Image and Video Processing
HyGAS: an Open, Sensor-Agnostic Platform for Multi-Satellite Methane Plume Retrieval, Uncertainty Propagation, and Emission-Rate Estimation
Alvise Ferrari, Valerio Pampanoni and Giovanni Laneve
The rapid expansion of spaceborne methane observing capabilities at the facility-scale (fostered both by public missions and commercial constellations) has created a need for harmonised, reproducible, and uncertainty-aware processing chains that support both monitoring workflows and fair inter-sensor comparisons. This paper presents HyGAS (Hyperspectral Gas Analysis Suite), an open and sensor-agnostic framework that standardises methane processing across multiple imaging spectrometers. HyGAS currently supports end-to-end processing from Level-1 radiance to methane enhancement for PRISMA, EnMAP, and Tanager-1, and it supports ingestion of Level-2 methane enhancement products from EMIT and GHGSat, which are subsequently processed through common downstream modules for background selection, plume
segmentation, Integrated Mass Enhancement (IME), and emission-rate inversion. HyGAS prioritises operational robustness via (i) matched-filter variants designed to mitigate background heterogeneity and pushbroom artefacts, (ii) explicit decomposition and propagation of uncertainty from instrument noise and scene-driven clutter to IME and flux, and (iii) a scale-aware
segmentation strategy defined in physical units and rescaled by ground sampling distance to improve multi-sensor comparability. Representative sample outputs are reported for PRISMA, EnMAP, and Tanager-1. Keywords: Methane emissions, hyperspectral satellites, Tanager-1, PRISMA, EnMAP, GHGSat, EMIT, Tanager, oil and gas, landfills, remote sensing, atmospheric science, greenhouse gas monitoring, spectral analysis, emission quantification, satellite synergy, environmental monitoring.
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Image and Video Processing
Multi-Sensor Methane Mapping in a Unified Framework: Tanager-1 Integration and comparison to EnMAP and PRISMA
Alvise Ferrari, Valerio Pampanoni and Giovanni Laneve
Spaceborne imaging spectroscopy enables facility-scale methane (CH4) plume detection and quantification by exploiting absorption structure in the 1.65/2.3 um windows. However, performance ultimately depends on both radiometric sensitivity and the mitigation of pushbroom artifacts such as column-dependent variability and striping. This paper reports the integration of
Planet/Carbon Mapper Tanager-1 Level-1 radiances into a mature multi-sensor methane processing chain previously applied to PRISMA and EnMAP and evaluates the implications of Tanager-1 radiometric regime for matched-filter
retrieval, plume
segmentation, and IME-based flux estimation. The
retrieval is based on a Clutter Matched Filter (CMF) formulation that yields methane enhancements in concentration-path-length units (ppmm) and propagates uncertainty from radiance noise and background variability through enhancement maps, Integrated Mass Enhancement (IME), and emission rate via the IME method. Particular emphasis is placed on a column-wise CMF (CWCMF), in which background statistics are estimated per detector column to reduce structured false positives induced by pushbroom non-uniformities. A compact radiometric comparison between PRISMA, EnMAP and Tanager-1 is performed on homogeneous high-reflectance calibration scenes to derive reference SNR spectra and striping diagnostics for all these sensors . We then demonstrate CWCMF-only operational results on a landfill super-emitter in the Buenos Aires region, using paired Tanager-1 and EnMAP acquisitions over the same area of interest acquired on different dates. In the absence of near-simultaneous acquisitions and ground truth, results are interpreted in terms of background-limited sensitivity and uncertainty-stabilized IME/flux estimation rather than absolute accuracy.
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Systems and Control · Cross-listed from cs.NI
Locational Pricing for Generative-AI Services via Token-Flow Market Clearing
Shaohui Liu
GenAI services are in an early yet fast expanding phase. Providers compete on model capability and service quality, while the underlying infrastructure remains expensive and heterogeneous across regions, workloads, and compute assets. If these services diffuse into routine daily use, the relevant engineering problem becomes not only better models but also efficient dispatch on a geographically distributed AI service infrastructure. To address this, we formulate a network-constrained
token-flow
market that clears AI workloads across compute nodes and communication links. The baseline model is a linear program that co-optimizes routing and processing subject to compute-capacity and bandwidth constraints; its dual variables define location- and workload-specific marginal service prices. We further introduce a transfer-aware extension that prices data movement in physical units and isolates bandwidth congestion rents. In a 5-node U.S. case study, the transfer-aware model uncovers four saturated backbone links and raises total operating cost by 2.7% relative to the
token-equivalent baseline, while tightening the chatbot latency limit from 100~ms to 15~ms increases one locational price by 117%. A 20-node scale-up exhibits the same merit-order dispatch logic and becomes infeasible once demand exceeds aggregate capacity. These results suggest that locational pricing is a useful organizing principle for operating an emerging AI service infrastructure and, over time, for designing competitive
markets around it.
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Image and Video Processing · Cross-listed from cs.CV
Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination
Adrian Azzarelli, Nantheera Anantrasirichai, James Pollock et al. (+1 more)
Virtual production (VP) use LED walls to provide both background imagery and image-based lighting. While this enables on-set compositing, it couples lighting to background and scene appearance, limiting flexibility for downstream editing. In addition, inverse rendering conventionally relies on physically-based rendering to estimates 3D geometry and lighting, using environment maps. However, these maps are typically low-resolution and assume far-field lighting. In VP, with near-field and high-resolution image-based lighting, this can lead to inaccuracies and introduce complexities when editing. Addressing this, we propose a VP-specific framework for 3D reconstruction and relighting using Gaussian Splatting. This uses the known background imagery to condition the relighting process. This avoids relying on environment maps and reduces compositing to a background-image editing task. To realize our framework, we introduce a process (and associated
dataset) that captures real VP scenes under varying background content and illumination conditions. This data is used to decompose a 3D scene into fixed appearance and variable lighting components. The variable lighting process simulates light transport by parameterizing each primitive with a UV coordinate, intensity value and resolution modifier. Using mipmaps, these directly sample the background texture in image space - implicitly capturing reflections and refractions without physically-based rendering. Combined with the fixed appearance component, this allows us to render relit scenes using a Gaussian Splatting rasterizer. Compared to baselines, our approach achieves higher-quality 3D reconstruction and controllable relighting. The method is efficient (<3 GB RAM, <5 GB VRAM, <2 hours training, ~35 FPS) and supports rendering useful arbitrary output variables including depth, lighting intensity, lighting color, and unlit renders.
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All authors Adrian Azzarelli, Nantheera Anantrasirichai, James Pollock, David R. Bull
Signal Processing
Semantic Communication for Multi-Satellite Massive MIMO Transmission: A Mixture of Cooperative Modes Framework
Yafei Wang, Yuchen Zhang, Yiming Zhu et al. (+5 more)
This paper investigates semantic communications (SemComs) for multi-satellite cooperative massive multiple-input multiple-output (
MIMO) transmission, where multiple massive-
MIMO satellites jointly serve a common set of multi-
antenna user terminals. For the first time, SemComs with image transmission task are integrated into satellite massive
MIMO and multi-satellite cooperative transmission. For the two representative cooperative modes, namely coherent transmission (CT) and non-coherent transmission (NCT), we develop multi-satellite CT (MSCT) and multi-satellite NCT (MSNCT) SemCom frameworks, respectively. MSCT adopts a symmetric architecture, whereas MSNCT introduces transmitter-side stream allocation and a two-stage receiver design that combines per-stream semantic extraction with cross-stream semantic-interference exploitation. To instantiate MSCT, we further design a symmetric encoder and decoder network based on hybrid Swin-
Transformer and lightweight bottleneck convolutional neural network (CNN) blocks, termed HSTC, where Swin
Transformer provides scalable computation and the CNN branch improves performance and convergence. For MSNCT, a
Transformer-based backbone is employed to support cross-stream interference exploitation through global
attention. Building on these two frameworks, we propose a mixture of cooperative modes (MoCM) framework, in which a permutation-invariant network dynamically switches between MSCT and MSNCT using multi-satellite statistical channel state information, thereby balancing semantic performance and complexity. Simulation results under practical configurations demonstrate the performance gains of the proposed frameworks.
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All authors Yafei Wang, Yuchen Zhang, Yiming Zhu, Vu Nguyen Ha, Rui Ding, Wenjin Wang, Symeon Chatzinotas, Björn Ottersten
Signal Processing
SEM-RAG: Structure-Preserving Multimodal Graph Compilation and Entropy-Guided Retrieval for Telecommunication Standards
Yuzhi Yang, Lina Bariah, Yuhuan Lu et al. (+2 more)
Telecommunication standards pose a unique challenge for
retrieval systems, where accuracy depends on semantic relevance as well as on preserving the structural logic embedded in the documents, including structured relationships embedded in tables, conditions, and formulas. When these elements are flattened into text, critical dependencies are lost, leading to unreliable
retrieval. In this paper, we present SEM-RAG, an end-to-end
retrieval framework built around two design choices. First, a layout-aware compiler converts text, tables, and formulas into typed graph primitives. Each table
cell is linked to its row headers, column headers, predicates, and source coordinates, while each formula is converted into an operator graph tied to nearby symbol definitions. Second, the compiled graph is compressed with Structural
Entropy Minimization (SEM), which avoids LLM-based bottom-up clustering during indexing. A Jensen-Shannon
alignment layer and a lightweight query controller serve as supporting
retrieval components that map user queries to the right subgraphs, while keeping online cost stable. Experiments on TeleQnA, TSpec-LLM, SPEC5G, and ORAN-Bench-13K show that SEM-RAG improves performance on table-heavy and formula-heavy questions, reaches 94.1% accuracy on TeleQnA and 93.8% on ORAN-Bench-13K, and cuts indexing-time
token usage by a wide margin relative to standard GraphRAG. These results indicate that structure-preserving compilation is a practical requirement for
retrieval over telecom specifications, not merely an optional preprocessing step.
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All authors Yuzhi Yang, Lina Bariah, Yuhuan Lu, Hang Zou, Mérouane Debbah
Audio and Speech Processing · Cross-listed from cs.CL
Dolphin-CN-Dialect: Where Chinese Dialects Matter
Yangyang Meng, Huihang Zhong, Guodong Lin et al. (+6 more)
We present Dolphin-CN-Dialect, a streaming-capable ASR model with a focus on Chinese and dialect-rich scenarios. Compared to the previous version, Dolphin-CN-Dialect introduces substantial improvements in data processing, tokenization, training stability, and data sampling strategies. To address the challenges of highly imbalanced dialect data, we propose a temperature-based sampling strategy that effectively balances standard Mandarin and low-resource dialects, leading to significant gains in dialect recognition performance. In addition, we redesign the tokenizer to better align with linguistic characteristics, adopting character-level modeling for Chinese and subword modeling for English, while introducing extensible dialect
tokens. Experimental results show that Dolphin-CN-Dialect achieves improvement in dialect recognition accuracy and CER reduction compared to Dolphin. Furthermore, Dolphin-CN-Dialect reaches competitive performance with recent SOTA open-source ASR models, while maintaining a significantly smaller model size. Dolphin-CN-Dialect supports both streaming and non-streaming inference, enabling a practical balance between latency and accuracy. It also provides flexible customization through hotword support and efficient deployment optimized for specialized hardware. These improvements make Dolphin-CN-Dialect a strong and practical solution for real-world multi-dialect ASR applications.
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All authors Yangyang Meng, Huihang Zhong, Guodong Lin, Guanbo Wang, Hu Du, Zhiming Shao, Yukai Huang, Ke Li, Wei-Qiang Zhang
Signal Processing
Comprehensive Review of Advances and Challenges in Next Generation Wireless Networks: From Novel Hardware Technologies to Learning Based Resource Allocation in 6G
Armin Farhadi and Ali Olfat
In modern wireless communication systems, there is a rapidly increasing demand for connectivity to wireless networks. Devices such as internet of things (IoT) devices, connected vehicles, smartphones, surveillance systems, and various other applications contribute significantly to this demand. Consequently, next-generation wireless systems must be capable of handling this enormous volume of devices and traffic. In recent years, several technologies have been introduced to address these challenges, including reconfigurable intelligent surfaces (RIS), integrated sensing and communication (
ISAC), advanced
antenna and intelligent surface technologies, and novel multiple access (MA) techniques. Furthermore, due to the limited resources available in communication systems, efficient resource allocation strategies are essential to support complex and high-dimensional optimization problems. In addition, modern communication systems are required to optimize resources within strict time constraints. Therefore, resource allocation solutions must be intelligent and computationally efficient. Conventional optimization techniques, such as convex optimization, are often inadequate for addressing these requirements. To overcome these limitations, novel resource allocation
algorithms based on learning methods have been developed. In this paper, we comprehensively investigate advanced communication technologies alongside modern resource allocation optimization methods and
algorithms based on machine learning techniques. Subsequently, current challenges of wireless networks are analyzed. Finally, open research challenges are identified.
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Signal Processing
OTFS-IM-Assisted Non-Terrestrial Networks Relying on Autoencoder-Aided Soft-Decision Detection
Xinyu Feng, Chao Zhang, Mohammed EL-Hajjar et al. (+2 more)
Orthogonal Time Frequency Space ({OTFS}) modulation offers significant advantages over Orthogonal Frequency Division Multiplexing ({OFDM}), particularly in high speed environments. Hence, we consider {OTFS} transmission over high-Doppler Non-Terrestrial Networks ({NTN}). However, OTFS-based systems inherit some deficiencies from {OFDM}, such as its high peak to average power ratio, the bandwidth efficiency loss due to the cyclic prefix, and the sensitivity to the carrier frequency offset. Against this background, we harness both Multi-Band Discrete
Fourier Transform-based Spreading (MB-DFT-S) and Index Modulation ({IM}) in our {OTFS} system, termed as MB-DFT-S-OTFS-IM. More explicitly, 1) DFT-S has been shown to reduce the {PAPR}; 2) {IM} is capable of improving the throughput by harnessing it in the Delay and Doppler ({DD}) domain; and 3) MB-DFT-S-OTFS-IM provides frequency diversity gain, which benefits the tolerance to carrier frequency offset. Furthermore, we propose a {PAPR} reduction method based on a Deep Learning ({DL}) Autoencoder ({AE}) architecture for both hard- and soft-decision detection, where the encoder is specifically trained for minimizing {PAPR} and the decoder is conceived for accurately reconstructing the transmitted signal. Finally, we extend the proposed {AE}-aided {OTFS-IM} scheme constructed for a practical {NTN} channel model, representing a variety of satellite-to-ground schemes.
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All authors Xinyu Feng, Chao Zhang, Mohammed EL-Hajjar, Chao Xu, Lajos Hanzo
Image and Video Processing
VISTA: A Benchmark for Real-Time Video Streaming under Network Impairments in Surgical Teleoperation
Zexin Deng, Zhenhui Yuan, Tian Lu et al. (+3 more)
Real-time
video streaming is crucial in
surgical teleoperation, yet reproducible evaluation under realistic network impairments remains limited. This paper presents VISTA, a
benchmark designed to study how impairments along the forward
video path affect received
video quality, temporal continuity, and human task performance. VISTA employs Linux Traffic Control with NetEm and a Gilbert-Elliott loss model to emulate five network conditions: Hospital LAN, 5G Urban, 4G Rural, LEO Satellite, and GEO Satellite. The
benchmark integrates a standardised peg transfer task with synchronized measurements of network quality of service (QoS), objective
video quality (PSNR, SSIM, and VMAF), and temporal continuity through freeze rate, while maintaining a stable reverse control channel. Across 375 experimental trials, network degradation substantially reduced teleoperation performance: success rate decreased from 97% in Hospital LAN to 79% in 5G Urban, 35% in 4G Rural, 71% in LEO Satellite, and 12% in GEO Satellite, while mean task completion time for successful trials increased from 80 s in Hospital LAN to 117 s in 5G Urban, 211 s in 4G Rural, 152 s in LEO Satellite, and 255 s in GEO Satellite. These findings show that network impairments have a direct impact on task completion and success in
surgical teleoperation, and provide a reproducible basis for evaluating teleoperation
video under realistic network constraints. Source code available at https://github.com/Dzxx623/VISTA.
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All authors Zexin Deng, Zhenhui Yuan, Tian Lu, Gaofeng Li, Meipeng Huang, Longhao Zou
Signal Processing
Low-Complexity Beamspace Channel Denoiser for mmWave Massive MIMO with Low-Resolution ADCs
Hanyoung Park, Eunho Kim and Ji-Woong Choi
In this paper, we propose a low-complexity beamspace channel denoising
algorithm for millimeter-wave (mmWave) massive multi-input multi-output (
MIMO) systems with low-resolution analog-to-digital converters (ADCs). The proposed method exploits the inherent sparsity of mmWave channels in the beamspace domain and formulates the denoising problem as a
Bayesian binary hypothesis testing under a Bernoulli-complex Gaussian prior. To capture the distortion induced by low-resolution ADCs in a complexity-efficient manner, thermal noise and quantization noise are jointly modeled as a composite noise. Based on this modeling, a closed-form threshold value and a hard-thresholding-based denoising rule are derived to distinguish signal-dominant and noise-dominant components. The resulting
algorithm avoids computationally intensive operations such as
matrix inversion, iterative optimization, and parameter searching, and achieves near-linear computational complexity with respect to the number of
antennas. Furthermore, a hardware-efficient very large-scale integration (VLSI) architecture is developed to enable practical deployment of the proposed
algorithm, and is implemented on an AMD-Xilinx Kintex UltraScale+ KCU116 FPGA platform. The design incorporates hardware-aware simplifications and an efficient processing structure, leading to significantly lower latency and reduced hardware resource utilization compared to existing hardware implementations, along with sublinear scaling as the number of
antennas increases. Extensive simulation results demonstrate that the proposed method achieves performance comparable to computationally intensive existing approaches while significantly reducing computational complexity.
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Signal Processing
Holographic Surface Enabled Integrated Sensing and Communications
Haobo Zhang, Shuhao Zeng, Xinyuan Hu et al. (+12 more)
Integrated sensing and communications (
ISAC) is an essential 6G capability for joint data transmission and environmental sensing. To support 6G scenarios with stringent
ISAC performance requirements, existing massive-
MIMO-based systems are expected to scale toward ultra-massive
MIMO. However, this scaling incurs prohibitive cost and power consumption when realized using widely adopted phased arrays with complex phase shifters and feeding networks. Recently,
holographic integrated sensing and communications (HISAC) has emerged as a promising paradigm to address this issue. It employs reconfigurable
holographic surfaces (RHSs), a type of leaky-wave
antenna, as a cost- and energy-efficient implementation of ultra-massive
MIMO-based
ISAC, and offers enhanced flexibility for
ISAC beam synthesis through
holographic beamforming. In this paper, we provide a comprehensive tutorial on HISAC, focusing on how RHS-enabled
holographic beamforming can be exploited to jointly support communication and sensing under practical hardware constraints. We first introduce the fundamentals of RHSs and discuss the unique leakage power constraint of
holographic beamforming. We then present a general optimization framework for HISAC and show how HISAC enhances joint communication and sensing, sensing-assisted communication, and communication-assisted sensing. We further present HISAC system implementations and experimental results. Finally, we outline promising research directions for HISAC, highlighting the potential of HISAC in advancing efficient, flexible, and high-performance
ISAC networks.
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All authors Haobo Zhang, Shuhao Zeng, Xinyuan Hu, Shupei Zhang, Boya Di, Hongliang Zhang, Lianlin Li, Lin Chang, Weixiang Jiang, Shaohui Sun, Zhu Han, Yonina Eldar, Rui Zhang, Mérouane Debbah, Lingyang Song
Signal Processing
Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins
Zihao Zhou, Zhaolin Wang and Yuanwei Liu
Wireless digital twins (WDTs) enable site-specific learning, management, and evaluation, but constructing and maintaining uniformly high-fidelity WDTs for large-scale urban environments is costly. This paper studies task-oriented nonuniform WDT refinement (TONR) by addressing the following question: given a limited sensing and reconstruction budget, which building geometries in the initial WDT should be refined to best preserve wireless fidelity? A resource-constrained building selection problem is formulated to minimize the expected discrepancy between the wireless response of the refined WDT and the physical environment. Through local first-order approximation, the refinement value of each building is shown to depend jointly on its correctable geometry uncertainty and the sensitivity of the task response to that uncertainty. This value is then estimated solely from the initial low-fidelity WDT using physically interpretable geometry perturbations and central finite differences. To reduce the computational cost, a propagation-relevance ellipsoid is proposed to filter out buildings unlikely to contribute significantly to the wireless propagation. The resulting knapsack problem is solved under both equal and heterogeneous refinement costs. Simulations across multiple urban scenarios show that the proposed
algorithm can substantially improve wireless fidelity by refining only a small subset of buildings.
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Image and Video Processing · Cross-listed from cs.LG
Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning
Nikhil J. Dhinagar, Vidhi Chhatbar, Chirag Jagad et al. (+6 more)
Deep
vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not merely from overfitting but from a
geometric failure: finite-sample noise corrupts the
embedding covariance, collapsing the eigengap and limiting the number of recoverable signal-bearing modes. We develop a spectral theory of finite-sample representation learning that quantifies the recoverable dimension K(N), the number of eigenmodes that can be stably estimated from N samples. Using perturbation theory and concentration bounds, we show that only modes with
eigenvalues above the noise floor $\|\hat{\Sigma} - \Sigma\|_{\mathrm{op}} \sim \sqrt{D/N}$ are reliable, yielding a truncated Mahalanobis energy that governs classification performance. Under a power-law spectral model, this energy can be approximated by a truncated Riemann
zeta function, linking
eigenvalue decay to data efficiency and AUC. Within this framework,
multimodal learning acts as spectral stabilization:
vision-
language models impose low-rank constraints that suppress noise-dominated directions and preserve the eigengap, increasing K(N) under data scarcity. Across MNIST and multi-disease neuroimaging, we show that
multimodal training maintains more stable modes and improves class separation, even when unimodal models achieve comparable few-shot accuracy. These results identify spectral collapse as a fundamental bottleneck in low-data learning. We use truncated Mahalanobis energy and K(N) to diagnose encoder quality, and introduce
zeta-based spectral filtering as a principled approach to improve data efficiency.
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All authors Nikhil J. Dhinagar, Vidhi Chhatbar, Chirag Jagad, Pavithra Senthilkumar, Sophia I. Thomopoulos, Mahir H. Khan, Sook-Lei Liew, the ENIGMA-Stroke Recovery Working Group, Paul M. Thompson
Signal Processing
Path-Level Radio Map-Aided Fast and Robust Channel Estimation for Pilot-Starved MIMO-OFDM Systems
Xiucheng Wang, Nan Cheng, Yiyan Zhang et al. (+2 more)
Accurate channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (
MIMO-OFDM) systems is challenging when the number of pilot symbols is much smaller than the number of transmit
antennas. Conventional compressed sensing methods perform a three-dimensional search over the angle-of-arrival, angle-of-departure, and delay domains, which incurs high computational cost. In this paper, we propose CHARM (channel estimation with angular-delay
radio map), a framework that extracts an angular-delay power spectrum (ADPS) prior from path-level
radio maps. The ADPS identifies the joint angle-of-arrival and delay support of the dominant multipath components offline, reducing the online estimation to a one-dimensional angle-of-departure search per path. A trust-region constraint is further introduced to prevent sub-grid refinement from diverging under dictionary mismatch. Simulation results show that CHARM achieves accuracy comparable to three-dimensional joint orthogonal matching pursuit (OMP) with $34.8\times$ speedup at pilot length $T \leq 4$, and that the trust-region variant degrades by only 3.7~dB under severe dictionary mismatch of 0.2~rad standard deviation, compared with 8.2~dB without the constraint.
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All authors Xiucheng Wang, Nan Cheng, Yiyan Zhang, Ruijin Sun, Haixia Peng
Signal Processing · Cross-listed from cs.IT
Sensing-Aided Secure Multicast in Rotatable Antenna-Enabled ISAC Systems
Zequan Wang, Liang Yin, Chongjun Ouyang et al. (+4 more)
Acquiring the channel state information (CSI) of passive eavesdroppers remains a fundamental challenge in physical layer security. The sensing capability of integrated sensing and communication (
ISAC) systems enables estimation of a potential eavesdropper's angle of departure (AoD) before secure transmission. Accordingly, a sensing-aided secure multicast scheme is proposed using a rotatable
antenna (RA) architecture that combines array-level and element-level rotations with analog beamforming. The scheme comprises eavesdropper sensing and secure communication stages. In the sensing stage, the maximum likelihood
estimator (MLE) and corresponding Cramer–Rao bound (CRB) are derived for eavesdropper AoD estimation. The two rotation levels are then optimized through cyclic coordinate search to minimize the worst-case CRB. The resulting AoD estimate and CRB determine the center and width of the angular uncertainty region, respectively. In the communication stage, the constant-modulus analog beamformer and RA configuration are jointly optimized to maximize the worst-case secrecy rate over this region. After angular discretization and smooth approximation, the resulting problem is solved using a product-space joint optimization framework. Numerical simulation results validate the convergence and effectiveness of the proposed
algorithms. It is demonstrated that i) the proposed RA-enabled sensing design effectively improves the eavesdropper AoD estimation accuracy; ii) a high and nearly constant secrecy rate is maintained over the uncertainty region; and iii) the joint optimization of the two rotation levels yields lower CRBs and higher secrecy rates than schemes employing either a fixed-position array or a single rotation level.
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All authors Zequan Wang, Liang Yin, Chongjun Ouyang, Hao Xu, Yunan Sun, Yitong Liu, Hongwen Yang
Image and Video Processing
Thin-Client Interactive Gaussian Adaptive Streaming over HTTP/3
Emanuele Artioli, Philipp Fößl, Daniele Lorenzi et al. (+4 more)
Recent advancements in 3D Gaussian Splatting (3DGS) have enabled photorealistic rendering of complex scenes, yet widespread adoption on mobile and Extended Reality (XR) devices is hindered by substantial computational and bandwidth requirements. While existing solutions often focus on model compression for client-side rendering, they still demand significant GPU power, limiting applicability on resource-constrained hardware. We propose TIGAS (Thin-client Interactive Gaussian Adaptive Streaming), a remote rendering framework offloading rasterization to a backend. To bypass the prohibitive latencies connected to fluctuating network conditions, TIGAS streams view-dependent 2D projections to a lightweight web client over QUIC, minimizing head-of-line (HoL) blocking. A dedicated ABR
algorithm adapts rendering quality to fluctuating network conditions, maintaining motion-to-
photon latency within strict 6DoF interactive constraints. Furthermore, we discuss the integration of an experimental WebGPU super-resolution pipeline to analyze the trade-offs between perceptual quality enhancements and thin-client processing bottlenecks. We extensively evaluate TIGAS across multi-continental environments using 14 3DGS models and real 6DoF EyeNavGS movement traces. Powered by a backend rendering frames in under 10 milliseconds, TIGAS maintains latency within interactive thresholds while achieving an average SSIM of 0.88, serving both as a robust testbed for 3DGS streaming research and a capable delivery system. The source code is available at: https://github.com/Rekenar/GaussianAdaptiveStreamer.
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All authors Emanuele Artioli, Philipp Fößl, Daniele Lorenzi, Farzad Tashtarian, Mahdi Dolati, Cheng-Hsin Hsu, Christian Timmerer
Signal Processing
Fair and Efficient Scheduling for Sensor Networks via Online Whittle Index Policy
Sokipriala Jonah, Seong Ki Yoo, Saurav Sthapit et al. (+1 more)
Wake-Up
Radio (WUR) enables resource-constrained, battery-powered sensor nodes to remain in a low-power deep sleep state while continuously listening for a Wake-Up Signal (WUS). Sensor nodes only wake and transmit data after receiving the WUS, significantly reducing energy consumption. However, polling nodes whose transmitted data provides little or no meaningful update to the remote monitor can still result in unnecessary energy usage and increased storage overhead. To address this issue, this paper uses the Age of Incorrect Information (AoII) metric to prioritise the polling of nodes that provide informative updates to the remote monitor. Determining the optimal set of nodes to poll based on AoII can be formulated as a Restless Multi-Armed Bandit (RMAB) problem, which traditionally requires prior knowledge of the monitored process transition dynamics. Since such dynamics are often unknown in practical deployments, we propose an online learning framework based on state estimation to derive Whittle Index AoII (WAoII) and Fair Whittle Index AoII (FWAoII) policies without assuming known transition probabilities. The proposed policies efficiently schedule node polling while adapting to unknown process behaviour. Experimental evaluation using both real-world and synthetic
datasets demonstrates that the proposed online WAoII policy can reduce packet transmissions by up to 70% compared to the widely used Round Robin (RR) polling strategy, while maintaining Root Mean Squared Error (RMSE) values within acceptable application error tolerances. These results demonstrate the effectiveness of WAoII and FWAoII as energy-efficient polling techniques for low-power WUR sensor networks.
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All authors Sokipriala Jonah, Seong Ki Yoo, Saurav Sthapit, Anita Khadka
Signal Processing
Beyond Information Redundancy: Expanding Cross-Modal Knowledge Representation for Power Load Time Series Forecasting
Yuxuan Chen, Shuo Dai, Ruoyi Xu et al. (+1 more)
Load
forecasting is pivotal for stable power systems. Conventional uni-modal methods suffer from representation drift under data scarcity. While recent multi-modal approaches attempt to alleviate this, they exhibit severe information redundancy, merely recycling time series data via superficial intra-modal transformations. In this paper, we argue that the essence of multi-modal time series learning should expand representation
manifolds via complementary cross-modal knowledge enrichment rather than duplicating redundant information, especially for few-shot scenarios prevalent in power systems. To this end, we propose KEMM-Net, a Knowledge-Enriched Multi-Modal Network for power load
forecasting. KEMM-Net first constructs textual and visual
embeddings to strengthen load time series representations from different knowledge perspectives. It then introduces a Partial Information Decomposition (PID)-guided cross-modal contrastive learning mechanism to achieve cross-modal semantic
alignment and balance redundant, synergistic, and unique information for
forecasting. Extensive experiments on real-world public
datasets demonstrate that KEMM-Net consistently outperforms strong deep learning and multi-modal baselines, particularly in few-shot settings. Our code is available at https://anonymous.4open.science/r/KEMM-Net-2898.
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All authors Yuxuan Chen, Shuo Dai, Ruoyi Xu, Haipeng Xie
Signal Processing
Wideband Precoding for U6G XL-MIMO Systems: Beam Squint Boundaries and Channel Slicing
Zhizheng Lu, Yu Han, Xiaojie Li et al. (+2 more)
The unconventionally large aperture of extremely large-scale multiple-input multiple-output (XL-
MIMO) arrays, in conjunction with the wider bandwidths in the upper-6 GHz (U6G) frequency bands, will very likely lead to non-negligible beam squint effects. In the context of a limited number of
radio frequency (RF) chains, and by adopting hybrid precoding, the beams at different subcarriers may point to different positions and compromise the spectral efficiency (SE). Moreover, the existence of multiple paths in U6G XL-
MIMO channels also entails practical challenges for wideband precoding. It is therefore essential to ascertain whether the beam squint effect is pronounced for U6G XL-
MIMO systems and design efficient wideband precoding schemes. To address these challenges, precise
antenna-domain and frequency-domain wideband boundaries are derived from the near-field and far-field perspectives, respectively. These boundaries can inform the design of wideband precoding in future system settings. Subsequently, a channel slicing scheme is proposed for wideband precoding. The process involves the
segmentation of U6G XL-
MIMO channels into multiple blocks, with the objective of mitigating the beam squint effect for each path. The
antenna-domain and frequency-domain slicing methods are developed for multipath and multiuser scenarios, respectively. The simulation results prove that the beam squint effect remains a significant issue for U6G XL-
MIMO systems, while the near-field effect invariably precedes the beam squint effect as the array size and the bandwidth increase. In addition, the proposed scheme can greatly improve the SE.
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All authors Zhizheng Lu, Yu Han, Xiaojie Li, Shi Jin, Michail Matthaiou
Signal Processing
Analytical Framework of Airy Beams in Near-Field XL-MIMO: From Ideal Optics to Wireless Reality
Zhizheng Lu, Yu Han, Ruirui Sun et al. (+2 more)
The synthesis of Airy-profiled wavefronts has emerged as a pivotal paradigm for advanced electromagnetic engineering, attributed to their intrinsic non-diffractive propagation, transverse self-acceleration, and structural self-healing properties. While the advent of extremely large-scale multiple-input multiple-output (XL-
MIMO) and the elevation in frequency bands for sixth generation wireless systems provide the physical foundation for generating such structured beams, their wireless realization is fundamentally governed by hybrid precoding architectures, finite array apertures, and discrete
antenna topologies. These constraints induce significant deviations from ideal optical Airy beam models, necessitating a rigorous re-characterization of Airy beams in practical wireless contexts. Consequently, this paper establishes an analytical theoretical framework to explicitly characterize Airy beam propagation in near-field XL-
MIMO and derives the constraints on array aperture and
antenna spacing to sustain distortion-free main lobe trajectories. Furthermore, quantitative metrics are developed to rigorously evaluate the performance trade-offs between Airy beams and Gaussian focusing beams, thereby providing systematic guidelines for their deployment in scenario-dependent wireless applications. Numerical results corroborate the proposed analytical theoretical framework of Airy beams in near-field XL-
MIMO, and demonstrate the potential to achieve robust communication and spectral efficiency (SE) improvement in certain scenarios.
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All authors Zhizheng Lu, Yu Han, Ruirui Sun, Jiali Nie, Shi Jin
Signal Processing
Channel Geometry Preserving Generative Models for CSI Feedback in MU-MIMO
Juseong Park, Taekyun Lee, Foad Sohrabi et al. (+1 more)
Under limited feedback, channel state information (CSI) reconstruction for multiuser multiple-input multiple-output (MU-
MIMO) precoding is challenging, since the precoder should provide not only beamforming gain, but also robust suppression of inter-user interference. This paper revisits this classic problem by developing powerful decompression techniques at the base station (BS) that harness modern deep generative models. We propose two novel BS-side flow-matching generative CSI decoders that progressively transform either a simple prior or an initial CSI estimate into a reconstruction consistent with the feedback-conditioned channel distribution. We further show theoretically that conventional minimum mean-squared-error (MMSE)-based reconstructions of CSI often result in centroid-like compromises that fail to preserve the posterior geometry needed for inter-user interference suppression. In other words, MU-
MIMO precoding based on MSE-oriented CSI reconstructions can be suboptimal, since such reconstructions frequently fail to maintain user orthogonality. Numerical results in FR3 spectrum show that the proposed flow-based methods consistently outperform MSE-based baselines in downlink sum-rate, with the advantage especially pronounced in interference-limited and spatially dense regimes. These results suggest that posterior-guided flow reconstruction is better aligned with MU-
MIMO precoding than traditional MMSE-oriented CSI feedback, since it better preserves the channel geometry needed for user separation.
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All authors Juseong Park, Taekyun Lee, Foad Sohrabi, Jeffrey G. Andrews
Signal Processing
Large Language Models over Networks: Collaborative Intelligence under Resource Constraints
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang et al. (+2 more)
Large
language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a growing class of applications, including those operating under intermittent connectivity, sub-second latency budgets, data-residency constraints, or sustained high-volume inference. On-device deployment is in turn constrained by limited computation and
memory. No single endpoint can deliver high-quality service across this spectrum. This article focuses on collaborative intelligence, a paradigm in which multiple independent LLMs distributed across device and cloud endpoints collaborate at the task level through natural
language or structured messages. Such collaboration strives for superior response quality under heterogeneous resource constraints spanning computation,
memory, communication, and cost across network tiers. We present collaborative inference along two complementary and composable dimensions: vertical device-cloud collaboration and horizontal multi-
agent collaboration, which can be combined into hybrid
topologies in practice. We then examine learning to collaborate, addressing the training of routing policies and the development of cooperative capabilities among LLMs. Finally, we identify open research challenges including scaling under resource heterogeneity and trustworthy collaborative intelligence.
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All authors Liangqi Yuan, Wenzhi Fang, Shiqiang Wang, H. Vincent Poor, Christopher G. Brinton
Audio and Speech Processing
Reducing Linguistic Hallucination in LM-Based Speech Enhancement via Noise-Invariant Acoustic-Semantic Distillation
Zheng Wang, Xiaobin Rong, Hang Su et al. (+6 more)
Language model (LM)-based
speech enhancement (SE) can generate natural-sounding
speech, but under severe noise it often suffers from unreliable conditioning, leading to perceptually plausible yet linguistically incorrect outputs. To address this issue, we propose L3-SE, a noise-invariant acoustic-semantic
distillation framework for reducing linguistic
hallucination in LM-based SE. The proposed method learns a noise-invariant conditioning encoder from noisy
speech by jointly distilling two complementary clean-
speech targets: an acoustic target for reconstruction fidelity and a semantic target for linguistic consistency. The resulting noise-invariant acoustic-semantic representations are used to condition a decoder-only autoregressive
language model, which predicts clean acoustic
tokens that are decoded into enhanced
speech. To support high-quality generation, we further employ a high-fidelity codec built on learnable weighted WavLM layer representations as the discrete acoustic interface. By improving the reliability of conditioning under adverse conditions, the proposed framework substantially reduces
hallucination and improves content faithfulness. Experiments show that the proposed method consistently outperforms prior LM-based
speech enhancement baselines on linguistic consistency metrics, with especially clear gains under low-SNR and reverberant conditions, while maintaining competitive perceptual quality. Audio samples are available at https://max1wz.github.io/L3-SE-Demo-Page/. The complete source code will be released after the manuscript is accepted.
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All authors Zheng Wang, Xiaobin Rong, Hang Su, Tianyi Tan, Junnan Wu, Lichun Fan, Zhenbo Luo, Jian Luan, Jing Lu
Signal Processing
Intra-Pair Skew Propagation Graph (ISPG): An Analytical Model for Cascaded Channels
David Nozadze, Zurab Kiguradze, Srinath Penugonda et al. (+3 more)
As data rates scale, intra-pair skew has become a critical bottleneck for high-speed differential signaling. Current analytical models are often limited, while 3D electromagnetic simulations are computationally intensive. This paper presents a comprehensive analytical framework for intra-pair skew in generic asymmetric coupled transmission lines, explicitly integrating skew into S-parameter formulations. We introduce the Intra-pair Skew Propagation Graph (ISPG), a novel graph-based methodology for calculating cumulative skew in complex, cascaded channels. The proposed framework is validated against both S-parameter simulations and empirical measurements of a 2m bulk twinax cable assembly, demonstrating excellent accuracy and robustness for high-speed interconnect design.
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All authors David Nozadze, Zurab Kiguradze, Srinath Penugonda, Sayed Ashraf Mamun, Amendra Koul, Mike Sapozhnikov