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arXiv cs.LGNov 8
CHANNELS4WORLD: An Environment-Scale Dataset for Wireless Foundation Models
Joonkyu Jang, Hyung-Joo Moon, Kwang Soon Kim, Seong-Lyun Kim, Jakob Hoydis +2
Wireless foundation models learn reusable representations across tasks, configurations, and deployment environments. Their pretraining requires environmental breadth, yet wireless datasets often sample few sites densely. We introduce Channels4World, a scene-centered dataset of 100,000 distinct outdoor RF scenes and a procedural method for generating them. Each scene links a generated environment and RF material assignments to 32 base-station (BS) positions, 16,384 UE positions, and aligned variable-length multipath and ordered interaction records at 3.5 and 7~GHz. The corpus comprises 52.43 billion physical BS-UE pairs, 104.86 billion frequency-specific evaluations, and a 29.14-TB packaged payload. Scene identity aligns geometry, placements, channels, and interactions across frequencies, allowing environmental breadth and within-scene observation density to vary independently. The public release offers predefined subsets at multiple scales and separate access to corpus components. A deterministic 90K/10K scene-level train-test split keeps both frequencies and all scene components together. We characterize morphology, RF materials, and transceiver placement across three generation…
PDF arXiv
arXiv cs.NINov 8
Confidence-driven adaptive semantic transmission for task-oriented communication
Suman Das, Prajnyajit Mohanty, Siddhartha S. Borkotoky
Image-based remote monitoring over wireless networks faces a trade-off between communication cost and classification reliability. Transmitting complete images is communication-intensive, whereas relying solely on lightweight edge classifiers may compromise accuracy. To address this challenge, we propose Confidence-Driven Adaptation (CDA) for image transmission and classification. An edge device (ED) captures images and applies a lightweight classifier before transmission to a remote server. Based on its classification confidence, the ED selects among three transmission modes with varying levels of information: a short text string reporting its predicted class when confidence is high, a compressed Region of Interest (ROI) at intermediate confidence levels, or an uncompressed ROI when confidence is low. The server accepts the predicted class directly in the first case and performs classification using a more powerful classifier in the other two cases. Numerical evaluations under bandwidth-limited, lossy links and latency-constrained settings demonstrate that CDA achieves high task accuracy at the receiver compared with non-adaptive baselines that transmit only the edge-classifier…
PDF arXiv
arXiv cs.AINov 7
Receiver-Resolution APS-PDP Maps for Wireless Channels: Benchmarking and Coarse-to-Fine Modeling
Peilin Zheng, Haozhe Jia, Xiucheng Wang, Wenshuo Chen, Nan Cheng
Angle Power Spectrum (APS) and Power Delay Profile (PDP) are receiver-facing spectra that govern beam selection and wideband equalization. Most ray-tracing benchmarks provide supervision as variable-size multipath component lists. This format does not match practical receivers, whose angular and delay resolutions are limited by array aperture and system bandwidth. As a result, learning targets are ambiguous and evaluations can change with reconstruction conventions. We introduce UrbanADPSMap, a benchmark that converts ray-traced supervision into fixed-grid APS and PDP targets by explicitly modeling bandwidth-limited and aperture-limited response kernels. By fixing the receiver-domain kernels, discretization, and validity mask, UrbanADPSMap removes reconstruction-induced target ambiguity and stabilizes downstream evaluation across methods. Built on this benchmark, we propose SpecFlowAR, a coarse-to-fine generator based on multi-scale autoregressive flow matching. The model first captures coarse dispersion structure and then refines beam-relevant peaks and low-power tails. The number of function evaluations controls the sampling budget, enabling latency and quality to be adjusted…
PDF arXiv
arXiv cs.AINov 7
MambaLoc: Lightweight Indoor Localization Using Cross-Modal Knowledge Distillation
Mohab Bahnassy, Omar Saqr, Hamada Rizk, Moustafa Youssef
Accurate indoor localization on resource-constrained edge devices remains challenging due to the tension between modeling fidelity and computational efficiency. Wi-Fi Channel State Information (CSI) enables low-cost deployment using existing Wi-Fi infrastructure but is heavily distorted by multipath propagation. Transformer-based solutions mitigate these effects but their quadratic complexity limits real-time edge deployment. Ultra-Wideband (UWB) provides geometrically precise ranging yet requires dedicated infrastructure. We present MambaLoc, a cross-modal distillation framework that improves the accuracy-efficiency balance in CSI-based localization. MambaLoc uses a linear-time Selective State Space Model (Mamba) as a lightweight CSI student, guided during training by a UWB-Transformer teacher. A Feature Alignment Module bridges the representational gap between heterogeneous RF modalities, allowing the CSI model to inherit UWB-derived geometric structure without UWB hardware at inference. A probabilistic Gaussian Mixture regression head models multi-modal spatial ambiguity from multipath effects, avoiding the physically implausible averaging of deterministic regression.…
PDF arXiv
arXiv cs.AINov 6
Model-based learning for DoA estimation and array calibration via continuous sparse recovery
Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay, Matthieu Crussière, Christian Häger +3
Direction of arrival (DoA) estimation, a classical signal processing problem, has experienced renewed interest with the advent of integrated sensing and communication (ISAC). While decades of research have yielded highly accurate DoA estimators, hardware impairments have frequently been neglected, despite degrading both DoA estimation accuracy and downstream communication performance in ISAC systems. This work introduces a joint array calibration and DoA estimation method based on the simultaneous orthogonal matching pursuit (SOMP) algorithm, following the model-based machine learning paradigm. The proposed method enables the calibration of antenna arrays subject to severe gain-phase and antenna location impairments through projected gradient descent (PGD), with either supervised or unsupervised objectives. Additionally, DoA estimation is enhanced by extending SOMP to continuous dictionaries via PGD refinement steps. Simulation results demonstrate that the proposed method enables efficient hardware impairment compensation, even without labeled samples, while outperforming conventional sparse recovery and subspace-based DoA estimation baselines.
PDF arXiv
arXiv cs.LGNov 6
TOFS: Feature Selection with Swap Supervision for Semantic Communication over Fading Channels
Zhiyi Zhu, Eiji Takimoto, Patrick Finnerty, Chikara Ohta
Task-oriented semantic communication transmits task-relevant features to support receiver inference under limited communication resources and adverse channel conditions. For image classification, the usefulness of a feature depends on the other transmitted features and the distortion introduced by the wireless channel. Effective feature prioritization should therefore account for classification performance after transmission of the selected features. This paper proposes a Task-Oriented Feature Selection (\textit{TOFS}) framework and method, which evaluates feature replacements using receiver classification loss. Loss differences confirmed under independent transmission samples provide pairwise ranking supervision for a selector network. The trained selector predicts priorities in a single forward pass. CIFAR-10 simulations demonstrate improved classification accuracy over an importance-based feature selection baseline at the same transmitted feature count and average transmit power. The proposed \textit{TOFS} framework shows 84\% accuracy performance under Rayleigh fading and Gaussian noise, compared with the 68\% performance of a SOTA method.
PDF arXiv

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channel state information · mimo · channel state121 papers14 this week

Recent papers

All papers in Research

arXiv cs.LGNov 8
CHANNELS4WORLD: An Environment-Scale Dataset for Wireless Foundation Models
Joonkyu Jang, Hyung-Joo Moon, Kwang Soon Kim, Seong-Lyun Kim, Jakob Hoydis +2
Wireless foundation models learn reusable representations across tasks, configurations, and deployment environments. Their pretraining requires environmental breadth, yet wireless datasets often sample few sites densely. We introduce Channels4World, a scene-centered dataset of 100,000 distinct outdoor RF scenes and a procedural method for generating them. Each scene links a generated environment and RF material assignments to 32 base-station (BS) positions, 16,384 UE positions, and aligned variable-length multipath and ordered interaction records at 3.5 and 7~GHz. The corpus comprises 52.43 billion physical BS-UE pairs, 104.86 billion frequency-specific evaluations, and a 29.14-TB packaged payload. Scene identity aligns geometry, placements, channels, and interactions across frequencies, allowing environmental breadth and within-scene observation density to vary independently. The public release offers predefined subsets at multiple scales and separate access to corpus components. A deterministic 90K/10K scene-level train-test split keeps both frequencies and all scene components together. We characterize morphology, RF materials, and transceiver placement across three generation…
PDF arXiv
arXiv cs.NINov 8
Confidence-driven adaptive semantic transmission for task-oriented communication
Suman Das, Prajnyajit Mohanty, Siddhartha S. Borkotoky
Image-based remote monitoring over wireless networks faces a trade-off between communication cost and classification reliability. Transmitting complete images is communication-intensive, whereas relying solely on lightweight edge classifiers may compromise accuracy. To address this challenge, we propose Confidence-Driven Adaptation (CDA) for image transmission and classification. An edge device (ED) captures images and applies a lightweight classifier before transmission to a remote server. Based on its classification confidence, the ED selects among three transmission modes with varying levels of information: a short text string reporting its predicted class when confidence is high, a compressed Region of Interest (ROI) at intermediate confidence levels, or an uncompressed ROI when confidence is low. The server accepts the predicted class directly in the first case and performs classification using a more powerful classifier in the other two cases. Numerical evaluations under bandwidth-limited, lossy links and latency-constrained settings demonstrate that CDA achieves high task accuracy at the receiver compared with non-adaptive baselines that transmit only the edge-classifier…
PDF arXiv
arXiv cs.AINov 7
Receiver-Resolution APS-PDP Maps for Wireless Channels: Benchmarking and Coarse-to-Fine Modeling
Peilin Zheng, Haozhe Jia, Xiucheng Wang, Wenshuo Chen, Nan Cheng
Angle Power Spectrum (APS) and Power Delay Profile (PDP) are receiver-facing spectra that govern beam selection and wideband equalization. Most ray-tracing benchmarks provide supervision as variable-size multipath component lists. This format does not match practical receivers, whose angular and delay resolutions are limited by array aperture and system bandwidth. As a result, learning targets are ambiguous and evaluations can change with reconstruction conventions. We introduce UrbanADPSMap, a benchmark that converts ray-traced supervision into fixed-grid APS and PDP targets by explicitly modeling bandwidth-limited and aperture-limited response kernels. By fixing the receiver-domain kernels, discretization, and validity mask, UrbanADPSMap removes reconstruction-induced target ambiguity and stabilizes downstream evaluation across methods. Built on this benchmark, we propose SpecFlowAR, a coarse-to-fine generator based on multi-scale autoregressive flow matching. The model first captures coarse dispersion structure and then refines beam-relevant peaks and low-power tails. The number of function evaluations controls the sampling budget, enabling latency and quality to be adjusted…
PDF arXiv
arXiv cs.AINov 7
MambaLoc: Lightweight Indoor Localization Using Cross-Modal Knowledge Distillation
Mohab Bahnassy, Omar Saqr, Hamada Rizk, Moustafa Youssef
Accurate indoor localization on resource-constrained edge devices remains challenging due to the tension between modeling fidelity and computational efficiency. Wi-Fi Channel State Information (CSI) enables low-cost deployment using existing Wi-Fi infrastructure but is heavily distorted by multipath propagation. Transformer-based solutions mitigate these effects but their quadratic complexity limits real-time edge deployment. Ultra-Wideband (UWB) provides geometrically precise ranging yet requires dedicated infrastructure. We present MambaLoc, a cross-modal distillation framework that improves the accuracy-efficiency balance in CSI-based localization. MambaLoc uses a linear-time Selective State Space Model (Mamba) as a lightweight CSI student, guided during training by a UWB-Transformer teacher. A Feature Alignment Module bridges the representational gap between heterogeneous RF modalities, allowing the CSI model to inherit UWB-derived geometric structure without UWB hardware at inference. A probabilistic Gaussian Mixture regression head models multi-modal spatial ambiguity from multipath effects, avoiding the physically implausible averaging of deterministic regression.…
PDF arXiv
arXiv cs.AINov 6
Model-based learning for DoA estimation and array calibration via continuous sparse recovery
Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay, Matthieu Crussière, Christian Häger +3
Direction of arrival (DoA) estimation, a classical signal processing problem, has experienced renewed interest with the advent of integrated sensing and communication (ISAC). While decades of research have yielded highly accurate DoA estimators, hardware impairments have frequently been neglected, despite degrading both DoA estimation accuracy and downstream communication performance in ISAC systems. This work introduces a joint array calibration and DoA estimation method based on the simultaneous orthogonal matching pursuit (SOMP) algorithm, following the model-based machine learning paradigm. The proposed method enables the calibration of antenna arrays subject to severe gain-phase and antenna location impairments through projected gradient descent (PGD), with either supervised or unsupervised objectives. Additionally, DoA estimation is enhanced by extending SOMP to continuous dictionaries via PGD refinement steps. Simulation results demonstrate that the proposed method enables efficient hardware impairment compensation, even without labeled samples, while outperforming conventional sparse recovery and subspace-based DoA estimation baselines.
PDF arXiv
arXiv cs.LGNov 6
TOFS: Feature Selection with Swap Supervision for Semantic Communication over Fading Channels
Zhiyi Zhu, Eiji Takimoto, Patrick Finnerty, Chikara Ohta
Task-oriented semantic communication transmits task-relevant features to support receiver inference under limited communication resources and adverse channel conditions. For image classification, the usefulness of a feature depends on the other transmitted features and the distortion introduced by the wireless channel. Effective feature prioritization should therefore account for classification performance after transmission of the selected features. This paper proposes a Task-Oriented Feature Selection (\textit{TOFS}) framework and method, which evaluates feature replacements using receiver classification loss. Loss differences confirmed under independent transmission samples provide pairwise ranking supervision for a selector network. The trained selector predicts priorities in a single forward pass. CIFAR-10 simulations demonstrate improved classification accuracy over an importance-based feature selection baseline at the same transmitted feature count and average transmit power. The proposed \textit{TOFS} framework shows 84\% accuracy performance under Rayleigh fading and Gaussian noise, compared with the 68\% performance of a SOTA method.
PDF arXiv

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