are enabled together, achieved by integrating accurate link-level and system-level simulators. To infer state-of-the-art deep-learning-aided Multiple Input Multiple Output (MIMO) receivers under the dynamic allocations produced by a realistic uplink scheduler, we propose a mirrored data augmentation method that decouples receiver performance from scheduled allocation size. In addition to these Physical Layer (PHY) receiver features, we combine several recent advances in deep reinforcement learning to train uplink power control and link adaptation that outperform a heuristic baseline and further boost the gains obtainable from the AI receiver alone. The system-level results show that the combined AI features improve the mean…
PDF arXiv
arXiv cs.LGOct 4
Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design
Avi Epstein, Snir Nehemia, Haim Suchowski, Lior Wolf
Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction and inverse design. We present a mesh-native, physics-augmented graph-learning framework that treats radiation-pattern prediction as signal reconstruction on an irregular surface mesh. For the forward problem, a GPS graph transformer is trained with Physics-Augmented Intermediate Supervision (PAIS), an auxiliary node-level objective that predicts complex surface currents, the physical intermediate linking geometry to radiation. PAIS improves multiple GNN backbones at no inference-time cost, while shuffled-current and non-physical controls show the gain comes from physical correspondence. Direction-conditioned decoding and a differentiable radiation-integral consistency loss further exploit this structure. On an 80,000-sample CST benchmark, GPS+PAIS reaches MSE 0.17 / PSNR 19.67, generalizes to a PCA split, and transfers zero-shot to canonical patches. For inverse design, surrogate-filtered diffusion beats nearest-neighbor retrieval by 32% relative MSE.
PDF arXiv
arXiv cs.LGOct 4
SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systems
Amr M. Zaki, Farhoud Jafari, Honggeun Ji, Komal Sarda, Marin Litoiu
Industrial Internet of Things (IIoT) systems generate high-dimensional sensor telemetry from interconnected components, where faults can propagate across the system. To address these challenges, we propose SFlexRCA (Scalable and Flexible Root Cause Analysis), a topology-free RCA framework designed for resource-constrained IIoT environments. SFlexRCA transforms multivariate telemetry into compact orthogonal representations and applies shared lightweight linear modeling, avoiding explicit graph construction, message passing, and per-variable or lag-specific parameter growth. We evaluate SFlexRCA on three publicly available IIoT datasets, BATADAL, SWaT, and WADI, spanning different numbers of monitored variables, temporal characteristics, and training-data regimes. SFlexRCA is compared with 10 statistical, causal, and non-causal baselines in terms of RCA accuracy, training efficiency, inference latency, and memory consumption. In addition, inference efficiency and memory consumption are evaluated on Raspberry Pi 3 and Raspberry Pi 5, while energy consumption is additionally measured on Raspberry Pi 5. We further investigate temporalcontext sensitivity, architectural and loss…
PDF arXiv
arXiv cs.LGOct 3
Multi-Agent Spectrum Sharing
Job Elliott, Graduate Student Member, IEEE, Justin G. Metcalf, Golnaz Habibi
This project explores how multiple cognitive radars can learn to share limited wireless spectrum with other radio users without interfering with one another. Using machine learning (ML), each device independently decides where and how widely to transmit within a fixed 100 MHz band. The system analyzes real or simulated signal activity to detect which parts of the spectrum are currently in use and which are open. Based on this information, the devices adapt their transmission choices to avoid crowded frequencies while making efficient use of available space. The goal is to develop a flexible, scalable approach to spectrum sharing that could support future wireless communication systems. Experimental results using both over-the-air software-defined radio (SDR) recordings and simulated environments demonstrate that the proposed meta-learning approach consistently balances competing objectives better than conventional reinforcement learning (RL) methods in multi-agent spectrum-sharing scenarios. Across five multi-agent benchmark environments, our proposed method achieved the highest average reward among the primary baseline algorithms while simultaneously maintaining low collision…
PDF arXiv

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 1h ago

Sensing communication

Found by the topic model: sensing communication.

14papers this week−12.5% vs last week

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64 papers this month

Open problems in Sensing communication

Generalization to unseen attacks in multispectral iris PAD1 paper raises it · 1 addresses it
Current methods fail to reliably detect new presentation attack instruments not seen during training.
- WaveSpec: Multi-Spectral Iris Presentation Attack Detection via Wavelength-Conditioned Spectral Coherence Adaptationraises · addresses
Realistic benchmarks for indoor 5G localization1 paper raises it · 1 addresses it
Existing datasets do not capture severe multipath and NLOS conditions of industrial environments, limiting evaluation of AI methods.
- RAIL-5G: A Challenging Real-World Dataset and Benchmark for AI-Based 5G Indoor Localizationraises · addresses
Setup-agnostic channel charting for label-free indoor localization1 paper raises it · 1 addresses it
Channel charting methods often rely on setup-specific dissimilarity metrics and precomputed distance matrices, limiting scalability and transferability.
- ChronoChart: Setup-Agnostic Channel Charting for Label-Free Indoor Localizationraises · addresses
Reliable RF fingerprinting under domain shift1 paper raises it · 1 addresses it
RFF models degrade in performance when faced with changes in receiver, distance, or environment, and confidence metrics are insufficient for trustworthy deployment.
- TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shiftraises · addresses
Hardware-in-the-loop validation for wireless embedded systems1 paper raises it · 1 addresses it
LLM agents cannot directly verify wireless link functionality without hardware access, limiting their use in embedded development.
- LoopHIL: Looping Hardware into LLM-assisted Agents for Wireless Embedded Systemsraises · addresses

Named by an AI from these papers' abstracts.