Papers

Field

Top tools here

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.

Open problems

Recently funded

Topics in this field

sensing communication · channel state information · mimo71 papers15 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

Coming with more sources: repos

Activity

Recent papers

All in Research →

arXiv cs.ITSep 22
Logit-Aware MIMO AirComp for Distributed Mixture-of-Experts LLM Inference over Wireless Edge Networks
Lyutianyang Zhang, Yunjian Jia, Liu Cao, Dengke Wang, Jinke Ren +1
Distributed mixture-of-experts (MoE) inference is a promising architecture for deploying large language models (LLMs) at wireless edge networks because sparse experts can be placed across coordinated base stations (BSs), while the anchor node and user equipment (UE) can offload LLM inference tasks to BSs. The communication bottleneck is the MoE aggregation, where the anchor BS must recover a weighted sum of selected expert outputs before each decoding step. Over-the-air computation (AirComp) is well matched to this operation because the wireless multiple-access channel naturally superposes simultaneous transmissions. However, conventional AirComp minimizes communication distortion, whereas MoE aggregation errors have unequal impact on LLM outputs. We propose a logit-aware MIMO AirComp framework that estimates local logit sensitivity as block-level weights and jointly optimizes receive combiners and BS precoders under per-BS power constraints. We also develop an alternating algorithm that protects decoding decisions from aggregation perturbations. Using OpenCompass, we evaluate Qwen3-30B-A3B-Instruct-2507-FP8 on GSM8K and ARC-Challenge. At 30 dB aggregation SNR, perturbed Qwen3…
PDF arXiv
arXiv eess.SPSep 22
Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models
Xiao Tang, Tong Hui, Chao Shen, Yichen Wang, Qinghe Du +2
The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challenge for information security. Conventionally optimization always requires recalculation and learning strategy often suffers poor generalization, which are thus incapable for the security provisioning with wide scenario coverage. In this paper, we propose an adaptive and robust learning framework that leverages a mixture-of-experts (MoE) architecture to achieve cross-scenario physical layer security guarantee. Specifically, we first select a few representative scenarios and establish the scenario-specific generative diffusion model (GDM)-based experts for secure transmission beamforming with artificial noise. The diffusion nature of experts learns the overall probability distribution of security strategy solution landscape and the Transformer-based denoising process enhances the ability to generalize across varying network configurations. Then, a lightweight gating network is constructed to identify the scenarios by engineering the channel features and select the most relevant experts. Finally, an attention-based combiner is introduced…
PDF arXiv
arXiv cs.CVSep 21
MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts
Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Paudel, Konrad Schindler, Luc Van Gool +1
Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring updates to be transmitted from centralized servers. We propose using this setup to obtain dense, specialized module coverage that adapts a fixed base model to specific spatiotemporal contexts, such as parking lots, gas stations, ferries, or construction sites. However, in order to reliably transmit these modules to the edge device, using TCP, UDP, and BTP over V2X, Wi-Fi, and 2G-5G hardware, we establish a strict limit of 14.6 KB per module to fit within the first TCP window and to minimize UDP/BTP fragmentation. We further introduce Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model. We validate MECAIL on D-RICO and ODinW-13, where it largely matches the performance of parameter-heavy approaches while enabling practical, bandwidth-efficient large-scale deployment. This…
PDF arXiv
arXiv cs.AISep 20
Echo State Network (ESN) for Signal Recovery in RF-Impaired IBFD MIMO Systems
Conrad Prisby, Siyao Li, Chengtao Xu, Thomas Yang
In-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems enable simultaneous transmission and reception on the same frequency band, improving spectral efficiency for next-generation wireless networks. However, IBFD-MIMO systems are susceptible to self-interference (SI), which may overpower signals of interest (SOI). In this scenario, blind source separation (BSS) algorithms can be adopted to remove SI and perform joint sensing and communication (JSAC), but BSS algorithms mostly assume an idealized linear and quasi-stationary signal model, which does not hold under realistic radio frequency (RF) impairments, such as I/Q imbalance, carrier frequency offset (CFO), phase noise, and power amplifier nonlinearity. This paper proposes a two-stage echo state network (ESN)-based scheme that is superior to BSS under these realistic conditions. A frozen ESN is trained offline to characterize the static SI path, while an adaptive ESN, updated online via recursive least squares, tracks the time-varying SOI path using sparse pilot symbols. We evaluate the proposed scheme's SOI recovery performance and acquisition speed with different block sizes, comparing it against other…
PDF arXiv
arXiv cs.CVSep 20
Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach
Can Zheng, Jiguang He, Guofa Cai, Henk Wymeersch, Merouane Debbah
Accurate multi-user localization is challenging in complex urban environments, where wireless measurements can become ambiguous under noise, blockage, and multipath, while visual observations provide complementary spatial context. This paper presents a vision-wireless fusion framework for multi-user localization using pilot-indexed channel state information (CSI). Orthogonal pilot indices preserve the identities of the communicating UEs in the CSI token sequence and localization outputs. The model encodes each pilot-indexed CSI observation as a query token and uses cross-attention to retrieve user-specific information from spatial visual memory. Self-attention among CSI tokens further captures inter-user interactions, while the resulting multimodal representations are used for user-wise localization. Experiments on different datasets show consistent improvements over model-based, CSI-only, and multimodal-fusion baselines. Further experiments evaluate the model under different wireless and visual conditions.
PDF arXiv
arXiv cs.LGSep 19
K-TRAIL: Simulator-Guided Generative Design of EM/RF Circuits
Piyush Saha, Evan Newell, Hanna O'Leary, Arun Natarajan, Alireza Aghasi
Inverse design of RF and electromagnetic (EM) circuits is challenging because the relationship between circuit layout and electrical response is non-unique, and full-wave simulation is computationally expensive. This paper presents K-TRAIL, a simulator-guided generative framework for automated EM/RF circuit synthesis. K-TRAIL combines diffusion-based layout generation with derivative-free ensemble Kalman guidance, allowing feedback from a black-box EM simulator to refine candidate layouts during generation without requiring adjoint sensitivities or differentiable solver models. The framework supports both synthesis from prescribed S-parameter responses and synthesis directly from RF performance constraints. Experiments on multi-layer RFIC structures show that simulator-guided generation improves agreement with target responses and can identify structurally distinct layouts that satisfy circuit-level design requirements. The proposed approach provides a practical path toward generative, verification-aware RF circuit design while retaining the flexibility to explore diverse layout topologies.
PDF arXiv

Papers

Topics in this field

sensing communication · channel state information · mimo71 papers15 this week

Recent papers

All in Research →

arXiv cs.ITSep 22
Logit-Aware MIMO AirComp for Distributed Mixture-of-Experts LLM Inference over Wireless Edge Networks
Lyutianyang Zhang, Yunjian Jia, Liu Cao, Dengke Wang, Jinke Ren +1
Distributed mixture-of-experts (MoE) inference is a promising architecture for deploying large language models (LLMs) at wireless edge networks because sparse experts can be placed across coordinated base stations (BSs), while the anchor node and user equipment (UE) can offload LLM inference tasks to BSs. The communication bottleneck is the MoE aggregation, where the anchor BS must recover a weighted sum of selected expert outputs before each decoding step. Over-the-air computation (AirComp) is well matched to this operation because the wireless multiple-access channel naturally superposes simultaneous transmissions. However, conventional AirComp minimizes communication distortion, whereas MoE aggregation errors have unequal impact on LLM outputs. We propose a logit-aware MIMO AirComp framework that estimates local logit sensitivity as block-level weights and jointly optimizes receive combiners and BS precoders under per-BS power constraints. We also develop an alternating algorithm that protects decoding decisions from aggregation perturbations. Using OpenCompass, we evaluate Qwen3-30B-A3B-Instruct-2507-FP8 on GSM8K and ARC-Challenge. At 30 dB aggregation SNR, perturbed Qwen3…
PDF arXiv
arXiv eess.SPSep 22
Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models
Xiao Tang, Tong Hui, Chao Shen, Yichen Wang, Qinghe Du +2
The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challenge for information security. Conventionally optimization always requires recalculation and learning strategy often suffers poor generalization, which are thus incapable for the security provisioning with wide scenario coverage. In this paper, we propose an adaptive and robust learning framework that leverages a mixture-of-experts (MoE) architecture to achieve cross-scenario physical layer security guarantee. Specifically, we first select a few representative scenarios and establish the scenario-specific generative diffusion model (GDM)-based experts for secure transmission beamforming with artificial noise. The diffusion nature of experts learns the overall probability distribution of security strategy solution landscape and the Transformer-based denoising process enhances the ability to generalize across varying network configurations. Then, a lightweight gating network is constructed to identify the scenarios by engineering the channel features and select the most relevant experts. Finally, an attention-based combiner is introduced…
PDF arXiv
arXiv cs.CVSep 21
MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts
Matthias Neuwirth-Trapp, Maarten Bieshaar, Danda Paudel, Konrad Schindler, Luc Van Gool +1
Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring updates to be transmitted from centralized servers. We propose using this setup to obtain dense, specialized module coverage that adapts a fixed base model to specific spatiotemporal contexts, such as parking lots, gas stations, ferries, or construction sites. However, in order to reliably transmit these modules to the edge device, using TCP, UDP, and BTP over V2X, Wi-Fi, and 2G-5G hardware, we establish a strict limit of 14.6 KB per module to fit within the first TCP window and to minimize UDP/BTP fragmentation. We further introduce Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model. We validate MECAIL on D-RICO and ODinW-13, where it largely matches the performance of parameter-heavy approaches while enabling practical, bandwidth-efficient large-scale deployment. This…
PDF arXiv
arXiv cs.AISep 20
Echo State Network (ESN) for Signal Recovery in RF-Impaired IBFD MIMO Systems
Conrad Prisby, Siyao Li, Chengtao Xu, Thomas Yang
In-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems enable simultaneous transmission and reception on the same frequency band, improving spectral efficiency for next-generation wireless networks. However, IBFD-MIMO systems are susceptible to self-interference (SI), which may overpower signals of interest (SOI). In this scenario, blind source separation (BSS) algorithms can be adopted to remove SI and perform joint sensing and communication (JSAC), but BSS algorithms mostly assume an idealized linear and quasi-stationary signal model, which does not hold under realistic radio frequency (RF) impairments, such as I/Q imbalance, carrier frequency offset (CFO), phase noise, and power amplifier nonlinearity. This paper proposes a two-stage echo state network (ESN)-based scheme that is superior to BSS under these realistic conditions. A frozen ESN is trained offline to characterize the static SI path, while an adaptive ESN, updated online via recursive least squares, tracks the time-varying SOI path using sparse pilot symbols. We evaluate the proposed scheme's SOI recovery performance and acquisition speed with different block sizes, comparing it against other…
PDF arXiv
arXiv cs.CVSep 20
Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach
Can Zheng, Jiguang He, Guofa Cai, Henk Wymeersch, Merouane Debbah
Accurate multi-user localization is challenging in complex urban environments, where wireless measurements can become ambiguous under noise, blockage, and multipath, while visual observations provide complementary spatial context. This paper presents a vision-wireless fusion framework for multi-user localization using pilot-indexed channel state information (CSI). Orthogonal pilot indices preserve the identities of the communicating UEs in the CSI token sequence and localization outputs. The model encodes each pilot-indexed CSI observation as a query token and uses cross-attention to retrieve user-specific information from spatial visual memory. Self-attention among CSI tokens further captures inter-user interactions, while the resulting multimodal representations are used for user-wise localization. Experiments on different datasets show consistent improvements over model-based, CSI-only, and multimodal-fusion baselines. Further experiments evaluate the model under different wireless and visual conditions.
PDF arXiv
arXiv cs.LGSep 19
K-TRAIL: Simulator-Guided Generative Design of EM/RF Circuits
Piyush Saha, Evan Newell, Hanna O'Leary, Arun Natarajan, Alireza Aghasi
Inverse design of RF and electromagnetic (EM) circuits is challenging because the relationship between circuit layout and electrical response is non-unique, and full-wave simulation is computationally expensive. This paper presents K-TRAIL, a simulator-guided generative framework for automated EM/RF circuit synthesis. K-TRAIL combines diffusion-based layout generation with derivative-free ensemble Kalman guidance, allowing feedback from a black-box EM simulator to refine candidate layouts during generation without requiring adjoint sensitivities or differentiable solver models. The framework supports both synthesis from prescribed S-parameter responses and synthesis directly from RF performance constraints. Experiments on multi-layer RFIC structures show that simulator-guided generation improves agreement with target responses and can identify structurally distinct layouts that satisfy circuit-level design requirements. The proposed approach provides a practical path toward generative, verification-aware RF circuit design while retaining the flexibility to explore diverse layout topologies.
PDF arXiv

Active contributors · 90 days

Activity · 12 weeks

Coming with more sources: repos

Fields · AI · updated 4m ago

Sensing communication

Found by the topic model: sensing communication.

15papers this week0.0% vs last week

All papers · 71 With code · 0

Newest Most relevant

65 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.