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  • arXiv eess.SPNov 12

    Measurement-Calibrated Synthetic Pretraining for Rapid Adaptation of UAV Channel Predictors

    Qiuheng Zhou, Wei Jiang, Donglin Wang, Hans D. Schotten

    Unmanned aerial vehicle (UAV) links can change faster than route-specific channel data can be collected, so a channel predictor must often be adapted from only a short target-flight calibration segment. This paper studies measurement-calibrated synthetic pretraining as an offline source prior for rapid adaptation of channel state information (CSI) predictors. The synthetic source is calibrated to flight-only power evolution, temporal shape variation, frequency selectivity, and phase disturbance, and is then combined with measured source-flight fine-tuning before target-flight adaptation. Under chronological held-out flight splits, synthetic-to-real (S2R) fine-tuning reduces the measured-data budget needed to reach useful prediction in the low-data regime. For 50 ms-ahead prediction, it crosses a latest-channel-estimate persistence baseline with 50 measured training windows, roughly 0.5 s of 10 ms-spaced supervised windows, whereas training from scratch requires 600 windows. A target-prefix adaptation study further shows that a lightweight gated recurrent unit predictor can reuse the pretrained source prior with one prediction-layer update epoch, positioning measurement-calibrated…

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  • arXiv cs.NINov 12

    Machine Learning for Scalable Multi-AP Coordination in IEEE 802.11bn Networks

    Maksymilian Wojnar, Szymon Szott

    As Wi-Fi deployments become denser, fully distributed random channel access no longer guarantees the required quality of service. The multi-access point coordination (MAPC) framework introduced in IEEE~802.11bn lets neighboring access points (APs) cooperate, but choosing which APs transmit together and how to configure them remains an open, computationally hard problem. We develop and validate a stack of machine learning (ML) coordination methods, progressing from lightweight hierarchical multi-armed bandits (H-MABs) to a generative pipeline built on graph neural networks (GNNs) and flow matching (FM). The methods are evaluated in simulation and on a hardware testbed, improving throughput over legacy access and scaling coordination to dozens of APs where analytical and search-based methods become intractable.

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  • arXiv eess.SPNov 11

    Reliable Near-Field Source Enumeration and Positioning Informed by Two-Stage MUSIC

    Jiaying Li, Haifeng Wen, Changsheng You, Yuanwei Liu, Hong Xing

    Accurate and reliable near-field multi-source localization is essential for location-aware processing in future wireless systems. However, coherent multipath propagation complicates the direct recovery of line-of-sight (LoS) source positions, while existing estimation methods generally lack explicit control of the risk of source underestimation and positioning uncertainty. In this paper, we investigate reliable source enumeration and positioning in near-field coherent multipath environments. Specifically, we develop a conformal risk control (CRC) based source enumeration scheme that calibrates the penalty coefficient of an eigenvalue-based criterion to control the expected miss-detection ratio at a prescribed level. We further propose MUSIC-Net, a model-based deep learning framework informed by two-stage MUltiple SIgnal Classification (MUSIC), which learns the LoS-related subspace and surrogate distances through subspace-distance-based training, thereby enabling direct source positioning without estimating non-line-of-sight (NLoS) path parameters or performing subsequent path association. To quantify the uncertainty of the estimated positions, we develop a split conformal…

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  • arXiv cs.LGNov 11

    GLCR: Global-Local Collaborative Routing for Multi-Task Electromagnetic Signal Processing

    Fang Lu, Yao Lu, Zhuangzhi Chen, Sicheng Zhang, Shanqing Yu +3

    Deep neural networks for electromagnetic signal processing are typically developed as single-task experts with fixed inputs and objectives, limiting cross-task transferability. Electromagnetic foundation models offer broader task coverage but incur substantial pretraining and inference costs. To balance accuracy and efficiency, we adopt model routing to dynamically dispatch unknown signals to matched experts within a heterogeneous pool. We construct HeteroSignal-7, a multi-task routing benchmark with 140,000 samples from seven datasets covering six task types, paired with seven classification and waveform-recovery experts and a unified evaluation protocol. We then propose GLCR, a two-stage routing method combining global dispatch with local discrimination of similar tasks. First, a lightweight one-dimensional convolutional neural network (CNN) with an additive-margin head predicts one of seven routing classes. Second, a gradient-boosted-tree specialist uses four physical statistics to re-evaluate samples whose top two candidates match a confusion pair automatically identified from validation errors. The pipeline is trained across the full signal-to-noise ratio (SNR) range and…

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  • arXiv cs.LGNov 11

    Observations Drift, Structures Remain: Structural Pretraining with Time Alignment for Electromagnetic Signals

    Wenjin Gui, Junyu Shen, Haibo Xu, Yuchuang Sun, Luqing Luo +5

    Electromagnetic signal modeling is increasingly moving toward self-supervised pretraining on heterogeneous I/Q observations. However, an observed waveform is jointly shaped by signal generation, propagation environments, and acquisition processes, which mix reusable structural regularities with observation-specific variations. Existing methods usually pursue unified pretraining with reconstruction objectives on mixed observations. This heterogeneity creates two key mismatches for reconstruction pretraining: (1) appearance mismatch, induced by propagation-dependent waveform variations; and (2) temporal mismatch, induced by acquisition-dependent sampling coordinates. These mismatches make observation-specific appearances and sampling coordinates easier to reconstruct than stable signal structures, inducing observational shortcuts. We identify this failure mode as observation-biased pretraining. To address this, we propose Structural Pretraining with Time Alignment for heterogeneous EM signals. It mitigates temporal mismatch with RoPE-fs, which maps token positions to sampling-rate-normalized physical-time positions, and reduces reliance on appearance shortcuts by replacing waveform…

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  • arXiv cs.CRNov 10

    What Does an RF Fingerprint Identify? A Cross-Corpus Evaluation of Hardware-Family-Associated and Unit-Level Signal

    Hyojung Han

    RF fingerprinting benchmarks report how well a classifier separates transmitters, not whether that separability reflects the individual unit or its hardware family. We compare a benchmark's real hardware grouping with size-matched random groupings of the same devices, using calibrated, unclipped variational lower bounds under label-blind, device-level cross-fitting on devices never seen in training. On WiSig (OFDM Wi-Fi), the generation grouping exceeded all 999 matched random groupings (permutation p = 0.001, the resolution floor), although no grouping attained a positive certified bound, and the direction survives removing channel equalization. A preregistered replication on a LoRa corpus (chirp spread spectrum, three hardware models), with a front-end frozen on synthetic data, again exceeded all 999 (p = 0.001), and two sensitivity analyses judged by a reversal rule fixed in advance did not reverse the direction. This is a directional replication only: the excess magnitude reflects a miscalibrated null, the chain-rule decomposition leaves a residual of +0.296 bits, and capture order is undocumented. In a same-model corpus of 122 ESP32 radios, a temperature-calibrated held-out…

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  • arXiv cs.NINov 10

    Risk-Aware Joint Inference and Resource Control for Physical AI in 6G Networks

    Amin Farajzadeh, Melike Erol-Kantarci

    Wireless edge inference enables Physical-AI devices to execute edge-inferred actions using transmitted sensory inputs. Since the risk of incorrect actions vary across observations and tasks, the associated uplink transmission of sensory inputs requires different levels of wireless reliability. For instance, applying a fixed signal-to-interference-plus-noise ratio (SINR) requirement can overprovision low-risk tasks while underprovisioning high-risk ones. Therefore, we propose risk-aware control for edge inference (RACE), which maps predicted action-failure probability and task criticality to adaptive SINR targets and combines them with deadline-derived SINR requirements. A slow-timescale large language model (LLM) proposes task requirements for deterministic validation, while a fast hybrid actor selects inference configurations and transmit power levels, complemented by analytical receive beamforming and finite power restoration. Simulations show $11\%$ improvement in the average accuracy-latency-energy objective over fixed-high-SINR control, with near-zero constraint outage.

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  • arXiv eess.SPNov 10

    Achieving Robust Channel Estimation Neural Networks on Doppler effects by Designed Training Data

    Dianxin Luan, John Thompson

    Channel estimation is crucial in wireless communication systems. Many existing works train and test their models using the same or similar channel. However, practical channels are infinitely diverse and time-variant while data-driven algorithms often degrade on new and previously unseen channels. This prohibits the practical implementation of neural networks for channel estimation, particularly in high speed scenarios characterized by rapidly channel profile changes and very high Doppler shifts. Online training can fine-tune models to adapt to new channels, but requires high computation resource and incurs significant latency for physical-layer devices. Moreover, online training cannot exhaustively cover all possible channels because there are infinitely many possible channels. Therefore, we investigate the ability of neural networks to generalize to wireless channels. This paper proposes design criteria to generate synthetic training data, which guaranty that the resulting networks perform robustly across wireless channels, especially for very high Doppler shifts and different Doppler spectra. It ensures that offline-trained neural networks require no prior information or online…

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  • arXiv cs.SDNov 10

    Multi-Step Underwater Acoustic Channel Prediction via Dual-Domain Structure Preservation and Pretrained Transformer

    Mingjie Cao, Gewei Zhang, Deqing Wang, Lizhao You, Fei-Yun Wu +1

    Accurate future channel state information (CSI) is essential for underwater acoustic (UWA) communication because delays associated with signal propagation, feedback, scheduling, and transceiver reconfiguration can render currently available CSI outdated. However, most existing predictors estimate only the next channel state and obtain multi-step forecasts through recursive rollout, which causes prediction errors to accumulate as the multipath structure evolves. The scarcity of sea-trial measurements further limits reliable model training. To address these challenges, we propose LLM4UACP, a dual-domain, structure-preserving framework that adapts a pretrained Transformer for few-shot multi-step UWA channel prediction. LLM4UACP extracts complementary features from the channel impulse response (CIR) and channel frequency response (CFR), aligns them through Fourier-consistent fusion, and encodes the fused representation as delay-time tokens. A pretrained Transformer with a frozen backbone is adapted through Butterfly Orthogonal Fine-tuning to jointly predict multiple future CIRs in a single forward pass, thereby avoiding the error accumulation associated with recursive prediction.…

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  • arXiv cs.LGNov 9

    Neural Network-Assisted CLEAN for Efficient Multipath Parameter Estimation in MIMO Systems

    Chaofan Deng, Linyu Sun, Jaeho Lee, Zishen Wan, Arijit Raychowdhury

    High-resolution multipath estimation in wideband multiple-input multiple-output (MIMO) channels requires repeated searches over angle of arrival, angle of departure, and propagation distance. This paper presents neural network-assisted CLEAN (NN-CLEAN), which replaces the multidimensional search at each extraction step with a multi-head classifier while retaining least-squares (LS) estimation of the complex path gain, parametric reconstruction, and residual cancellation. The same classifier is applied to successive residuals, allowing the extraction depth to vary without changing its output structure. With the number of iterations prescribed by the reference multipath-component (MPC) count, off-grid synthetic tests show that NN-CLEAN slightly exceeds grid-search CLEAN (GS-CLEAN) in path accuracy for one and two MPCs, although its accuracy declines more rapidly at larger path counts. For complete three-MPC extraction at a batch size of eight, NN-CLEAN achieves an approximately $27\times$ speedup and a $52$-fold reduction in peak GPU memory relative to GS-CLEAN. Applied without retraining to two-MPC line-of-sight (LOS) and non-line-of-sight (NLOS) Sionna ray-tracing channels within…

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  • arXiv cs.LGNov 9

    Continuous-Time Set-Transformers for Asynchronous WiFi-IMU Indoor Localization

    Mohamed Bachar, Ilyass Abouelaziz, Yuehua Ding

    Indoor localization commonly combines WiFi received signal strength indicator (RSSI) fingerprints with inertial measurements. However, these sensor streams are sampled asynchronously at different rates; therefore, observations rarely align in time and may be missing or stale. Most existing methods resample the data onto a shared clock and process each modality using a separate branch. We instead present a continuous-time transformer that represents observations as an unordered set of timestamped tokens. Each token encodes its measurement, modality, and elapsed time since acquisition. A single attention block jointly fuses information across modalities and time, avoiding both resampling and modality-specific branches while remaining invariant to token order. During training, we randomly mask entire modalities and individual observations, enabling the model to operate under missing or outdated inputs. We validate the modality-specific encoders on their respective benchmarks and evaluate the fused model on both simulated and real datasets, including a crosssession dataset collected on unseen days. The proposed approach improves upon an open WiFi fingerprinting baseline under…

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  • arXiv cs.AINov 9

    Wireless AI Networks: Co-Adapting Communication Resources and Distributed Intelligence

    Ziqiang Ye, Yue Xiao, Qiyan Zhan, Yulan Gao, Xianfu Lei +1

    Distributed artificial intelligence (AI) increasingly relies on wireless networks, where communication and computing resources shape how AI tasks are configured and executed. However, resource availability alone does not determine how effectively these tasks can be supported, since different information selections and transmission strategies may yield different AI performance under the same resource constraints. To address this challenge, this article presents Wireless AI Networks as a unified design perspective for bidirectional adaptation between wireless communication and distributed intelligence. Specifically, an AI-executable resource budget characterizes candidate AI configurations, while task-level information value guides their selection and wireless resource allocation. Building on these principles, a wireless-AI co-adaptation framework coordinates resource abstraction, joint configuration and allocation, and execution feedback. Two complementary experiments illustrate this perspective: update selection and statistical correction improve federated learning under identical model-update payload budgets, while sensitivity-aware power allocation reduces model accuracy…

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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…

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  • 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…

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  • 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…

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  • 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.…

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  • 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.

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  • 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.

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  • arXiv cs.LGNov 5

    Graph Neural Networks for Resource Allocation in Integrated Sensing and Communication

    Junyu Liao, Lili Chen, Tian Han, Jamie Evans

    In vehicular integrated sensing and communication (ISAC) networks, each service-provider vehicle uses a limited power budget to serve multiple sensing targets and communication receivers. Maximizing the communication sum-rate subject to sensing signal-to-interference-plus-noise ratio (SINR) constraints is nonconvex, and iterative optimization is costly to rerun for every network layout. This paper proposes a graph neural network (GNN)-based power allocation algorithm, termed GNN-ISAC. Concretely, GNN-ISAC addresses the aforementioned problem by considering two distinct edge types in the graph representation, one capturing the inter-provider interference, the other representing the intra-provider power-budget sharing. GNN-ISAC is trained without labels using a sensing-penalized sum-rate loss. We also adapt the weighted mean-square error minimization (WMMSE) algorithm to this problem by introducing a sensing-power reservation step. Simulation results show that GNN-ISAC achieves communication sum-rates close to WMMSE with low sensing-link violation rates and much less computation. It also outperforms an existing heterogeneous GNN and a channel-blind baseline, and generalizes to…

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  • arXiv cs.AINov 4

    Learning to Decode Linear Codes with Generalized Belief Propagation Network

    Samira Malek

    Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels. Recently, neural decoders have demonstrated notable performance gains over classical iterative algorithms. In this paper, we propose a Generalized Belief Propagation Network (GBPNet) that preserves message-passing symmetry while enhancing the decoder's learning capability. The architecture uses cascaded variable-node updates and learned element-wise message scaling while retaining the Tanner graph structure. We also introduce two training strategies, progressive training and high-SNR training, that improve decoding accuracy. Experimental results on several linear codes show that GBPNet achieves lower bit error rates than conventional belief propagation and the evaluated state-of-the-art neural decoders.

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