Signal Processing · Cross-listed from cs.LG
Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning
Mert Can Turkmen, Eng Leong Tan and Yee Hui Lee
Most data-driven ionospheric models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-based sensing. We instead model the ionosphere as a dynamic graph over ionospheric pierce points, with connectivity that evolves as satellite positions change. Because satellite trajectories are predictable, the graph
topology over the forecast horizon can be constructed in advance. We exploit this property to condition forecasts on the future graph structure, which we term ephemeris conditioning. This enables prediction on lines of sight (LoS) that appear only in the forecast horizon. We evaluate our framework on Global
Navigation Satellite System data from a co-located receiver pair in Singapore spanning 2023 to 2025. The task is
forecasting irregularities defined by the Rate of TEC Index (ROTI) up to 2 hours ahead as per-node binary classification. The resulting model, IonoDGNN, achieves a Brier Skill Score (BSS) of 0.55 and an area under the precision-recall curve (PR-AUC) of 0.77. These correspond to improvements over persistence of 53% in BSS and 58% in PR-AUC, with larger gains at longer lead times. Ablations confirm that graph structure and ephemeris conditioning each contribute meaningfully. Under simulated coverage dropout, the model retains predictive skill on affected nodes through spatial message passing from observed neighbors. Compared to interpolation baselines, the proposed model achieves better recovery, especially at higher dropout levels. These results suggest that dynamic graph
forecasting on evolving LoS is a viable alternative for ionospheric modeling. The project and the
dataset are available at https://github.com/Mert-chan/IonoDGNN.
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Systems and Control · Cross-listed from cs.RO
DAG-STL: A Hierarchical Framework for Zero-Shot Trajectory Planning under Signal Temporal Logic Specifications
Ruijia Liu, Ancheng Hou, Xiao Yu et al. (+1 more)
Signal Temporal Logic (STL) is a powerful
language for specifying temporally structured robotic tasks. Planning executable trajectories under STL constraints remains difficult when system dynamics and environment structure are not analytically available. Existing methods typically either assume explicit models or learn task-specific behaviors, limiting zero-shot generalization to unseen STL tasks. In this work, we study offline STL planning under unknown dynamics using only task-agnostic trajectory data. Our central design philosophy is to separate logical
reasoning from trajectory realization. We instantiate this idea in DAG-STL, a hierarchical framework that converts long-horizon STL planning into three stages. It first decomposes an STL formula into reachability and invariance progress conditions linked by shared timing constraints. It then allocates timed waypoints using learned reachability-time estimates. Finally, it synthesizes trajectories between these waypoints with a
diffusion-based generator. This decomposition–allocation–generation pipeline reduces global planning to shorter, better-supported subproblems. To bridge the gap between planning-level correctness and execution-level feasibility, we further introduce a rollout-free dynamic consistency metric, an anytime refinement search procedure for improving multiple allocation hypotheses under finite budgets, and a hierarchical online replanning mechanism for execution-time recovery. Experiments in Maze2D, OGBench AntMaze, and the Cube domain show that DAG-STL substantially outperforms direct robustness-guided
diffusion on complex long-horizon STL tasks and generalizes across
navigation and manipulation settings. In a custom environment with an optimization-based reference, DAG-STL recovers most model-solvable tasks while retaining a clear computational advantage over direct optimization based on the explicit system model.
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All authors Ruijia Liu, Ancheng Hou, Xiao Yu, Xiang Yin
Systems and Control · Cross-listed from cs.RO
Relative State Estimation using Event-Based Propeller Sensing
Ravi Kumar Thakur, Luis Granados Segura, Jan Klivan et al. (+4 more)
Autonomous swarms of multi-Unmanned Aerial Vehicle (
UAV) system requires an accurate and fast relative state estimation. Although
monocular frame-based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic range, and microsecond-level temporal resolution. This paper proposes a framework for relative state estimation for quadrotors using event-based propeller sensing. The propellers in the event stream are tracked by detection to extract the region-of-interests. The event streams in these regions are processed in temporal chunks to estimate per-propeller frequencies. These frequency measurements drive a kinematic state estimation module as a thrust input, while camera-derived position measurements provide the update step. Additionally, we use
geometric primitives derived from event streams to estimate the orientation of the quadrotor by fitting an ellipse over a propeller and backprojecting it to recover body-frame tilt-axis. The existing event-based approaches for quadrotor state estimation use the propeller frequency in simulated flight sequences. Our approach estimates the propeller frequency under 3% error on a test
dataset of five real-world outdoor flight sequences, providing a method for decentralized relative localization for multi-
robot systems using event camera.
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All authors Ravi Kumar Thakur, Luis Granados Segura, Jan Klivan, Radim Špetlík, Tobiáš Vinklárek, Matouš Vrba, Martin Saska
Signal Processing
RSMA-Aided Full-Duplex Networks Under Imperfect CSI and SIC: Performance Evaluation
Farjam Karim, Nurul Huda Mahmood, Deepak Kumar et al. (+2 more)
This work investigates a full-duplex (FD)-enhanced Rate-Splitting Multiple Access (RSMA) system under practical constraints, including imperfect channel state information (CSI) and successive interference cancellation (SIC). We derive closed-form expressions for key performance metrics, such as outage probability and throughput, for both uplink and downlink users. The analysis considers co-channel interference (CCI) from uplink to downlink users and models the self-interference (SI) channel as a random variable. Monte Carlo simulations validate the analytical results and highlight the impact of system imperfections on RSMA-FD performance. At low transmit power, imperfect CSI significantly affects the system, though this effect weakens as power increases. In contrast, imperfect SIC becomes more detrimental at high transmit power, causing severe degradation. Additionally, neglecting CCI and assuming perfect SI cancellation leads to substantial overestimation of performance. Lastly, we demonstrate that the SI cancellation factor must be carefully selected to suppress interference effectively. Otherwise, a poor choice limits the full potential of FD technology.
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All authors Farjam Karim, Nurul Huda Mahmood, Deepak Kumar, Arthur Sousa de Sena, Matti-Latva-aho
Audio and Speech Processing
Incremental learning for audio classification with Hebbian Deep Neural Networks
Riccardo Casciotti, Francesco De Santis, Alberto Antonietti et al. (+1 more)
The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to sound classification. We propose a kernel plasticity approach that selectively modulates network kernels during incremental learning, acting on selected kernels to learn new information and on others to retain previous knowledge. Using the ESC-50
dataset, the proposed method achieves 76.3% overall accuracy over five incremental steps, outperforming a baseline without kernel plasticity (68.7%) and demonstrating significantly greater stability across tasks.
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All authors Riccardo Casciotti, Francesco De Santis, Alberto Antonietti, Annamaria Mesaros
Signal Processing
Impact of CSIR, SIC, and Hardware Impairments on the Ergodic Rate of Downlink RSMA
Farjam Karim, Deepak Kumar, Prathapasinghe Dharmawansa et al. (+3 more)
This work investigates the ergodic rate performance analysis of rate-splitting multiple access (RSMA) in a downlink communication system under practical impairments. Closed-form expressions are derived for key performance metrics such as ergodic rate, energy efficiency, sum-rate, and Jains
fairness index, capturing the joint effects of imperfect channel state information at the receiver (CSIR), imperfect successive interference cancellation (SIC), and hardware impairments. Numerical simulations validate the accuracy of the analytical expressions and reveal several insightful trends. At low transmit powers, imperfect CSIR is the dominant performance-limiting factor, followed by hardware impairments and imperfect SIC. However, as the transmit power increases, hardware impairments become the primary bottleneck, with the impact of imperfect CSIR gradually diminishing, and imperfect SIC becoming a more prominent bottleneck. Moreover, RSMA consistently outperforms non-orthogonal multiple access (NOMA) in terms of ergodic rate,
fairness, and sum-rate, even under severe non-idealities. These findings underscore the importance of incorporating
fairness as a core design objective alongside rate and energy efficiency, positioning RSMA as a robust and strong multiple access candidate for next-generation wireless networks.
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All authors Farjam Karim, Deepak Kumar, Prathapasinghe Dharmawansa, Nurul Huda Mahmood, Arthur Sousa de Sena, Matti Latva-aho
Systems and Control
Scenario-Based Stochastic MPC for Energy Hubs with EV Fleets Under Persistent Grid Outages
Kobena Badu Enyam, Cara Koepele, Timothy Asare et al. (+2 more)
Emissions reduction and resilience to outages motivate the adoption of renewable microgrids. Surprisingly, research integrating both probabilistic grid outages and electric vehicle (EV) charging requirements remains limited. This paper addresses this gap by developing a scenario-based
stochastic model predictive controller (SMPC) for a microgrid energy hub comprising
solar generation, battery storage, diesel backup, and an EV fleet connected to a weak grid. Grid outage and campus load scenarios are generated from a continuous-time Markov chain and a Gaussian Process, respectively. Using 2023 operational data from the Ashesi University Energy Hub in Ghana, we demonstrate that the SMPC achieves performance within 1% of a perfect-forecast
benchmark. In contrast, a naive MPC that assumes continuous grid availability offers no economic or sustainability advantage over rule-based control, with both incurring significantly higher costs and emissions than the SMPC. These results highlight that outage anticipation is essential for economic viability. Finally, we show that incorporating a deterministic buffer against EV consumption uncertainty eliminates over 90% of state-of-charge violations with negligible impact on total operating costs
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All authors Kobena Badu Enyam, Cara Koepele, Timothy Asare, Kevin Wallington, John Lygeros
Signal Processing
Passive RIS Is Not Silent: Revisiting Performance Limits Under Thermal Noise
Farjam Karim, Deepak Kumar, Prathapasinghe Dharmawansa et al. (+3 more)
Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution for enabling energy-efficient and flexible spectrum usage in wireless communication, particularly in the context of sixth-generation (6G) networks. While passive RIS architectures are widely regarded as virtually noiseless due to the lack of active components, this idealized assumption can lead to misleading performance evaluations. In this paper, we revisit this assumption and demonstrate that the thermal noise generated by passive RIS elements, though often neglected, can significantly affect system performance. We propose a tractable approximated analytical framework that incorporates RIS-induced thermal noise into the system and derive closed-form expressions for key performance metrics, such as outage probability and throughput. Simulation results validate our approximated analysis and highlight the substantial performance discrepancies that arise when RIS thermal noise is ignored. Our results offer valuable insights into the trade-offs between receiver and RIS noise, guiding the development of robust and efficient 6G communication systems.
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All authors Farjam Karim, Deepak Kumar, Prathapasinghe Dharmawansa, Nurul Huda Mahmood, Arthur Sousa de Sena, Matti-Latva-aho
Signal Processing
WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM)
Xuanyu Liu, Shijian Gao, Boxun Liu et al. (+2 more)
Current learning-based wireless methods struggle with generalization due to the fragmented processing of communication and sensing data. WiFo-MiSAC addresses this as a task-agnostic foundation model that tokenizes heterogeneous signals into a unified space for self-supervised pre-training. A shared-specific disentangled mixture-of-experts (SS-DMoE) architecture is employed to decouple modality-shared and modality-specific representations, facilitating interaction without cross-modal interference. By combining masked reconstruction with contrastive
alignment, the model achieves state-of-the-art performance across downstream tasks, including beam prediction and channel estimation. Experimental results demonstrate robust few-shot adaptation and seamless integration of new modalities, positioning WiFo-MiSAC as a scalable backbone for future integrated sensing and communication systems.
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All authors Xuanyu Liu, Shijian Gao, Boxun Liu, Xiang Cheng, Liuqing Yang
Signal Processing
A Novel Piecewise Atmospheric Attenuation Model for Free Space Optical Links in Vertical Heterogeneous Networks
Eylem Erdogan, Mohammed Elamassie, Ibrahim Altunbas et al. (+3 more)
Free-space optical (FSO) communication is emerging as a key backhaul technology for next-generation vertical heterogeneous networks (VHetNets), whose architecture spans satellites, high-altitude platform stations (HAPS), unmanned aerial vehicles (
UAVs), and terrestrial nodes. Along these vertical and slant paths, optical beams traverse successive atmospheric layers that may contain clouds, fog, rain, and aerosols, conditions that conventional single-coefficient Beer-Lambert models typically handle only in isolation. Instead of such simplified formulas, we present a unified attenuation model that incorporates aerosols, fog, rain, cloud layers, and drizzle, accounts for the zenith angle, and provides a holistic estimate of the cumulative power loss across atmospheric layers. Numerical results show several-decibel attenuation variations across representative weather scenarios, while the difference between the proposed model predictions and the layer-resolved MODTRAN simulations remains within 1 dB, thereby validating the accuracy of the proposed model and its practical relevance for VHetNet link-budget studies.
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All authors Eylem Erdogan, Mohammed Elamassie, Ibrahim Altunbas, Gunes Karabulut Kurt, Murat Uysal, Halim Yanikomeroglu
Signal Processing
Cramér-Rao Bound Optimization for Near-Field ISAC with Extended Targets
Zongyao Zhao, Zhaolin Wang, Lincong Han et al. (+4 more)
Near-field integrated sensing and communication (
ISAC) requires target models beyond the point-target abstraction when the target has a non-negligible spatial extent. In this letter, a geometry-aware transmit design is developed for a parametric extended target (ET) described by its center, orientation, and size under spherical-wave propagation. The CRB for the
geometric parameters is formulated around a nominal ET state, an exact ET-aware reduced subspace is identified for the lifted covariance formulation, and a reduced-dimensional semidefinite relaxation (SDR) is developed under signal-to-interference-plus-noise ratio (SINR) and power constraints. Simulation results show lower CRB values than point-target and geometry-agnostic baselines together with substantially reduced runtime for large arrays.
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All authors Zongyao Zhao, Zhaolin Wang, Lincong Han, Liang Xu, Jing Jin, Yuanwei Liu, Kaibin Huang
Signal Processing
Geometry-Aware Networking for Low-Altitude Economy: Movable Antennas in Space-Air-Ground Integrated Systems
Heyou Liu, Bang Huang and Mohamed-Slim Alouini
Space–air–ground integrated networks (SAGINs) are emerging as a key foundation for future non-terrestrial networks (NTNs) and low-altitude economy services. However, their performance is increasingly limited not only by communication resources, but by the inability to adapt to rapidly changing spatial geometry. Here, spatial geometry refers to the relative configuration among network nodes, obstacles, and targets, which directly determines propagation conditions, blockage states, interference patterns, and sensing observability.This trend becomes more pronounced as low-altitude operations grow in density and complexity, causing the dominant bottleneck to shift from static resource allocation toward real-time maintenance of favorable spatial geometry across layers.In this article, we argue that movable
antenna (MA) technology provides a fundamentally new perspective for SAGIN design. By enabling controlled
antenna displacement, MA introduces a spatial degree of freedom that allows the network to directly adapt local spatial geometry at fine granularity, rather than passively reacting to it through beamforming or platform mobility.We present a geometry-aware, layered SAGIN architecture, where Low-Earth-Orbit (LEO) provides macro-scale coverage and coordination, High-Altitude Platform Stations (HAPS) enables regional continuity and backhaul support, and MA is incorporated into the layered design to enable fine-grained geometry adaptation, particularly at unmanned aerial vehicles (
UAVs) and terrestrial layers where local channel dynamics are most pronounced. We further discuss how such geometry control enhances robustness, supports multi-functional operation spanning communication, sensing, control, and
navigation, and enables more flexible spatial cooperation across layers.
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Systems and Control
Informativity of Data-Knowledge Pairs for Lyapunov Equations
Ikumi Banno
In the past few years, data informativity with prior knowledge has attracted increasing
attention. This line of research aims to characterize whether data and prior knowledge suffice for system analysis or design. In this paper, we investigate such a characterization for the data-driven problem of determining a unique solution to Lyapunov equations. First, we introduce a notion of joint informativity for data-knowledge pairs as an extension of the standard informativity concept. Second, we derive an algebraic necessary and sufficient condition for the joint informativity. Finally, we provide further insights into the joint informativity by considering a special case of prior knowledge. The characterization presented in this paper is developed for a wide class of prior knowledge, enabling the incorporation of various forms of system information.
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Systems and Control
Frugal Geofencing via Energy-aware Sensing and Reporting
David E. Ruiz-Guirola, Miltiadis Filippou and Onel A. Lopez
Timely and accurate monitoring in geofencing scenarios is challenging when relying on ultra-low power Internet of Things devices (IoTDs) powered by energy harvesting (EH). This is mainly because frequent wake-ups for data acquisition and data uploading may quickly deplete their limited energy buffer. Conventional grid-like IoT deployments overlook these limitations and merely rely on continuously powered sensing. Herein, we propose an energy-aware geofencing framework for camera-equipped EH IoTDs deployed around a protected area and its surrounding perimeter zone. The framework integrates a directional sensing power model with an operational representation of EH, sensing, sleeping, and reporting, accounting for the limited field-of-view (FoV) and distance-dependent detection confidence of the IoTDs. Device activity is controlled by the coverage-providing access point, which hosts a mobile edge host and a facility geocencing system to ensure timely and reliable detection under tight energy constraints. Reinforcement learning is used to determine IoTD placement, enabling earlier intruder detection than uniform grid-based deployments. Numerical results show that the proposed coordinated sensing and reporting configuration achieves frugal geofencing with fewer devices, while concurrently improving detection timeliness and dependability.
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Systems and Control
Leader-Follower Formation Control Using Differential Drag and Effective Surface Regulation
Alessio Bocci, José Juan Corona-Sánchez and Raymond Kristiansen
The growing interest in space activities has led to the emergence of new space operators and innovative mission concepts. Small satellites such as CubeSats reduce mission costs and are typically deployed in constellations or formation flights. Since they are often propulsionless, passive orbital control strategies are the standard, primarily through differential drag achieved via attitude control maneuvers. This work develops a control system to achieve a generic relative positioning between two small satellites in a virtual leader and real follower formation flight, relying entirely on differential drag achieved through attitude maneuvers. We propose a control law based on the integrator backstepping technique, which, in a closed loop with the rotational dynamics, results in the asymptotic stability of the closed-loop system
equilibrium points. We demonstrate the asymptotic stability of the closed-loop system
equilibrium points using the Lyapunov theory, and a numerical simulation assesses the effectiveness and accuracy of the control strategy.
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Signal Processing
Semi-Blind Receivers for RIS-Aided Fluid Antenna Systems
Josué V. de Araújo, Gilderlan T. de Araújo and André L. F. de Almeida
Reconfigurable intelligent surfaces (RISs) and fluid
antennas (FAs) are key technologies for enhancing spatial degrees of freedom in future wireless networks. However, channel acquisition in RIS-aided FA systems is challenging as cascaded links depend on time-varying
antenna-port selections and RIS configurations, leading to high training overhead in conventional pilot-based methods. We propose a semi-blind estimation framework for this joint architecture to estimate channels and symbols concurrently. Two hierarchical transmission protocols are introduced, resulting in distinct tensor models. Protocol 1 uses a two-time-scale structure yielding a PARAFAC (PF) model, while Protocol 2 employs a single-time-scale structure with blockwise spatial variations, leading to a Nested PARAFAC2 (NPF) model. For both, we develop semi-blind receivers based on trilinear alternating least squares to jointly estimate user-to-RIS channels, RIS-to-BS channels, and transmitted symbols by exploiting spatio-temporal diversity from FA and RIS reconfiguration. We derive identifiability conditions and computational complexity, revealing a fundamental trade-off: the PF receiver (Protocol 1) more aggressively exploits joint RIS/FA reconfiguration for stronger robustness, whereas the NPF receiver (Protocol 2) offers a flexible, lower-complexity alternative. Simulations show the proposed receivers achieve accurate recovery with significantly reduced training overhead, demonstrating the effectiveness of tensor-based semi-blind processing for RIS-aided fluid
antenna communications.
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Audio and Speech Processing
NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR
Yuan Xie, Jiaqi Song, Guang Qiu et al. (+9 more)
Integrating large
language models (LLMs) into automatic
speech recognition (ASR) has become a mainstream paradigm in recent years. Although existing LLM-based ASR models demonstrate impressive performance on public
benchmarks, their training remains predominantly data-driven, leaving key practical challenges insufficiently addressed – particularly limited downward scalability in resource-constrained deployments and
hallucinations under acoustically challenging conditions. To address these issues, we present NIM4-ASR, a production-oriented LLM-based ASR framework optimized for both efficiency and robustness. Grounded in a principled delineation of functional roles between the encoder and the LLM, we redesign the multi-stage training paradigm to align each module with its intended capability boundary. Specifically, we reformulate the pre-training architecture and objective to mitigate the modality gap and improve parameter efficiency; introduce an iterative asynchronous SFT stage to preserve acoustic fidelity and constrain representation drift; and design an ASR-specialized reinforcement learning stage to further enhance recognition quality and robustness. We additionally incorporate a suite of production-oriented optimizations, including robustness under noisy and silent conditions, real-time streaming inference, and hotword customization via
retrieval-augmented generation (RAG). Experiments show that NIM4-ASR achieves state-of-the-art performance on multiple public
benchmarks with merely 2.3B parameters, while substantially outperforming larger-scale competitors on internal
benchmarks – particularly in entity-intensive real-world scenarios. NIM4-ASR further supports million-scale hotword customization via RAG with sub-millisecond
retrieval latency, enabling efficient adaptation to emerging entities and personalized user requirements.
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All authors Yuan Xie, Jiaqi Song, Guang Qiu, Xianliang Wang, Kai Qiao, Junfeng Yuan, Shengqing Liu, Yi Zhang, Bowen Chen, Ming Lei, Jie Gao, Jie Wu
Signal Processing
Low-Complexity Tone Injection via Candidate Ranking for PAPR Reduction in OFDM and AFDM Systems
Yupeng Zheng, Ang Li, Jinfei Wang et al. (+2 more)
Tone injection (TI) is a promising distortionless PAPR reduction technique that incurs no spectral efficiency loss. However, state-of-the-art TI schemes based on random candidate generation or clipping noise spectrum suffer from fundamental limitations in PAPR performance. In this paper, we propose novel TI schemes compatible with both OFDM and AFDM systems. The proposed schemes iteratively update the TI sequence via a candidate ranking procedure guided by time-domain local peaks. This accurately selects effective candidates while achieving a complexity comparable to that of the fast
Fourier transform. Depth-first search is further integrated to enhance PAPR performance by exploiting the tree structure of the process. Simulations demonstrate that the proposed schemes achieve over 1 dB PAPR gain over baseline TI schemes at comparable complexity. The gain is consistent across various numbers of subcarriers under controlled per-iteration complexities, confirming a superior performance-complexity trade-off for both OFDM and AFDM.
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All authors Yupeng Zheng, Ang Li, Jinfei Wang, Yi Ma, Rahim Tafazolli
Signal Processing
Joint Detection and Velocity Estimation in OFDM-ISAC Cell-Free Massive MIMO Networks
Maryam Darabi, Sergi Liesegang, Emanuele Grossi et al. (+1 more)
This paper develops a sensing framework for
cell-free massive
MIMO (CF-mMIMO) networks operating under orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (
ISAC). The framework explicitly incorporates the 3D bistatic Doppler geometry across distributed access points (APs) into a generalized likelihood ratio test (GLRT) detector. To address system scalability, a user-target-centric AP association approach is utilized. The 3D velocity vector of the target is estimated, and several search and optimization strategies, including coarse grid search, gradient-based refinement, and particle swarm optimization (PSO), are developed and evaluated. Simulation results demonstrate that the proposed PSO-aided detector achieves the most favorable accuracy-complexity trade-off, while Doppler mismatch can cause substantial sensing signal-to-noise ratio (SNR) degradation in high-mobility scenarios. Additionally, leveraging more OFDM subcarriers enhances frequency-domain diversity and yields further sensing-SNR gains.
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All authors Maryam Darabi, Sergi Liesegang, Emanuele Grossi, Stefano Buzzi
Signal Processing
User Mobility Demands Near-Field Communications in Terahertz Band Wireless Networks Beyond 6G
Peng Zhang, Vitaly Petrov, Arjun Singh et al. (+2 more)
Near-field propagation is often unavoidable at terahertz (THz) frequencies due to the large apertures needed for sufficient array gain, yet near-field operation complicates practical system design, especially under user mobility. This paper asks whether a mobile THz link can remain broadband, achieve the desired high rates and coverage, while operating exclusively in the radiative far field. To answer this question, we develop a proof-by-contradiction feasibility framework that jointly enforces (i) a far-field requirement based on the Fraunhofer distance and (ii) a reliability requirement specified by a target SNR at the worst-case link distance. We derive closed-form upper bounds on the far-field-feasible bandwidth for stationary and mobile links. We further incorporate practical misalignment through several UE rotation and mobility scenarios. Numerical results show that stationary THz links can remain far-field-only with physically realizable apertures while supporting extremely large bandwidths, whereas practical mobile THz systems cannot. In practically relevant mobile THz access settings, the far-field-feasible bandwidth becomes a severe limiting factor: achieving tens-of-GHz targets would require unrealistically high UE transmit power. A cross-band comparison further shows that far-field-only operation is largely attainable at sub-6~GHz and, to a significant extent, at mmWave for moderate bandwidths, while near-field-aware designs become essential for mobile THz access.
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All authors Peng Zhang, Vitaly Petrov, Arjun Singh, Emil Björnson, Josep Miquel Jornet
Signal Processing
Paradigm Shift from Statistical Channel Modeling to Digital Twin Prediction: An Environment-Generalizable ChannelLM for 6G AI-enabled Air Interface
Yichen Cai, Yuelong Qiu, Jianhua Zhang et al. (+4 more)
As 6G advances, ubiquitous connectivity and higher capacity requirements of the air interface pose substantial challenges for accurate and real-time wireless channel acquisition in diverse environments. Conventional statistical channel modeling relies on offline measurement data from limited environments, struggling to support online applications facing diverse environments. To this end, the digital twin channel (DTC) has emerged as a novel paradigm that constructs a digital replica of the physical environment through high-fidelity sensing and predicts corresponding channel in real time utilizing artificial intelligence (AI) models. As the engine of DTC, existing AI models struggle to simultaneously achieve strong environmental generalization in real-world and end-to-end channel prediction for real time tasks. Therefore, this paper proposes a channel large model (ChannelLM)-driven DTC architecture comprising three modules: low-complexity and high-accuracy environment reconstruction based on dynamic object detection and
multimodal alignment of image and point cloud data, physically interpretable environment feature extraction, and a ChannelLM core to mapping these features into generalized environment representations for multi-task channel prediction. Simulation results demonstrate that, in unseen test environments, compared with small-scale AI models, ChannelLM reduces prediction errors by 4.23 dB in channel state information prediction while achieving an end-to-end inference latency of 70 milliseconds in the real world.
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All authors Yichen Cai, Yuelong Qiu, Jianhua Zhang, Li Yu, Yuxiang Zhang, Zhen Zhang, Guangyi Liu
Systems and Control
EcoTIM: Fuel-saving multi-brand tillage with ISO 11783 TIM
Ruben Hefele and Timo Oksanen
Tillage operations account for a large share of on-farm diesel consumption, yet the fuel efficiency of the combined tractor-implement system is not optimised in current practice. Modern continuously variable transmission (CVT) tractors minimise engine fuel consumption internally, but they treat the implement as an unknown load and do not account for the effect of vehicle speed on implement draft force. This paper presents EcoTIM, a distributed fuel-optimisation concept in which the tractor and tillage implement cooperate through the extended ISO 11783 (ISOBUS) Tractor Implement Management (TIM) interface to minimise fuel consumption per hectare in real time. In the EcoTIM concept, the tractor Electric Control Unit fuses its internal engine, transmission, and traction efficiencies into a single combined efficiency value and its derivative with respect to vehicle speed, and broadcasts both to the implement at the standard 100 ms CAN bus cycle. The implement ECU combines these two received scalars with its own analytically known draft force model to evaluate the fuel-consumption gradient, and commands the optimal speed, and as a novel TIM extension, the desired acceleration, back to the tractor. Because only two scalar values are exchanged and neither party discloses proprietary subsystem models, the architecture is inherently multi-brand and plug-and-play. The required data exchange is realised with three new messages and one backward-compatible byte-level extension to the standard TIM speed command, and this paper proposes that these messages are standardised within ISO 11783. The acceleration command enables feed-forward torque and CVT ratio planning on the tractor side, improving transient response compared with speed-only TIM commands. This paper also contains a proof-of-concept simulation with six tillage scenarios and a spatially varying 1km test track for initial concept validation.
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Audio and Speech Processing
MINT-Bench: A Comprehensive Multilingual Benchmark for Instruction-Following Text-to-Speech
Huakang Chen, Jingbin Hu, Liumeng Xue et al. (+12 more)
Instruction-following text-to-
speech (TTS) has emerged as an important capability for controllable and expressive
speech generation, yet its evaluation remains underdeveloped due to limited
benchmark coverage, weak diagnostic granularity, and insufficient
multilingual support. We present MINT-Bench, a comprehensive
multilingual benchmark for instruction-following TTS. MINT-Bench is built upon a hierarchical multi-axis taxonomy, a scalable multi-stage data construction pipeline, and a hierarchical hybrid evaluation protocol that jointly assesses content consistency, instruction following, and perceptual quality. Experiments across ten
languages show that current systems remain far from solved: frontier commercial systems lead overall, while leading open-source models become highly competitive and can even outperform commercial counterparts in localized settings such as Chinese. The
benchmark further reveals that harder compositional and paralinguistic controls remain major bottlenecks for current systems. We release MINT-Bench together with the data construction and evaluation toolkit to support future research on controllable,
multilingual, and diagnostically grounded TTS evaluation. The leaderboard and demo are available at https://aslp-lab.github.io/MINT-Bench-Demo/
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All authors Huakang Chen, Jingbin Hu, Liumeng Xue, Qirui Zhan, Wenhao Li, Guobin Ma, Hanke Xie, Dake Guo, Linhan Ma, Yuepeng Jiang, Bengu Wu, Pengyuan Xie, Chuan Xie, Qiang Zhang, Lei Xie
Signal Processing
A Novel CSI-RS Reporting Scheme for RIS Optimization in O-RAN-based NextG Networks
Ali Fuat Sahin, Sefa Kayraklik, Ali Gorcin et al. (+3 more)
Reconfigurable intelligent surface (RIS) technology is a promising enabler for next-generation (NextG) wireless systems, capable of dynamically shaping the propagation environment. Integrating RIS within the open
radio access network (O-RAN) architecture enables flexible and intelligent control of wireless links. However, practical RIS-assisted operation requires efficient acquisition and reporting of channel state information (CSI) to support real-time control from the base station side. This paper proposes a CSI reference signal (CSI-RS)-based reporting scheme for downlink complex channel information (CCI) to facilitate RIS optimization in an O-RAN-compliant environment. The proposed framework establishing CCI extraction and CSI-RS reporting procedures is experimentally validated on a real-world testbed integrating an open-source O-RAN system with an RIS prototype operating in the n78 frequency band. Existing channel estimation-based RIS optimization
algorithms, including Hadamard and orthogonal matching pursuit (OMP), are tailored for integration into the O-RAN architecture. Experimental results demonstrate notable improvements in received signal power for both near and far users, highlighting the effectiveness and practical viability of the proposed scheme.
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All authors Ali Fuat Sahin, Sefa Kayraklik, Ali Gorcin, Ibrahim Hokelek, Ertugrul Basar, Halim Yanikomeroglu
Systems and Control
Robust Distributed Sub-Optimal Coordination of Linear Agents with Uncertain Input Nonlinearities
Takumi Namba
In this paper, we study robust distributed sub-optimal coordination of linear
agents subject to input nonlinearities. Inspired by the robust agreement literature, we formulate a bounded distributed sub-optimal coordination problem, in which each
agent converges to a neighborhood of the optimizer of a global optimization problem defined over a communication network. We propose a novel control protocol, and analyze convergence by employing a robust control approach, in which both the input nonlinearities and the gradients of the objective functions are treated in a unified manner via sector conditions. In particular, we derive sufficient conditions for the solvability of the considered problem and characterize them in terms of
matrix inequalities. The effectiveness of the proposed method is demonstrated through a numerical simulation.
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Signal Processing
Ray Tracing-Enabled Digital Twin for RIS Phase Optimization: Implementation and Experimental Validation
Ömer Lütfü Karakelle, Sefa Kayrakl{\i}k, İbrahim Hökelek et al. (+2 more)
Determining the optimal phase configurations of reconfigurable intelligent surface (RIS) elements typically requires complex channel estimation procedures with high pilot overhead, creating a bottleneck for real-time deployment in time-varying wireless environments. In this paper, we propose a digital twin (DT)-driven framework for RIS phase shift optimization that eliminates extensive signaling overhead associated with estimating high-dimensional RIS channels. Leveraging the NVIDIA Sionna ray-tracing library, we construct a DT of the physical environment based on a three-dimensional map. The proposed system utilizes the location information of the transceivers to compute the optimal RIS phase shift configurations within the DT. These computationally generated configurations are then transferred to a physical RIS prototype. Experimental results demonstrate that the phase configurations obtained from the DT significantly enhance the received signal power in the physical environment, validating the fidelity of the ray-tracing model and the feasibility of the proposed optimization strategy.
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All authors Ömer Lütfü Karakelle, Sefa Kayrakl{\i}k, İbrahim Hökelek, Ali Görçin, Halim Yan{\i}kömeroğlu
Systems and Control
Quantitative Verification of Constrained Occupation Time for Stochastic Discrete-time Systems
Bai Xue, Peixin Wang and C.-H. Luke Ong
This paper addresses the quantitative verification of constrained occupation time in
stochastic discrete-time systems, focusing on the probability of visiting a target set at least $k$ times while maintaining safety. Such cumulative properties are essential for certifying repeated behaviors like surveillance and periodic charging. To address this, we present the first barrier certificate framework capable of certifying these behaviors. We introduce multiplicative
stochastic barrier functions that encode visitation counts implicitly within the algebraic structure of a scalar barrier. By adopting a switched-system reformulation to handle safety, we derive rigorous probabilistic bounds for both finite and infinite horizons. Specifically, we show that dissipative barriers establish upper bounds ensuring the exponential decay of frequent visits, while attractive barriers provide lower bounds via submartingale analysis. The efficacy of the proposed framework is demonstrated through numerical examples.
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Signal Processing
Joint Phase Noise and Off-Grid Channel Estimation for AFDM Systems via Sparse Bayesian Learning
You Xu, Huaijin Zhang, Lixia Xiao et al. (+2 more)
In practical affine frequency division multiplexing (AFDM) systems, the intricate coupling of oscillator phase noise (PN) and off-grid fractional shifts traps conventional
estimators in a severe high-SNR error floor. To address these challenges, we propose a joint PN and channel estimation method based on sparse
Bayesian learning (JPNCE-SBL). Specifically, a reduced-rank subspace projection is first introduced to capture the dominant eigen-energy of the Wiener PN process. Concurrently, a dynamic grid
evolution strategy is designed to iteratively eliminate off-grid errors without requiring computationally prohibitive global grid densification. Both components are integrated into a unified Expectation-Maximization (EM) framework, where the channel and PN estimates are jointly updated at each iteration to prevent error propagation. Simulation results demonstrate that JPNCE-SBL significantly outperforms existing
benchmarks in both NMSE and BER, closely approaching the perfect channel state information case under practical PN conditions.
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All authors You Xu, Huaijin Zhang, Lixia Xiao, Guanghua Liu, Zilong Liu
Signal Processing
Symbol-Level Mask-Compliant Hybrid Precoding for Multi-User MIMO-OFDM Systems
Navid Reyhanian, Parisa Ramezani and Emil Björnson
Millimeter-wave (mmWave) technology is a crucial enabler for next-generation networks because it offers substantially greater available bandwidth. mmWave multiple-input multiple-output (
MIMO) systems cannot rely solely on fully digital precoding due to hardware costs. As a result, hybrid precoding, which combines digital baseband processing with RF precoding, has emerged as a practical solution that balances performance and implementation complexity. As mmWave links typically operate over wideband, frequency-selective channels, orthogonal frequency-division multiplexing (OFDM) is commonly used to mitigate dispersive effects, yet OFDM introduces practical drawbacks, including out-of-band (OOB) emissions from abrupt spectral transitions among subcarriers and additional spectral leakage induced by windowing. Moreover, nonideal phase shifters (PS) in the RF transmit precoder and the user combiner impose inherent implementation limits that result in phase errors. We investigate robust joint digital–RF precoder design for minimizing the downlink sum mean-squared error (MSE) in hybrid multi-user (MU)
MIMO–OFDM systems subject to maximum transmit-power, clipping, and OOB spectral-mask constraints. The resulting optimization is nonconvex and challenging to solve. To address this, we develop a minimum mean-squared error (MMSE) based block coordinate descent (BCD)
algorithm that alternates between updating the transmitter-side digital–RF precoders and the user-side digital–RF combiners. For each BCD subproblem, we propose computationally efficient and scalable, closed-form solution strategies suitable for practical implementation. Extensive simulations validate the proposed methods and show clear performance improvements over established
benchmark schemes.
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Signal Processing
Low-Complexity Learning-Based Beamforming for Ultra-Massive MIMO THz Communications
Sourabh Solanki, Abuzar Babikir Mohammad Adam, Chandan Kumar Sheemar et al. (+4 more)
Terahertz (THz) communications have emerged as a key technology for escalating data rates in future generation wireless networks. However, severe propagation losses at THz frequencies pose significant challenges, which can be mitigated via ultra-massive multiple-input multiple-output (UM-
MIMO) systems employing highly directional transmissions. To this end, codebook-based analog beamforming constitutes a realistic solution, eliminating the need for explicit channel estimation. However, in UM-
MIMO systems, the use of extremely narrow beams makes beam training and
alignment increasingly challenging, leading to a substantial increase in the number of codewords to be tested and, thus, to high computational complexity. In this paper, a novel artificial neural network architecture for low-complexity beam training in UM-
MIMO THz systems is presented, which does not require a constant feedback link between transmitter and receiver to obtain the best beamformer and combiner pair. An inception and residual network, which is trained based on the received signal powers using the transmit and receive codewords generated from predefined hierarchical codebooks, is designed. Our numerical investigations demonstrate that the proposed machine learning approach significantly reduces the complexity of UM-
MIMO transmit and receive beamforming design, as compared to the standard exhaustive and hierarchical beam searching methods.
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All authors Sourabh Solanki, Abuzar Babikir Mohammad Adam, Chandan Kumar Sheemar, Zaid Abdullah, Eva Lagunas, George C. Alexandropoulos, Symeon Chatzinotas