Papers

We propose a new architecture for practical quantum computing that combines three established principles: symmetry protection of relative-motion qubits via the generalized Kohn theorem, control via twisted-light orbital angular momentum, and metamaterial nanofocusing (e.g. using Weyl-semimetal plasmonics). Crucially, the core mechanism is generic: it applies to any current or future quantum computing system involving parabolic confinement, including cold atoms, ions, and semiconductor dots.

In terrestrial networks, especially in urban areas, cell-edge users often face significant capacity limitations due to high path loss, shadowing, and inter-cell interference (ICI). This paper proposes integrating a high-altitude platform station (HAPS) into terrestrial networks, where terrestrial base stations (BS) can alleviate these issues by relaying data intended for cell-edge users via HAPS, thereby leveraging line-of-sight (LoS) links. We formulate an energy-efficiency (EE) maximization problem to jointly design beamforming vectors at the BS and HAPS with the goal of improving cell-edge user performance. Since the resulting problem is non-convex, we develop an online optimization framework based on a graph neural networks (GNN), which effectively captures the network topology. Numerical results show that the proposed HAPS-assisted architecture improves network performance, particularly by increasing the 5th-percentile EE, thereby enhancing service for cell-edge users.

In this paper, we investigate contraction analysis for nonlinear time-delay systems described by functional differential equations. We first extend the concept of Lyapunov-Krasovskii functionals within the differential framework. We then show that its existence is equivalent to that of an incremental Lyapunov-Krasovskii functional and guarantees uniform incremental exponential stability. Next, we extend the concept of Lyapunov-Razumikhin functions within the differential framework, whose existence also ensures uniform incremental exponential stability. As an application of our results, we formulate stabilizing feedback control design for nonlinear time-delay systems with single delays in terms of linear matrix inequalities.

Continuous variable-rate compression is highly demanded in real-world applications, but remains underexplored in scalable image coding for humans and machines. In this paper, we propose a training-free variable-rate scalable image coding framework. By adaptively adjusting quantization step sizes based on predicted scale values, the proposed method enables independent and continuous bitrate control for the machine and enhancement layers while preserving important latent information in each layer. Experimental results demonstrate the effectiveness of the proposed method and highlight the importance of bitrate allocation between the two layers.

Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-brain microstructure in vivo, yet reliable quantitative mapping remains confined to specialized research settings requiring dense sampling and optimized acquisition protocols. To address this gap, we present a physics-informed generative microstructure network (PIGMENT) that learns a universal generative prior of human brain microstructure and adapts it zero-shot to each participant's measured data to recover subject-specific maps. Trained on 11375 scans spanning multiple sites, vendors, and field strengths, PIGMENT enabled reliable quantitative mapping for tensor, kurtosis, and NODDI models across external datasets from five independent centers. It remains effective where conventional fitting becomes unreliable, recovering meaningful maps from extremely sparse acquisitions while supporting downstream tractography and structural connectivity mapping. PIGMENT estimates demonstrated strong biological validity, preserving submillimeter cortical microarchitectural patterns and early-childhood white matter developmental trajectories from 10-fold accelerated scans. Furthermore, PIGMENT enables reliable quantitative tensor mapping on cost-efficient low-field systems and the extraction of tumor-related biomarkers using ultra-fast clinical protocols. Together, these results establish PIGMENT as a physics-informed foundation model that extends quantitative diffusion MRI into regimes traditionally too sparse, heterogeneous, or clinically constrained for reliable analysis.

Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability. Existing deep learning methods are typically contrast-specific and fail to generalize across diverse modalities and artifact severities. We propose a unified framework combining parameter-informed contrast disentanglement with severity-aware adaptive correction. ScanCLIP, pretrained on over 30,000 MRI text-image pairs, derives contrast embeddings from acquisition parameters to disentangle contrast style from anatomical content, yielding contrast-free features. A Vision Transformer then estimates motion severity and routes features through a Mixture-of-Experts network, enabling targeted artifact correction. A dual-pathway decoder reconstructs both the clean image and residual artifact map, enforcing image-space consistency. On IXI and HCP benchmarks, our method improves PSNR by 0.75 dB and SSIM by up to 0.0279 over state-of-the-art approaches, with larger gains at higher artifact severities. It further demonstrates robust zero-shot generalization on real-world clinical data acquired with unseen scanning parameters, where existing methods either fail to remove artifacts or introduce additional distortions.

This paper develops a functional theory for multi-target detection, where a compactly supported signal is recovered from a single noisy observation containing many unknown translations of the signal. Our formulation allows continuous, off-grid translations and correlated stationary Gaussian process noise, extending beyond the discrete, grid-aligned, white-noise models common in prior work. We analyze two uninitialized recovery algorithms based on autocorrelation analysis; in particular, both algorithms first estimate the signal's bispectrum via a debiased third-order empirical autocorrelation. The signal is then recovered from the estimated bispectrum using either a functional frequency marching scheme or a Kotlarski-type deconvolution formula. For both algorithms, we prove non-asymptotic recovery guarantees for compactly supported signals without bandlimiting assumptions. The resulting error bounds depend on the smoothness of the signal and the accuracy of bispectrum estimation, with the latter governed by the noise characteristics and the number of signal occurrences. Numerical experiments validate our theory and demonstrate accurate recovery in low-SNR regimes.

Microwave linear analog computers (MiLACs) have recently emerged to enable high-performance and efficient beamforming in the analog domain. In this paper, we introduce a dual-functionality framework for MiLAC-aided transceivers. Beyond analog-domain precoding/combining (active beamforming), a MiLAC and its antenna array can simultaneously act as a reconfigurable intelligent surface (RIS) (passive beamforming). This allows the MiLAC to execute beamforming for transmission/reception while reflecting external incident signals. We provide an optimal reconfiguration strategy for this dual-functional MiLAC, and characterize the fundamental limits on the trade-off between active and passive rate, namely the capacity region bounds and the sum-rate capacity.

We present UNISON, a latent diffusion framework that unifies speech generation, sound generation, and audio editing within a single model. A single model handles text-to-audio, text-to-speech, zero-shot speaker cloning, mixed speech-and-sound generation, scene-level audio editing, speech-in-scene editing, and timed temporal composition, all of which share a single set of weights. Our architecture features two core designs: (1) Layer-wise deep LLM fusion, which injects hidden states from uniformly sampled layers of a frozen MLLM into corresponding MM-DiT blocks via learned projections, providing depth-matched semantic conditioning that improves instruction following over single-layer baselines; and (2) a unified multi-task architecture where task identity is encoded solely by a channel-wise mask and source audio is provided through VAE-encoded channel concatenation. Training is stabilized by an online GPU-side multi-task data synthesis pipeline with task-homogeneous batching and a two-stage curriculum. With 621M–732M trainable parameters, UNISON achieves results competitive with or exceeding task-specialist models across evaluated domains, while being roughly $4\times$ smaller than comparable unified systems.

Satellite communications are envisioned as a key enabler for ubiquitous coverage in future 6G networks, yet the broadcast nature renders them vulnerable to eavesdropping, especially given the long-distance transmissions and associated high uncertainties. In this paper, we propose the physical layer security enhancement for multi-beam satellite communications with the assistance of an aerial reconfigurable intelligent surface (ARIS). Considering the high dynamics and uncertainties of channels, we characterize the channel distribution with moment-based ambiguity sets. Accordingly, a distributionally robust secrecy rate optimization is formulated through joint design of transmit and reflection beamforming. We then introduce a conditional value-at-risk-based reformulation to convert the probabilistic constraints into deterministic forms. An alternating optimization framework is subsequently employed to iteratively update the transmit and reflective beamforming vectors until convergence. Simulation results demonstrate that the proposed distributionally robust scheme significantly enhances secrecy performance, and maintains reliable performance across various channel error distributions.

Cell-free massive multi-input-multi-output (CFmMIMO) communication networks aim to provide uniform quality of service by distributing access points (APs) across a coverage area. In user-centric variants, each user equipment (UE) can choose a cluster of APs with the best channel conditions (e.g., the closest APs) for accessing service. This approach eliminates the notion of cells with dedicated regions and APs, as found in cellular mMIMO communication networks. Estimating uplink channels between UEs and APs is a crucial step in CFmMIMO communication networks; however, existing channel estimation (CE) approaches typically originate from mMIMO systems without considering the unique properties of CFmMIMO communication networks. For instance, shorter AP-UE distances in CFmMIMO systems result in Rician channel models with prominent line of sight (LoS) components between APs and UEs, motivating cooperation between APs for improved performance. In this paper, we propose a cooperative minimum-mean-squared-error (MMSE)-based uplink CE approach where APs share their linearly compressed signals as fused signals with other APs in the same cluster. The proposed approach is optimal, i.e., its performance is equivalent to that of the centralized CE approach, where APs share their uncompressed raw signals. Notably, this optimality is achieved in one shot; that is, given the required correlation matrices, the optimal fusion filters and estimators are derived non-iteratively. Consequently, the proposed approach guarantees lower communication overhead for cooperative CE compared to the centralized approach. Numerical experiments corroborate the superior performance of the proposed cooperative CE approaches in terms of CE accuracy and convergence rate.

For high-throughput applications such as ultra-high-definition video streaming and immersive extended-reality, perceptual quality rather than bit-level accuracy defines the primary performance criterion and provides a more informative and spectrally efficient objective than strict bitwise reconstruction. This is particularly relevant in millimeter-wave (mmWave) and sub-Terahertz (sub-THz) systems, where path loss, short channel coherence times and phase noise introduce severe fluctuations that degrade link spectral efficiency. We propose an extension to conventional Adaptive Modulation and Coding (AMC) framework that incorporates perceptual quality awareness into link adaptation. In this framework, the decision metric is a Perceptual Quality Indicator (PQI) derived from the Structural Similarity Index Measure (SSIM). The receiver employs a Denoising Convolutional Neural Network (DnCNN) denoiser to enhance post-decoding image quality before feedback estimation. The resulting perceptual metric replaces the standard Channel Quality Indicator (CQI) in the AMC loop, enabling adaptation to maximize spectral efficiency while satisfying a perceptual-fidelity constraint. Experiments on a 5G-compliant mmWave testbed demonstrate up to a twofold gain in spectral efficiency while maintaining perceptual fidelity, underscoring the potential of perception-optimized link adaptation.

Large language models (LLMs) can turn grid-analysis requests into executable programs for power-system simulation, but utilities and research laboratories often require on-premise deployment. In this setting, first-pass failures frequently arise at an API-knowledge boundary, through hallucinated functions, misused parameters, and mishandled result tables. We present PowerCodeBench, a parameterised benchmark generator released as a frozen 2,000-task suite for pandapower, and a deployment-time workflow that requires no weight updates. Documentation-driven L0-L3 probes produce per-model API profiles for diagnosis, model comparison, documentation allocation, and backend calibration. A query-side demand estimator selects layered API evidence before generation, while execution feedback routes targeted repair. Across ten open-weight LLMs (1.5B-480B) and four mid-tier APIs, the validation-enabled workflow raises scalar-match accuracy by 32-56 percentage points after up to three repair rounds relative to an unassisted first pass, for every model of at least 7B and every API. Open-weight models in the 70B-120B range reach the four-vendor mid-tier accuracy range under matched no-tool conditions. Selective injection approaches the full-layer reference using 41% of its prompt tokens. Among model-item pairs passing numerical checks under both workflows, engineering review confirms the requested analysis in 88% of full-workflow outputs versus 66% under plain repair. Round-0 pilots on OpenDSS and PyPSA motivate staged onboarding from broad retrieval at cold start to calibrated selective injection. Measured throughput, latency, energy, and allocated GPU memory establish a practical on-premise serving envelope.

Conversational automatic speech recognition in Hungarian is constrained by the limited amount of publicly available dialogue-style training data. The BEA-Dialogue corpus addresses this need, but its strictly speaker-disjoint train/dev/eval split reduces the usable material to only 85 hours. In this paper, we introduce BEA-Dialogue+, an expanded version of the corpus that relaxes the split criterion for experimenters and dialogue partners while preserving complete separation of the primary speakers. This results in 200 hours of transcribed natural conversations and enables a controlled study of the trade-off between additional training data and speaker overlap across the splits. We evaluate several Whisper- and FastConformer-based models on both corpus versions, including Serialized Output Training (SOT)-based fine-tuning for dialogue transcription. Our results show that the larger corpus is more challenging for models without fine-tuning, whereas SOT-based adaptation yields consistent improvements in WER, CER, cpWER, and cpCER. Overall, BEA-Dialogue+ provides a substantially larger yet still demanding benchmark for Hungarian dialogue ASR, and a practical resource for training and evaluating dialogue transcription systems.

Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a high inference latency that limits practical real-time deployment. In this work, we observe that robotic reasoning workloads contain substantial temporal redundancy, where consecutive observations frequently produce identical actions and subgoals. Based on this insight, we present REIS, a human cognition inspired robotic decision-making framework that minimizes unnecessary reasoning while preserving semantic adaptability. REIS combines lightweight scene gating, KV-steered affordance routing, and deliberative reasoning to accelerate robotic control under embodied constraints. Experiments on ALFRED, and real-world robotic tasks demonstrate that REIS significantly suppresses reasoning overhead while maintaining competitive task performance.

Ladder logic translation is an important problem in industrial automation because without it, it is difficult to switch Programmable Logic Controller (PLC) vendors. The prevailing translation problem highlights mismatched programming environments, incompatible ladder logic constructs, limitations in terms of differences in the semantic expressiveness of the vendor formalisms and integrated black-box proprietary engineering tools which are exemplified in our example case; Rockwell to Siemens PLC code translation. This work presents a mathematical formulation of the problem, the detailed architecture of a solution which supports XML extraction, structural normalization, constrained generative function (LLM), and system integration via the TIA Portal Openness API as rigorously engineered pipeline for automated translation of Rockwell Ladder Programs to Siemens S7 ladder programs. Finally, we present results that show that the translations retain high semantic consistency across instruction categories.

Image Scanning Microscopy (ISM) is a fluorescence imaging technique that combines detector-array acquisition and computational reconstruction to achieve the theoretical resolution of an ideal confocal microscope, i.e., one operating with an infinitesimally small pinhole, while maintaining high signal-to-noise ratio. Among the reconstruction methods for obtaining the super-resolved image, multi-image deconvolution (MID) and its extension aimed at preserving the optical sectioning capability of confocal microscopy, known as super-resolution sectioning ISM (s$^2$ISM), are among the most widely used approaches. Both methods rely on Richardson–Lucy-type iterative schemes, whose semi-convergent behavior requires early stopping and often leads to noise amplification and reconstruction artifacts. In this work, we introduce a self-tuning explicit regularization framework for both MID and s$^2$ISM reconstruction. Within a Bayesian maximum a posteriori formulation, we combine a multi-frame Poisson data fidelity term with explicit regularization, considering $\ell_1$ and smoothed total variation penalties as representative examples. We further develop an automatic and ground-truth-free strategy for regularization parameter selection by adapting the residual whiteness principle to the multi-frame Poisson setting and introducing a spectral high-pass extension tailored to s$^2$ISM. The resulting framework enables stable reconstructions without empirical stopping rules. To demonstrate the proposed framework, we consider first-order optimization schemes based on proximal gradient and mirror descent methods with adaptive backtracking strategies. Experiments on simulated and real fluorescence ISM datasets demonstrate improved reconstruction stability and image quality with respect to unregularized approaches, while enabling robust super-resolution and optical sectioning in low-photon conditions.

The electrification of on-road fleet logistics promises improved air quality, lower noise emissions, major climate benefits, increased energy flexibility through the use of locally generated electricity and reduced dependence on imported fuels. However, battery electric vehicles can introduce operational planning challenges not present with internal combustion engine vehicles, including heterogeneous charging speeds, exposure to volatile electricity prices, and scarcity in infrastructure. Managing these complexities requires solutions that balance cost efficiency and robustness, supported by sector coupling between transport and electricity systems. This paper reviews the current state of digital systems for operational decision-making in electric fleet management through a grey literature analysis, drawing on practitioner-oriented sources such as industry reports, company documentation, and technical blogs that reflect real-world practices and developments. We identify key trends and gaps, providing insights to guide future research and development.

This paper proposes an integrated sensing and communication (ISAC)-enabled grant-free uplink framework based on artificial-path delay modulation. A grant-free user equipment (g-UE) conveys uplink information by modulating the delay of a controllable artificial path derived from the scheduled downlink waveform. In contrast to conventional superposition-based schemes with successive interference cancellation, the proposed method enables uplink-downlink coexistence in the delay-sensing domain. By introducing a single weak artificial path confined within the cyclic prefix (CP), the g-UE allows the access point (AP) to decode uplink symbols from CSI perturbations while causing only limited degradation to the scheduled user equipment (s-UE) in the downlink. To support reliable finite-alphabet delay detection under unknown path gain and off-grid leakage, we develop a baseline delay calibration procedure and a normalized matched-filter detector. Results show that reflection power determines the reliability trade-off between the g-UE and the s-UE, whereas the delay step mainly controls the g-UE reliability-efficiency trade-off with little additional impact on the downlink s-UE. Even with an artificial path 15 dB weaker than the scheduled downlink signal, the g-UE achieves lower BER than the s-UE at an effective modulation order of 16-QAM. The proposed framework thus offers a low-complexity, SIC-free, and downlink-friendly solution for grant-free uplink in ISAC systems.

Detecting unauthorized UAV flights is critical for surveillance, security, and airspace management. Acoustic drone detection, which relies on the distinctive propeller and motor sounds of UAVs, provides a low-cost, passive solution that requires no line of sight. A central challenge is generalization: reliably distinguishing drone signatures from ambient noise across unseen recording setups, environments, and UAV types (out-of-domain). Inspired by advances in large-scale audio pretraining, we develop a compact DNN-based detector and improve its generalization by (1) pretraining the model for broad sound-event classification before fine-tuning on diverse in-house and public drone recordings, and (2) applying on-the-fly augmentations (pitch shifting, noise mixing, microphone transfer function simulation, spectrogram augmentation) to expose the model to varied acoustic conditions. An ablation study quantifies the impact of each augmentation. For evaluation, we set target false-positive rates (FPR) aligned with real-world surveillance needs and report true-positive rates (TPR) on both in-domain data (public IDMT Berne 2022) and out-of-domain data (public AuDroK). Our results show that pretraining is the dominant factor for robust detection, yielding substantial TPR improvements over training from scratch on all benchmarks. The full augmentation chain provides additional gains on acoustically mismatched out-of-domain data, achieving the best mean TPR on the AuDroK subsets and the largest improvements on the most challenging scenarios. We further validate real-world applicability by measuring false positives on public non-drone corpora (IDMT-TRAFFIC and ESC-50), demonstrating equally low FPR on unfamiliar backgrounds. A distance-dependent analysis on IDMT Berne 2022 shows effective detection at distances up to 150 m.

This paper studies model-free optimal control design and its convergence properties for linear time-invariant systems subject to probabilistic risk or chance constraints. In particular, we study a natural policy gradient (NPG)-based actor-critic (AC) algorithm with two timescales, using a Lagrangian primal-dual framework to enforce the constraint. Furthermore, the risk is defined as the probability that a function of the one-step-ahead state exceeds a user-specified threshold. To our knowledge, this is the first work to study the analytical convergence properties for NPG-based AC in a chance-constrained linear-quadratic Gaussian (LQG) regulator setting without model knowledge. We establish the coercivity and gradient dominance properties of the Lagrangian function, which ensure linear convergence and closed-loop stability during training for the actor. On the other hand, we analyse the convergence properties of the temporal difference (TD(0)) learning for the critic, applying stochastic approximation theory. Also, we demonstrate no duality gap in the constrained optimisation problem. Additionally, we have performed numerical analysis of the convergence properties and accuracy of the proposed method, comparing it with model-based chance-constrained LQR and scenario-based MPC. Results show that our approach effectively limits risk while maintaining near-optimal performance, without requiring full model knowledge or real-time optimisation.

We introduce the notion of worst-case posterior and worst-case likelihood sensitivity. These measure, respectively, the sensitivity of the expected cost to worst-case perturbations of the posterior distribution and worst-case perturbations of the likelihood of a Bayesian model. Each defines a quantitative measure of robustness. A decision maker concerned about the sensitivity of the out-of-sample expected cost to deviations from her assumptions will want a decision for which both sensitivities are small. We derive posterior and likelihood sensitivities for uncertainty sets defined in terms of deviation measures. Posterior sensitivity vanishes when the posterior variance shrinks to zero, which occurs when parameter uncertainty is eliminated from learning. Parameter learning does not eliminate likelihood sensitivity. A distributionally robust formulation of a Bayesian optimization problem makes a near-Pareto-optimal tradeoff between performance (expected cost) and robustness (posterior and likelihood sensitivity).

Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-space data while preserving state-dependent fidelity for dynamic and quantitative MRI (qMRI). Existing scan-specific implicit neural representations (INRs) often use monolithic spatiotemporal coordinate fields, explicit subspaces, motion or deformation models, calibration variables, or sequence-specific quantitative signal models. These design choices can limit flexibility in sharing spatial information while adapting image synthesis across acquisition states. Moreover, many INR-based baselines remain computationally demanding, typically requiring per-scan optimization times on the order of hundreds to thousands of seconds. We propose MoE-dqINR, a scan-specific multicoil MRI reconstruction framework that factorizes the image-domain representation into shared spatial experts and a state-conditioned routing pathway. Spatial experts encode reusable coordinate-dependent image content, whereas routing weights, conditioned on ordered acquisition states, synthesize each dynamic frame or contrast state from a common expert bank. The representation is coupled to a multicoil MRI forward model, uses the normalized state index to drive routing in both dynamic and quantitative MRI. By separating shared spatial representation from state-dependent synthesis, the framework provides an image-first architecture for dynamic and quantitative MRI while reducing scan-specific INR optimization to approximately 30 s per scan in our experiments. The proposed formulation establishes state-conditioned mixture-of-experts INR as a scan-specific multicoil MRI reconstruction prior that unifies shared spatial representation, dynamic- and qMRI-specific synthesis, and practical per-scan efficiency.

To make cross-band channel prediction practical for AI-native RAN, algorithms must generalize across diverse environments and support real-time inference. Existing approaches achieve one but not both. To bridge this gap, we introduce GUIDE, a physics-guided deep unfolding framework that embeds wireless channel physics into differentiable layers. Without retraining in unseen environments, GUIDE achieves 2.75x beamforming gain than the deep learning-based baseline FIRE with only a slight increase in inference time, and 1.39x beamforming gain than the strongest model-based baseline R2F2 while running over 1610x faster.

This paper studies the control of discrete-time linear fractional-order networks, a flexible modeling framework for systems with long-range memory such as power grids, biological networks, and neuronal circuits. In contrast to the common view that fractional exponents (time-scales) are fixed parameters, we show that they can be systematically steered, together with the network coupling matrix, by appropriately designed input sequences. We first derive algebraic conditions under which the coupling matrix and the vector of fractional exponents of a given network can be reconfigured to desired values, and we characterize how truncating the infinite-memory term impacts the resulting dynamics. Building on these results, we construct an equivalent linear representation that isolates the contribution of memory, and we introduce a fractional reachability matrix that provides explicit conditions for jointly steering both network parameters and state in a finite number of steps. To address practical implementations, we further formulate an energy-constrained steering problem that incorporates actuator bounds and finite-memory approximations as a quadratic program. The framework is illustrated on low-dimensional toy examples, on larger networks with Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz topologies, and on a brain network model inferred from electrocorticography recordings of an epilepsy patient, where we showcase transitions between pre-seizure and seizure configurations.

The experimental validation of diffuse scattering models has long been limited by the inability to spatially separate specular and diffuse contributions in measured channels. This paper overcomes this limitation by combining super-resolution multipath component (MPC) extraction, which resolves individual propagation paths including the specular component, with digital-twin-assisted geometry, enabling the spatial separation of specular and diffuse contributions from bistatic measurements at 28~GHz. Using this framework, we provide the first measurement-driven validation of the Effective Roughness (ER) model with independent characterization of diffuse scattering across ten common building materials, each measured over 266 angular configurations and all polarization combinations (HH, HV, VH, VV). Furthermore, we extend the ER framework by proposing a novel angle-dependent cross-polarization discrimination (XPD) model, capturing the geometry-dependent nature of depolarization that is neglected in existing approaches. The proposed method reproduces the measured diffuse power trends, achieving RMSE values as low as 3 dB across the tested materials, and improves XPD prediction over the baseline constant-XPD model for nearly all material-polarization cases. These results establish a physically consistent and practically viable approach for high-fidelity channel modeling in mmWave systems.

This study introduces a novel Air Traffic Control (ATC) concept to support self-separation between vehicles in Urban Air Mobility (UAM) corridors. Our proposed scheme involves sharing intended arrival schedules at Constrained Waypoints (CWPs) among UAM operators. We propose two approaches to assist the arrival scheduling at CWPs by computing the minimum arrival time gap necessary for each pair of vehicles to ensure their safety throughout the flights within the corridor. The first approach considers the minimum separation distance required by the Near Mid-Air-Collision (NMAC) avoidance rules, while the second one is based on the Responsibility-Sensitive Safety (RSS) rules. We demonstrate that the NMAC-rule-based approach can effectively prevent collisions in normal circumstances, where the vehicles adhere to the speed limits of the corridor. However, this approach does not guarantee safety if vehicles exceed the speed limits. Conversely, while the RSS-rule-based approach ensures collision prevention during emergencies when vehicles exceed speed limits, it may require larger arrival time gaps under normal circumstances, which may lead to reduced traffic flow. Our results are confirmed through numerical simulations.

This paper investigates distributed beam focusing for coordinated satellite constellations with phased arrays, motivated by future non-terrestrial network (NTN) systems. A geometric and channel model is developed by incorporating satellite positions, array orientations, antenna directivity, and polarization effects. Under ideal synchronization, the achievable coherent combining gain is analyzed for different constellation geometries, showing that maximum ratio transmission (MRT) enables quadratic scaling of the received power with the number of satellites. The impact of phase errors caused by residual synchronization, timing, mobility, and localization mismatches is then investigated. Closed-form expressions for the average coherent gain are derived for uniformly distributed timing offsets, demonstrating the transition from coherent to non-coherent combining. The results show that synchronization and timing mismatches reduce the coherent combining gain, while geometry dependent effects govern the resulting spatial focusing behavior. Numerical results further show that linear and circular constellations provide different focusing characteristics and spatial separation capabilities. However, MRT-based focusing results in strong sidelobes and limited spatial division capability, motivating the need for joint analog beamforming and digital precoding optimization to improve spatial selectivity and robustness against mobility and localization errors.

In public address systems and hearing aids, the maximally achievable amplification or gain is limited by acoustic feedback. Therefore, in order to be able to apply a higher gain, feedback cancellation methods are required. In addition, it is oftentimes also desirable to dereverberate a recorded signal, that is, remove the late reverberation component of the signal, before playing it back. In this paper, it is shown that under two mild conditions, the acoustic feedback signal can be written as a reverberant version of the source signal. Therefore, it is possible to treat the joint dereverberation and acoustic feedback cancellation problem as a dereverberation-only problem, meaning that dereverberation algorithms can be applied to the joint problem. Simulations corroborate this finding

Non-terrestrial networks (NTNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling ubiquitous connectivity and massive communication. In this context, channel prediction emerges as a key technique to improve the spectrum utilization efficiency by limiting the pilot overhead. However, many proposed predictors based on artificial intelligence (AI) are characterized by high inference complexity, posing challenges to onboard implementation. In this paper, we address the challenge of designing accurate yet computationally efficient channel prediction techniques tailored to low Earth orbit (LEO) NTNs, where strict power constraints limit model complexity, to enable spectral efficiency gains. We propose an iterative joint channel estimation and prediction framework in the context of 6G NTNs that significantly reduces pilot overhead by transmitting pilots only in the initial slot and relying on data-driven processing for subsequent slots. We introduce Data-driven Refinement and Iterative Forecast for wireless channel Tracking (DRIFT), a lightweight architecture that refines data-aided channel estimates and predicts future channel frequency responses with low computational cost and reduced error propagation. Two predictor variants based on convolutional and long short-term memory layers are investigated. Simulation results in an end-to-end simulation of an uplink LEO NTN scenario show that the proposed approach achieves up to 12% spectral efficiency gain compared to conventional pilot-based systems, with robustness to training-test mismatches and consistent performance across different channel models. Moreover, DRIFT requires fewer than 200k multiply-accumulate operations, making it suitable for on-board satellite implementation under stringent power constraints.