Systems and Control · Cross-listed from cs.RO
Spatiotemporal Tube-Based Safety-Certificate for Autonomous Navigation of Articulated Vehicles
Mohd. Faizuddin Faruqui, Ratnangshu Das, Ravi Kumar L et al. (+1 more)
Articulated vehicles are the workhorses of freight transportation, and their autonomous navigation is challenging. Their physical characteristics and motion constraints pose significant challenges in manoeuvring these vehicles on narrow routes. This paper presents a spatiotemporal tube-based approach to plan autonomous navigation of vehicles like tractor semi-trailers, truck/ tractor trailers, towing Automated Guided Vehicles (AGVs), and road trains. This planning approach provides a certified path plan for the truck or tractor, ensuring that the towed series of trailers always remains within the road corridor, limited by permissible corrections. The planning leverages the kinematics of the linked elements along with sway constraints to arrive at a safe tube for the actuated
prime mover. We modify the spatiotemporal tube using permissible corrections to provide a route safety certificate to the vehicle for the given route. The proposed planning method is verified on a truck-trailer navigation simulation for a complex route.
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All authors Mohd. Faizuddin Faruqui, Ratnangshu Das, Ravi Kumar L, Pushpak Jagtap
Audio and Speech Processing
Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures
Jocelyn Xu and Minje Kim
Music source separation systems typically extract a single vocal track and do not distinguish between multiple singers. We study singer-informed vocal source separation for multi-singer mixtures. Our framework introduces a short enrollment recording of a target singer to guide separation through a learned
embedding. The singer
embedding is incorporated using feature concatenation or feature-wise linear modulation (FiLM), enabling the model to focus on the target singer while suppressing interference. We construct a duet
dataset based on DAMP-VSEP with quality filtering and non-overlapping enrollment segments. Experiments on solo and duet settings show that while baseline models perform well for single-singer mixtures, the proposed method improves target-singer extraction in multi-singer cases, increasing target-singer SI-SDR from 0.33 dB to 5.58 dB. Fréchet Audio Distance (FAD) further shows improved perceptual quality and better
alignment with target audio distributions. Code and checkpoints are available at https://github.com/jocelynxu01/singer-separation-paper.
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Systems and Control
Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows
Charitha Nandepu, Lohitha Kalepu, Gabriele Ciavarella et al. (+1 more)
Network-level maintenance planning requires repeated evaluations of
equilibrium traffic flows under road capacity reductions. While
equilibrium traffic assignment models are well established, their repeated solution quickly becomes computationally prohibitive and challenging to embed within maintenance scheduling problems. This paper investigates data-driven surrogate models that approximate
equilibrium arc flows directly from origin-destination demand, using optimization-based
equilibrium solutions as ground truth. A real-world case study based on traffic data from the Newark, New Jersey area demonstrates the effectiveness of the proposed approach as a scalable building block for future maintenance scheduling frameworks.
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All authors Charitha Nandepu, Lohitha Kalepu, Gabriele Ciavarella, SangWoo Park
Signal Processing
Sensing-Aided Ordered Reliability Bits Guessing Random Additive Noise Decoding
Yu Ge, Lukas Rapp, Ken R. Duffy et al. (+1 more)
Integrated sensing and communication (
ISAC) is a key enabler for future wireless systems, providing environmental information that can support tasks beyond conventional data transmission. However, its impact on channel decoding remains less explored. This paper studies sensing-aided ordered reliability bits guessing random additive noise decoding (ORBGRAND) over single-input single-output narrowband fading channels. Environmental information is used to construct a geometry-based prior for the channel coefficient, which is fused with pilot observations via linear minimum mean square error (LMMSE) estimation. The resulting posterior channel estimate and uncertainty are used to compute the log-likelihood ratios (LLRs) supplied to ORBGRAND, improving the reliability ordering that drives its noise-guessing process. Simulation results demonstrate improved block error rate and reduced average query complexity, with the largest gains in pilot-limited regimes.
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All authors Yu Ge, Lukas Rapp, Ken R. Duffy, Muriel Médard
Systems and Control
Positive Arc-Weight Design Makes Every Directed Laplacian Diagonalizable
Aandrew Baggio Sahaya Arokiadoss and G. Arunkumar
For directed networks, the
Laplacian need not be diagonalizable, so the standard master-stability variational equations cannot in general be fully decoupled into independent eigenmodes. We prove that this obstruction can always be removed by coupling-strength design: every weakly connected digraph admits a strictly positive weighting of its existing arcs for which the weighted in-degree
Laplacian is diagonalizable. The construction uses a spanning directed acyclic subgraph with one source in each root strongly connected component, assigns distinct positive weighted indegrees to its non-source vertices, and then restores all remaining arcs with a common sufficiently small positive weight. The zero
eigenvalue remains semisimple and all nonzero
eigenvalues remain simple. We also give a discriminant criterion that computes an admissible interval of restoring weights. Thus, any fixed weakly connected directed
topology can be positively weighted so that master-stability perturbations admit a complete modal decomposition.
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Image and Video Processing
UMPIRE-Net: Unrolled Magnitude-Phase Regularization Network for Accelerated MRI
Mahdi Saberi, Toygan Kiliç and Mehmet Akçakaya
MRI reconstruction from undersampled k-space measurements is an ill-posed inverse problem. Physics-driven deep learning (PD-DL) methods have shown strong performance for this task by combining the MRI forward model with learned image regularization within
algorithm-unrolling frameworks. However, most existing PD-DL methods reconstruct complex-valued images directly, thereby implicitly coupling magnitude and phase within a single learned representation. This coupled regularization may be suboptimal in reconstruction settings where accurate phase modeling plays an important role, such as partial
Fourier (PF) imaging, where recovery of the omitted asymmetric k-space measurements depends on the underlying image phase. In such scenarios, explicit modeling of magnitude and phase as separate components may reduce the reliance on externally estimated or predefined phase information. To this end, we propose UMPIRE-Net (Unrolled Magnitude-Phase In REgularization Network), a PD-DL method that introduces separate learned regularizers for magnitude and phase components, together with a novel data-fidelity formulation that enforces measurements consistency. We evaluate UMPIRE-Net for accelerated MRI with PF across different
datasets and acceleration factors. Experimental results demonstrate that our proposed method improves reconstruction quality compared with a conventional complex-valued PD-DL baseline, yielding sharper images and reduced artifacts. Code available at: https://github.com/MahdiSaberii/UMPIRE-Net
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Signal Processing
Multi-Agent Reinforcement Learning for Joint Handover Management and Power Allocation in Multi-Orbit Satellite Networks
Yassine Afif, Ashutosh Balakrishnan, Philippe Martins et al. (+3 more)
Future sixth-generation non-terrestrial networks are expected to combine low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary Earth orbit (GEO) satellites, whose complementary layers must be coordinated through joint user association, power allocation, and handover management under fast LEO dynamics. This paper studies this problem by formulating it as a mixed-integer nonlinear program and decomposing it into a multi-
agent reinforcement learning (MARL) policy that selects the associations and a convex power-allocation subproblem solved exactly at each time slot that defines the reward of the MARL part. The association policy is trained with multi-
agent proximal policy optimization (MAPPO) and the targeted multi-
agent communication (TarMAC) mechanism, and is made aware of the orbital layer through a state that encodes layer-dependent handover penalties. Evaluated on a realistic multi-constellation scenario built from real two-line element data over Nairobi, Kenya, the proposed policy reaches 92% of the throughput of a greedy signal-to-noise ratio (SNR) maximizing scheme while triggering more than four times fewer handovers, and improves throughput by roughly 14% over a conservative stay heuristic. Compared to an LEO-only learned policy of identical architecture, it attains slightly higher throughput with fewer handovers by offloading a fraction of the users to the MEO and GEO layers, an emergent multi-orbit behavior that drives its favorable throughput and handover trade-off.
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All authors Yassine Afif, Ashutosh Balakrishnan, Philippe Martins, Mohammed Almekhlafi, Antoine Lesage-Landry, Gunes Karabulut Kurt
Systems and Control · Cross-listed from cs.AI
Sensor-Driven Mission Synthesis for UAV/UGV Swarms: A TB-CSPN Coordination Architecture with Hardware-Enforced Safety
Uwe M. Borghoff, Paolo Bottoni and Remo Pareschi
This paper presents a coordination architecture for heterogeneous
UAV/UGV swarms that synthesises mission actions from uncertain, multi-modal sensor evidence while preserving hardware-enforced safety at the actuation boundary. The approach combines radar, RF, acoustic, and visual observations with Topic-Based Communication Space Petri Net (TB-CSPN) orchestration to support incremental mission formation under partial and evolving information. Consultant
agents transform sensor outputs into temporally bounded semantic
tokens, while supervisor
agents provide authorisation and policy-governed release of mission transitions. This separation between interpretation, coordination, and execution yields auditable decision paths, constrains non-determinism within the coordination layer through guards and synchronisation, and enables bounded-time integration of heterogeneous evidence. To improve resilience in contested environments, including cyber compromise, spoofing, jamming, and communication loss, the digital coordination layer is complemented by independent analogue safety envelopes that clamp or veto unsafe actuator commands issued to individual vehicles. A coastal-surveillance case study illustrates how the proposed architecture enables dependable, governed, and physically safe swarm coordination under operational uncertainty.
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Signal Processing
Lightweight Beam Index Map Using Coupled Gaussian Mixture Models
Amar Kasibovic, Franz Weißer and Wolfgang Utschick
This paper addresses the beam
alignment problem in
MIMO systems from a decentralized, mobile terminal (MT)-centric perspective. We propose a lightweight machine learning approach that leverages position information to perform beam selection without relying on exhaustive search or strong base station coordination. Specifically, we model the joint distribution of MT positions and channel observations using a coupled Gaussian mixture model (GMM), enabling the construction of a beam index map (BIM) that directly associates spatial locations with codebook entries. To account for practical hardware constraints, we introduce a refinement procedure that adapts the learned statistical model to fixed codebooks. The resulting method is computationally efficient and suitable for deployment on resource-constrained devices. Simulation results on the DeepMIMO and QuaDRiGa
datasets demonstrate that the proposed approach outperforms clustering-based fingerprinting methods and achieves competitive performance compared to exhaustive search, while significantly reducing complexity and overhead.
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Signal Processing
LLM-Assisted LDPC Decoding via Syndrome-Verified Semantic Priors
Sojeong Park, Hyeonsu Lyu, Jaehyun Choi et al. (+1 more)
Semantic communication exploits the meaning of the payload, which bit-level processing discards. When channel decoding fails on a natural
language payload, the errors appear as corrupted characters in the recovered text. A large
language model (LLM) infers the intended characters from the semantic context, but it can also produce incorrect corrections. Applying them directly introduces new bit errors when the LLM modifies characters incorrectly. In this paper, we propose an LLM-assisted decoding framework for low-density parity-check (LDPC) codes. Rather than trusting LLM predictions, the decoder evaluates the modified characters jointly against the parity-check constraints and admits only the accepted corrections as verified semantic priors. These priors are injected as soft updates to the channel log-likelihood ratios, preserving the original channel evidence without modifying the decoder. A subsequent belief propagation pass distributes the injected evidence across the check nodes, recovering not only the injected bits but also the residual errors that the LLM fails to correct. Simulations demonstrate a 73% bit error rate reduction over a conventional decoder at 2.0 dB, whereas doubling its iterations to the same budget yields only 21%. The verification maintains an injection precision above 0.88 despite inaccurate LLM predictions, demonstrating that semantic knowledge can be reliably translated into physical-layer reliability gains.
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All authors Sojeong Park, Hyeonsu Lyu, Jaehyun Choi, Hyun Jong Yang
Image and Video Processing
Personalized Digital Semantic Communication for Image Transmission with Vision-Language Models
Nan Li, Li Zhou, Haijun Wang et al. (+3 more)
Semantic communication (SC) enables bandwidth-efficient wireless image transmission, but most existing SC schemes are user-agnostic and ignore receiver-dependent semantics. To address this issue, we propose a personalized digital semantic communication (PDSC) framework that integrates a
vision-
language model (VLM)-based semantic encoder with a latent
diffusion model (LDM)-based semantic decoder. Specifically, the semantic encoder extracts source-aware personalized semantic
tokens from both the source image and the receiver's historical interactions. These
tokens are vector-quantized into discrete semantic indices and further encoded into a compact fixed-length bitstream, enabling compatibility with digital transmission. At the receiver, the semantic decoder reconstructs a personalized image conditioned on the recovered semantic
tokens. Furthermore, we formulate a capacity-constrained personalized semantic rate-distortion problem and introduce a semantic distortion metric that jointly characterizes source-semantic fidelity and user-preference
alignment. Experiments show that PDSC achieves superior source-semantic consistency and personalization over state-of-the-art SC baselines, including CDDM and MoS, under bandwidth-limited wireless transmission.
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All authors Nan Li, Li Zhou, Haijun Wang, Jun Xiong, Haitao Zhao, Jibo Wei
Signal Processing
Why Performance Metrics Overpromise in Auditory Attention Decoding: an Information-Theoretic Reappraisal
Nicolas Heintz, Simon Geirnaert, Tom Francart et al. (+1 more)
Auditory
attention decoding (AAD)
algorithms are predominantly evaluated in a steady state where a listener continuously attends to the same speaker, using metrics such as accuracy and information transfer rate. However, such metrics fail to account for the (in-)dependence of an AAD prediction with respect to previous predictions. In this paper, we argue that failing to take this dependence into account in the
algorithm evaluation can lead to severe misrepresentations of the true performance of an AAD
algorithm. We therefore introduce the relative Incremental Mutual Information (rIMI); the rate at which a new prediction removes the remaining uncertainty about the identity of the attended speaker. This allows us to track how much new, useful information a prediction actually generates on top of the information already obtained from previous predictions. By investigating the rIMI and the behaviour of AAD models around
attention switches, we demonstrate that recent direct-classification AAD
algorithms are not superior to traditional AAD
algorithms based on stimulus reconstruction, despite what accuracy alone may suggest. We also demonstrate how these direct-classification AAD predictions are severely influenced by irrelevant feature drifts, which artificially inflates accuracies by leaking information across windows, and even across trials.
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All authors Nicolas Heintz, Simon Geirnaert, Tom Francart, Alexander Bertrand
Systems and Control · Cross-listed from cs.RO
Vibration Suppression in Collaborative Flexible Payload Manipulation Using Passive Force Control
Alaa Abderrahim, Antonio Rosales, Ferdinando Milella et al. (+2 more)
In large and heavy structures, vibrations arise during motion, posing significant challenges for precise manipulation. To accomplish the desired motion, control
algorithms must effectively suppress these structural vibrations. In cutting edge projects, such as remote maintenance of future fusion energy reactors (tokamaks), the manipulation of this type of structure is defined as a crucial task. This paper presents a control strategy to suppress transverse vibrations in flexible payloads during motion using a collaborative payload manipulation approach. Two different industrial
robot arms are arranged in a leader follower configuration for the manipulation strategy. The leader
robot guides the motion with shaped velocity commands, while the follower
robot ensures compliance with the estimated external forces applied by the leader on the payload through an admittance controller. Unlike existing methods, the proposed approach enables collaborative manipulation of heavier and larger flexible objects, addressing additional challenges such as vibration suppression and heterogeneous
robot specifications. The dynamics of the leader follower payload system are modeled using an equivalent mass spring damper model, and it is shown that, with appropriate admittance parameters, the total energy of the system is passively dissipated. A stability proof is also provided. Numerical simulations validate the proposed method, and experimental results demonstrate its effectiveness.
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All authors Alaa Abderrahim, Antonio Rosales, Ferdinando Milella, Markku Suomalainen, Shuai Li
Systems and Control
A Temporal Barrier Framework for Collision Avoidance in Multi-Agent Autonomous Aerial Vehicles
Benedikt Barthel Sorensen, Mitchell Black, Erfaun Noorani et al. (+1 more)
Operating teams of autonomous aircraft in dynamic, uncertain, and potentially
adversarial environments requires safety protocols that are reliable yet selective, and allow
agents to fly in close proximity while making progress toward mission objectives. We introduce
adversarial time-to-collision (aTTC), a risk metric that quantifies, for a given
agent, how quickly any surrounding
agent could reach it assuming
adversarial intent. We embed aTTC into the control barrier function (CBF) framework, defining the barrier directly in time rather than distance or velocity. The resulting aTTC-CBF is inherently anticipatory:
agents modulate their own velocity based not on whether a peer is on a collision course, but on how quickly one could reach collision given its dynamical constraints. A differentiable neural-network surrogate makes the aTTC computable in real time within a standard CBF quadratic program. Across long time-horizon simulations of 3D independent-pursuit and formation-flight scenarios, the aTTC-CBF achieves up to twice the waypoint progress at half the collision rate of a higher-order distance-based CBF baseline.
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All authors Benedikt Barthel Sorensen, Mitchell Black, Erfaun Noorani, Themistoklis Sapsis
Signal Processing · Cross-listed from cs.LG
KV Cache Compression Through the Lens of Transform Coding
Hannah Laus, Claudio Mayrink Verdun, Hao Wang et al. (+2 more)
The key-value (KV) cache stores information from past
tokens and is a major
memory bottleneck in long-context inference. Existing quantization methods address this bottleneck by representing the KV cache uniformly with lower-precision data types and designing quantization schemes to minimize reconstruction error in the cache itself, without accounting for how that error propagates through
attention mechanisms. We prove that, under a white-noise quantization model, the expected
attention-aware distortion decomposes into additive key and value contributions that factor across
tokens and channels. Building on transform coding and reverse water-filling, which are classical tools from signal processing and rate-distortion theory, we introduce
Attention-Aware Transform Coding (AATC), which allocates bits over a calibration set to minimize
attention-aware distortion. On Llama-3.1-8B-Instruct and Qwen-2.5-7B-Instruct, evaluated across LongBench, RULER, GSM8K, MMLU-Pro, and MATH-500, our method achieves near-lossless accuracy at approximately $5.8\times$ compression, whereas each baseline degrades in at least some settings.
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All authors Hannah Laus, Claudio Mayrink Verdun, Hao Wang, Flavio du Pin Calmon, Felix Krahmer
Signal Processing
Budget-Aware Federated Dual-Side Channel Estimation for Hybrid mmWave Massive MIMO
Jiawei Chen, Ruining Fan, Mouli Chakraborty et al. (+2 more)
This work studies communication-constrained
federated dual-side channel state information (CSI) estimation in hybrid millimeter-wave (mmWave) massive multiple input multiple output (
MIMO) systems. Accurate CSI recovery is challenging because hybrid beamforming yields compressed and noisy observations, while repeated model exchange in
federated learning (FL) makes communication efficiency strongly dependent on
estimator size. Rather than developing a new
federated optimization
algorithm, we focus on
estimator design under standard
federated averaging (FedAvg) and study how to use a limited parameter budget effectively under repeated model exchange. Based on this perspective, we propose a budget-aware recalibrated refinement network (BARRNet), which combines a compact residual backbone with lightweight channel-wise recalibration for dual-side CSI refinement. Simulation results show that BARRNet achieves a better normalized mean squared error (NMSE)–communication tradeoff than the backbone-only control and heavier convolutional neural network (CNN) baselines. At 5 dB SNR, for the -13 dB DL NMSE target, it reduces the cumulative communication required by 28.9% relative to the architecture-matched backbone-only control. These results indicate that communication-efficient
federated CSI estimation depends not only on model compactness, but also on how limited model capacity is used under repeated model exchange.
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All authors Jiawei Chen, Ruining Fan, Mouli Chakraborty, Avishek Nag, Anshu Mukherjee
Signal Processing
An HFM-Inspired Random Access Preamble Design for NTN under High Doppler
Mehwish Bibi, Saira Rafique, Ahmed Naeem et al. (+1 more)
Non-terrestrial networks (NTNs) are a key enabler of ubiquitous 6G connectivity, but the high orbital velocity and long propagation distances in low-Earth orbit (LEO) NTN operation introduce large Doppler shifts and substantial delay uncertainty that challenge New
Radio (NR) physical random access channel (PRACH) design. Conventional Zadoff Chu (ZC) and linear frequency modulated (LFM) preambles are particularly vulnerable, as Doppler induced ambiguity and delay Doppler coupling degrade timing estimation and preamble identification. This paper proposes a
hyperbolic frequency modulation (HFM) inspired PRACH preamble for robust synchronization and reliable identification under uncompensated or unknown Doppler, detected with a conventional matched filter receiver so that the gains reflect the preamble design. A unified delay Doppler ambiguity function framework characterizes the self and cross ambiguity behavior of ZC, LFM, and HFM inspired preambles, and a scaling-factor based codebook ensures multi-user separability. Simulation results under NTN channel conditions confirm higher detection probability, lower timing root mean square error (RMSE), and improved peak to sidelobe and integrated sidelobe levels compared with ZC and frequency-domain superposed LFM baselines.
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All authors Mehwish Bibi, Saira Rafique, Ahmed Naeem, Huseyin Arslan
Systems and Control · Cross-listed from cs.GT
Optimal Pricing and Charging Strategy Design for Non-cooperative Battery Swapping Stations
Huanyu Yan, Chenxi Sun, Huanxin Liao et al. (+1 more)
Battery swapping is a rapid way to recharge electric vehicles (EVs). As more and more entities are involved in building Battery Swapping Stations (BSSs), how non-cooperative BSSs maximize their profit in a competitive
market needs further investigation. In this paper, we focus on a practical scenario where competitive BSSs are coordinated by the same aggregator. To study the optimal pricing and battery charging, we formulate a hierarchical game-theoretic model, where BSSs determine the swapping price in the day-ahead
market in the first stage, and then determine the optimal battery charging strategy in the real-time
market in the second stage. We rigorously prove the existence and uniqueness of the Subgame Perfect Nash
Equilibrium (SPNE). In particular, the uniqueness property provides theoretical support that the strategy under
equilibrium is optimal in the competitive environment. Based on the unique SPNE, we propose an optimal pricing and charging strategy for each BSS to maximize profit in the competitive
market. A prediction error handling method is also proposed to deal with unexpected fluctuations in swapping demand. Our simulation with a 12-BSS system based on real-life data from Xi'an, China shows that our pricing and charging strategy increases the individual BSS profit by at least 18.1%, while the optimal charging strategy naturally achieves peak shaving for the power grid.
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All authors Huanyu Yan, Chenxi Sun, Huanxin Liao, Xiaoying Tang
Audio and Speech Processing
Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Model
Eloi Moliner, Christoph Hold, Juan Azcarreta Ortiz et al. (+5 more)
We address the problem of encoding room impulse responses (RIRs) into high-order Ambisonics (HOA) representations from arbitrary and potentially insufficient or incomplete microphone array measurements. This task is fundamentally ill-posed for microphone arrays with limited spatial capture capabilities, such as irregular or sparse arrays, as classical linear methods fail to reconstruct high-order spatial detail. We introduce a
diffusion-based generative framework that models the statistical properties of HOA RIRs. This enables device-agnostic encoding from arbitrary microphone arrays, potentially unseen during data measurement. Our approach incorporates a posterior sampling procedure that enforces consistency between the estimated signals and the measurements while plausibly reconstructing spatial information that is unobservable from the limited measurements alone. Experiments on simulated data demonstrate that our method outperforms linear and neural baselines, achieving accurate HOA RIR estimation up to 12th order. A listening test with binaural renderings, including both simulated and measured RIRs, further confirms that the proposed method yields higher perceptual similarity to reference Ambisonics RIRs than all baselines. The flexibility and accuracy of the proposed framework opens new possibilities for scalable acoustics simulations.
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All authors Eloi Moliner, Christoph Hold, Juan Azcarreta Ortiz, Sebastian Prepelita, Ishwarya Ananthabhotla, Daniel Wong, Sanjeel Parekh, Sanha Lee
Signal Processing
Integrated Sensing, Communication, and Computing in Multi-Tier Systems: Joint Hybrid Beamforming Design and Computation Resource Allocation
Peng Liu, Zesong Fei, Xinyi Wang et al. (+4 more)
This paper proposes a novel integrated sensing, communication, and computing (ISCC) framework over a cloud-edge-device collaborative architecture, where passive sensing is enabled by reusing uplink offloading signals to extract sensing information directly at the edge without incurring additional transmission overhead. Nevertheless, such signal reuse introduces an inherent tradeoff between communication efficiency and sensing coverage. To address this challenge, we adopt a hybrid beamforming architecture under practical hardware constraints. In addition, the integration of sensing tasks creates significant resource contention at the mobile edge computing (MEC) server, where latency-sensitive device tasks and computation-intensive sensing inference tasks compete for limited processing capacity. To alleviate this computation burden, we introduce a split inference mechanism that strategically partitions intelligent sensing tasks between the edge and the cloud. Building upon this framework, we formulate a joint optimization problem to minimize the average computation latency of all device tasks subject to strict sensing performance constraints. To tackle the high non-convexity of the formulated problem, we develop an efficient alternating optimization
algorithm. In particular, we design a two-layer framework to jointly determine the optimal DNN splitting point and computation resource allocation and employ a weighted minimum mean square error (WMMSE)-based approach with
manifold optimization for hybrid beamforming design. Numerical results demonstrate that the proposed framework achieves a superior tradeoff between sensing accuracy and computation latency compared to the
benchmark schemes.
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All authors Peng Liu, Zesong Fei, Xinyi Wang, Qiao Qi, Zhaohui Yang, Meng Hua, Arumugam Nallanathan
Systems and Control
Feedback-Guided DNN-Based Controller Fusion for Robust Fixed-Parameter Active Noise Control
Lu Bai, Yiming He, Xiaofeng Nan et al. (+2 more)
In active noise control (ANC) systems, adaptive approaches may suffer from instability or divergence, limiting their practical deployment. Consequently, fixed-parameter controllers are widely adopted, but their performance degrades under varying noise characteristics and acoustic path conditions. This paper proposes a feedback-guided DNN-based controller fusion framework for robust fixed-parameter ANC. The proposed method combines a causal WaveNet controller with a feedback-guided mixture-of-experts (MoE) module, where a gating network estimates the weights of multiple pre-trained FIR experts according to the current acoustic condition. The proposed approach improves robustness to varying acoustic conditions without online parameter updating. Furthermore, the model is fully causal and supports sample-wise streaming inference, with computational costs evenly distributed across sampling points to reduce peak computational load. Experimental results on headphone ANC demonstrate substantial low-frequency noise reduction with negligible noise amplification over 1-8 kHz.
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All authors Lu Bai, Yiming He, Xiaofeng Nan, Kai Chen, Jing Lu
Systems and Control · Cross-listed from cs.RO
Demonstration of Space Robot Teleoperation over a Lossy and Delayed Network using ATMOS
Inkyu Jang, Gregorio Marchesini, Nicola De Carli et al. (+15 more)
We present a demonstration showcasing the Autonomy Testbed for Multi-purpose Orbiting Systems (ATMOS), a planar spacecraft-analog
robot designed for hardware-in-the-loop evaluation of guidance and control strategies in microgravity-like conditions. Using ATMOS as the physical test platform, we investigate the design, analysis, and performance evaluation of control architectures for remotely operated spacecraft under round-trip communication delays. In this work, we develop and experimentally validate a control strategy that combines state prediction and trajectory tracking control to perform a docking maneuver, accounting for time-varying random communication latency between ground operators and the ATMOS system. The demonstration includes a long-distance remote control experiment between Seoul and Stockholm, introducing realistic intercontinental delays and variability. The results highlight the capability of ATMOS to support rapid, reliable, and cost-effective testing of spacecraft teleoperation concepts, establishing a first step toward robust validation of on-orbit operations in microgravity-like environments.
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All authors Inkyu Jang, Gregorio Marchesini, Nicola De Carli, Byeongjun Kim, Sunwoo Hwang, Dabin Kim, Elias Krantz, Youngkyoung Kong, Frank J. Jiang, Annika Wong, Pedro Roque, Prasetyo W. L. Sanjaya, Nicola Bastianello, Mani H. Dhullipalla, Karl H. Johansson, Hyungbo Shim, Dimos V. Dimarogonas, H. Jin Kim
Image and Video Processing
A Subjective Study on a New Sharpness Informed Class of Metrics
Uditangshu Aurangabadkar, Vibhoothi Vibhoothi, Darren Ramsook et al. (+1 more)
Perceptual loss functions in Deep Neural Network (DNN) deblurring architectures improve the overall quality of restored images. However, few focus on explicitly targeting sharpness in the restorations. We conduct a subjective study of models trained with and without losses which explicitly target sharpness using a four-protocol approach, exploring preferred sharpness levels and effects on image quality. We introduce a novel
dataset of images with uniform sharpness increments along with Difference Mean Opinion Scores (DMOS). Additionally, we propose a novel class of Sharpness Informed (SI) Image Quality Assessment (IQA) metrics which properly penalize over-sharpening. Our new SI-PSNR metric outperforms all other PSNR variants in terms of correlation statistics on IQA benchmarking
datasets. We show that, on average, images restored using a sharpness-aware composite loss are preferred in 67% of binarized comparisons, as opposed to losses that do not explicitly target sharpness.
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All authors Uditangshu Aurangabadkar, Vibhoothi Vibhoothi, Darren Ramsook, Anil Kokaram
Signal Processing
Standard-Compliant Neuromorphic Integrated Sensing and Communications Aided by an Intelligent Reflecting Surface
Jiho Park, Jiechen Chen, Joonhyuk Kang et al. (+1 more)
Neuromorphic computing enables event-driven, low-power inference and is therefore an attractive technology for jointly carrying out communication, sensing, and processing at resource-constrained wireless devices. Impulse
radio ultra-wideband (IR-UWB) signaling, which is standardized in the IEEE 802.15.4 family and widely deployed for ranging and short-range communication, is naturally matched to neuromorphic processing, since both operate through sparse temporal events, or spikes. In this context, this paper studies a standard-compliant neuromorphic integrated sensing and communications (N-
ISAC) system in which a single spiking neural network (SNN) receiver jointly demodulates digital data and detects a passive radar target directly from a common IEEE 802.15.4z high-rate-pulse-repetition-frequency (HRP) UWB waveform. Unlike prior N-
ISAC work, which assumed a basic pulse-position-modulation interface and a simplified multipath channel, we consider the full standardized physical layer together with a realistic ray-traced channel aided by a reconfigurable intelligent surface (RIS). The model accounts for the frequency-selective response of a metamaterial-based RIS, which disperses the wideband IR-UWB pulses and can degrade both communication and sensing. Numerical experiments quantify the impact of RIS frequency selectivity on UWB
ISAC and characterize the trade-off between
ISAC performance and receiver computation energy.
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All authors Jiho Park, Jiechen Chen, Joonhyuk Kang, Osvaldo Simeone
Audio and Speech Processing · Cross-listed from cs.SD
H2H Music Improv: A Communication Model and Audio-Visual Dataset for Music Improvisation
Aleksandra Teng Ma, Anthony Cammarota, Jiayi Wang et al. (+4 more)
Current real-time AI improvisation systems lack the communication awareness human musicians rely on: rather than treating communication as a foundational
algorithm design concern, most systems layer interaction strategies post-hoc onto generative
algorithms through explicit controls and predefined modes. This gap persists in part because no formalized, machine-readable communication model with musicians exists. To address this, we study how expert musicians communicate in free (non-idiomatic) improvisation, unconstrained by prior discussion or agreement. Through a collaborative co-design process with expert improvisers, we derive a communication model that (1) captures how free improvisers negotiate musical ideas and enter stable musical spaces, and (2) is formalized as a machine-readable annotation scheme. We further present the H2H (Human-to-Human) Music Improvisation
dataset: six hours of audio-visual expert duo improvisations with clean per-player stems and per-player annotations of both their own intentions and their perception of their partner's intentions. To our knowledge, this is the first such
dataset for free improvisation. Together, the communication model and the
dataset offer a new lens and resource for studying musician communication and may in future inform the design of AI musical partners that communicate by design.
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All authors Aleksandra Teng Ma, Anthony Cammarota, Jiayi Wang, Alexandria Smith, Cheng-Zhi Anna Huang, Jeffrey Albert, Alexander Lerch
Signal Processing
Conjugate Equivariant Neural Network for Precoder Learning
Shiyong Chen, Mingyu Deng and Shengqian Han
Exploiting mathematical properties of wireless policies in deep neural network (DNN) design can improve learning performance and generalizability while reducing training complexity. Permutation equivariance and permutation invariance have been incorporated into DNN architectures. In this paper, we investigate conjugation equivariance (CE) and propose a general conjugation-equivariant neural network (CENN) framework for precoder learning. We first establish that the optimal policies for a unified class of precoding problems satisfy CE, i.e., when the channel
matrices are conjugated, the conjugate of an optimal precoder remains optimal. We then show that, for DNNs with linear processing functions, enforcing CE restricts their ability to learn optimal precoding policies. To overcome this limitation, we develop a general nonlinear construction and prove that it converts an arbitrary base function into a CE processing function while preserving the base function's original equivariance properties. This construction enables existing equivariant networks to incorporate CE without adding learnable parameters. Simulations for fully digital and RIS-aided precoding show that the resulting CE-enhanced networks improve learning and generalization performance while requiring fewer training samples and shorter training time than their original counterparts.
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Systems and Control · Cross-listed from math.OC
First-Order Optimization as Minimum-Time Control
Liraz Mudrik, Isaac Kaminer and Pramod P. Khargonekar
We formulate first-order optimization as a minimum-time control problem. The iterate is the state, the update, a combination of the gradients observed so far, is the control, and the points with gradient norm at most a prescribed tolerance form the target set. For a fixed objective and start, the minimum number of oracle queries needed to reach the target is a value function: it measures the complexity of the instance, not the worst case over a class. On a strongly convex quadratic, the conjugate gradient iterates emerge from the discrete Pontryagin conditions, and the value is a controllability index. Beyond the quadratic, a Hessian-generated reachable span replaces the controllability
matrix, and feasibility becomes a reachability question: whether a critical point can be reached at all, and in how many steps, is read from the span.
Curvature is a resource: a checkable condition certifies that an instance needs fewer steps than the controllability index of its quadratic model at the minimizer, and the gap can grow without bound with the dimension. The value is thus a
benchmark for the intrinsic difficulty of each instance, against which any first-order method can be measured.
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Signal Processing
Omni-Photonic Base Station: A Three-Functional-Domain Photonic Architecture for Evolutionary 6G Wireless Infrastructure
Dapeng Wang, Xiaoxiong Song, Ziling Fu et al. (+7 more)
6G mobile communications impose immersive communication demands for higher data rates and massive connectivity, while emerging integrated sensing-computing-intelligence scenarios require base stations to concurrently enhance computing capability, guarantee service latency, and realize sensing. We propose the Omni-Photonic Base Station-a progressive evolutionary architecture that introduces photonic technologies into three functional domains, namely baseband processing, fronthaul transmission, and the RF front-end, and obtains system-level gains through cross-domain co-design. By introducing photonics, Omni-PBS harnesses the inherent physical advantages of ultra-broad bandwidth, ultra-low propagation latency, and native parallelism to transcend the aforementioned electronic bottlenecks and fulfill the compound demands of 6G scenarios. The optical baseband computing domain employs photonic accelerators to perform linear computation-intensive tasks, improving the energy efficiency of AI inference by one to two orders of magnitude. The analog optical fronthaul domain replaces digital fronthaul with analog
radio-over-fiber , which eliminates the ADCs/DACs and digital intermediate-frequency chips in the remote unit and substantially reduces fronthaul bandwidth requirements. The microwave-photonic RF domain breaks through the bandwidth and frequency limitations of electronic RF front-ends via optical true-time-delay beamforming, programmable photonic filtering, and optical heterodyne frequency conversion. The three domains yield system-level gains beyond single-domain summation through four co-design principles: end-to-end optical-domain continuity, cross-domain co-design, optical computing resource scheduling, and joint optimization of functional splitting.
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All authors Dapeng Wang, Xiaoxiong Song, Ziling Fu, Wenyang Cheng, Jiayao Wang, Xiaogang Yan, Ze Wang, Min Zhang, Jingdi Liu, Nan Li
Image and Video Processing
Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning
Yuanchao Bai, Yifan Zhao, Kai Wang et al. (+5 more)
Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression methods are often limited in efficiency due to rigid, handcrafted models. Conversely, deep neural network (DNN)-based compression methods, while effective, demand substantial computational resources, hindering deployment in resource-constrained settings. To address these challenges, we propose a novel tri-plane context tree (TCT)-based method for lossless volumetric medical image compression that delivers high performance without relying on DNNs or external training data. To exploit intra-slice and inter-slice redundancies, we introduce a compact tri-plane context representation that decomposes complex 3D context modeling into efficient 2D modeling on three orthogonal planes. By integrating this representation with a context tree framework, we develop an input-specific TCT model employing an adaptive binary tree structure. At each tree node, the model dynamically selects from a suite of tri-plane based predictors and contextual feature extractors, enabling data-adaptive context modeling tailored to local structural characteristics. Instead of offline training, we sample a subset of the input volume to learn the TCT model by optimizing the minimum description length (MDL) through iterative construction and
pruning. With the learned TCT model, each pixel retrieves its corresponding context, computes the prediction residual using the predictor dictated by the context, and performs
entropy encoding based on the associated histograms. Experimental results demonstrate that the proposed method achieves compression performance on par with recent DNN-based methods on multiple
datasets, while maintaining low computational cost and fast coding speeds, making it highly applicable in practice.
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All authors Yuanchao Bai, Yifan Zhao, Kai Wang, Yuanbo Du, Jie Cheng, Teng Fang, Xianming Liu, Wen Gao
Signal Processing
Dual-Layer Over-the-Air Federated Learning in LEO Satellite Networks: Architecture, Key Technologies and Applications
Zhendong Li, Shaojie Wang, Zhou Su et al. (+4 more)
Low Earth orbit (LEO) satellite networks are emerging as a pivotal infrastructure for global edge intelligence. In this context, integrating over-the-air (OTA) computation with adaptive beam hopping (BH) provides an innovative framework that seamlessly merges physical-layer analog aggregation with dynamic resource orchestration. This effectively overcomes the stringent bandwidth and power constraints of space platforms while extending
federated learning (FL) to pervasive Internet-of-things (IoT) deployments. In this article, we first outline the fundamental principles of the dual-layer OTA model and introduce the adaptive BH mechanism designed for time-varying
topologies. Then, we summarize the distinct advantages of this learning-centric architecture, which include decoupling aggregation latency from device density, optimizing spatio-temporal resource efficiency, and balancing data freshness with channel quality. Several application scenarios are explored to highlight the framework's potential across diverse vertical industries. Furthermore, a specific case is studied to demonstrate the practical efficacy of the proposed scheduling policy. The results reveal substantial performance gains in terms of model convergence speed and data utilization for satellite-based FL systems. Finally, we discuss the implementation challenges and outline future research directions, aiming to provide insights for the
evolution of ubiquitous non-terrestrial intelligence.
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All authors Zhendong Li, Shaojie Wang, Zhou Su, Tom H. Luan, Ruijin Sun, Ying Wang, Wen Chen