Papers

Terahertz time-domain spectroscopy (THz-TDS) based on air-plasma generation and balanced air-biased coherent detection offers gap-free broadband coverage, but individual continuous-scan traces are strongly affected by pulse-to-pulse fluctuations and electronic noise. Reaching a useful signal-to-noise ratio therefore requires averaging multiple traces, which directly increases measurement time. We propose a learned denoising approach that recovers high-quality THz waveforms from as few as one complete continuous delay sweep, referred to here as a single-scan trace. A compact one-dimensional residual U-Net is trained using two complementary strategies: a reference-supervised baseline that maps individual noisy traces to long-average reference waveforms, and a Noise2Noise approach that learns from pairs of independently acquired noisy traces without requiring a clean training target. Averaging the predictions of both models reduces systematic bias and yields a trace-reduction factor of approximately $5.4\times$ at $K=1$, meaning that one denoised trace achieves the reconstruction accuracy of averaging approximately five raw traces. The Noise2Noise model alone achieves $4.9\times$, outperforming both the reference-supervised baseline ($4.6\times$) and classical Wiener filtering ($3.2\times$). These results show that self-supervised learning from repeated noisy measurements can support faster continuous-scan THz-TDS without hardware modification.

This paper proposes a two-layer model predictive control (MPC) framework for the real-time operation of data centers integrated with on-site photovoltaic generation, battery energy storage, waste heat recovery, and district heating. The upper layer employs scenario-based stochastic optimization to jointly optimize intraday market participation, workload scheduling, and energy management under uncertainty. The lower layer adopts an adaptive tube-based MPC strategy that compensates short-term disturbances while tracking the dispatch references given by the upper layer. The framework further integrates multi-horizon forecasting to support real-time decision making. Microservice-based simulation studies under representative clear-sky and overcast operating conditions demonstrate that the proposed framework accurately tracks dispatch plans despite fast photovoltaic and workload fluctuations. Compared with single-layer control strategies, the adaptive lower-layer controller substantially reduces real-time dispatch deviations and the associated imbalance costs. In addition, the proposed framework naturally adapts to seasonal operating conditions and responds to carbon-aware operating signals, offering a practical approach for economically efficient, sustainable, and grid-supportive operation of future data centers.

Multi-step rollouts are essential for model-based reinforcement learning (RL) and predictive control, yet learned dynamics models often become unstable when recursively applied, leading to divergence and unreliable policy updates. This paper proposes a model-agnostic hybrid dynamics framework that blends a provably contracting nominal model with a flexible excursion model through an uncertainty-guided switching law. The switching signal is derived from calibrated epistemic uncertainty and activates only when the system leaves the nominal region, ensuring that each model operates within its reliability regime. Under clearly stated smoothness and boundedness assumptions, we show that the resulting hybrid predictor yields globally bounded recursive multi-step rollouts: trajectories remain Lyapunov-stable in the nominal region and exhibit at most affine growth during excursions. To illustrate the theory in practice, we instantiate the hybrid dynamics framework within a model-based RL scheme that uses real one-step transitions for value learning and hybrid rollouts for policy improvement. Experiments on a nonlinear Duffing oscillator demonstrate stable long-horizon prediction and improved cost-effort trade-offs relative to a stabilizing baseline.

The stringent energy-efficiency requirements of future Integrated Sensing and Communications (ISAC) systems are fundamentally challenged. Unlike conventional communication systems, ISAC transmitters must radiate significantly higher power to ensure reliable target detection, forcing the High-Power Amplifier (HPA) to operate closer to saturation, where nonlinear distortions become unavoidable. Consequently, the robustness of every candidate ISAC waveform to HPA nonlinearities must be carefully assessed. In this context, this paper investigates the robustness of Affine Filter Bank Modulation (AFBM), a recently proposed waveform that combines the delay-Doppler resilience of affine modulation with reduced Peak-to-Average Power Ratio (PAPR) and improved spectral containment. We develop a statistical characterization of the Ambiguity Function (AF) of the amplified AFBM waveform, deriving approximate expressions for its mean, variance, and Rician-distributed magnitude. Furthermore, a low-complexity Gaussian belief propagation receiver accounting for HPA nonlinearities is proposed for communication detection. Simulation results validate the analytical framework and demonstrate that AFBM preserves favorable sensing characteristics and robust Bit Error Rate (BER) performance even under severe nonlinear amplification.

Learning-based Model Predictive Control (MPC) using Gaussian processes (GPs) is an effective approach for safe control in the presence of model mismatch. High-probability safety guarantees typically require uncertainty bounds that hold uniformly over the entire state–input domain, but existing bounds are available only for full GP regression. Since exact GP inference scales poorly with the number of data points, its deployment is impractical in large-data regimes. We close this gap by developing a scalable GP framework that admits the derivation of uniform uncertainty bounds. We formalize a deterministic trigonometric feature Gaussian process (DTF-GP), a finite-dimensional kernel approximation based on discretized trigonometric features that reduces GP regression to Bayesian linear regression in feature space. We derive a high-probability uniform uncertainty bound for the proposed DTF-GP and provide its closed-form solution for the squared-exponential kernel case. Finally, we integrate the DTF-GP into a learning-based MPC scheme and demonstrate that it provides high-probability safety guarantees and exploration performance comparable to a full GP while improving computational efficiency in large-data regimes.

Direct Device-to-Satellite (D2S) communications promise global connectivity to unmodified user equipment (UE), extending coverage beyond terrestrial networks. Realizing this promise is fundamentally challenging: severe path loss and limited UE transmit power push uplink SNRs far below terrestrial norms, while suitable spectrum remains scarce. Together, these constraints impose a spectral-efficiency (SE) bottleneck, and under such conditions the efficiency of the UE power amplifier becomes critical, jointly governing transmit power and battery life. To improve UE-side power efficiency, 3GPP has adopted Discrete Fourier Transform-spread OFDM (DFT-s-OFDM) as an optional uplink waveform, exploiting its substantially lower Peak-to-Average Power Ratio (PAPR) relative to OFDM. To break the SE bottleneck, we show that aggressive non-orthogonal transmission, in which the number of concurrent users exceeds the number of receive antennas by more than 2x, can unlock substantial capacity gains that remain entirely unexploited. Realising these gains, however, requires receiver architectures that, to the best of our knowledge, have not yet been developed. DFT-s-OFDM intensifies the difficulty: the DFT spreading couples signal components across subcarriers, inflating the effective dimensionality of the detection problem. We address both challenges with a novel receiver design that jointly exploits the SE gains of aggressive non-orthogonal transmission and the power-efficiency benefits of DFT-s-OFDM. Simulations under realistic channel-estimation errors and high-mobility Doppler show that the proposed scheme achieves 2x the SE of baseline, surpasses recent nonlinear MIMO receivers by 40% at 15% of their complexity, and reduces PAPR by up to 6 dB relative to DFT-s-OFDM MIMO and 11 dB relative to OFDM.

Reconstructing heard speech from non-invasive electroencephalography (EEG) is challenging due to a low signal-to-noise ratio (SNR) and inter-session variability. While trial averaging improves the SNR, it is difficult to apply to continuous speech. We instead use repeated EEG responses to the same stimulus across different sessions as positive pairs for contrastive learning, and introduce variational regularization that, combined with this contrastive objective, keeps the encoder representation space broad. Experiments on a Japanese EEG dataset show that combining the session-invariant strategy with variational regularization improves the character error rate (CER) while maintaining mel-spectrogram reconstruction fidelity. Session probing confirms that the encoder representations achieve session-invariance.

Allocation schemes that greedily maximize a readiness metric over the actuator fiber bundle of an overactuated multirotor produce commands that jump between disconnected optimal strata, demanding actuator rates no motor can deliver; effort-minimizing schemes are continuous but cannot guarantee that wrench-rate authority stays above any certified level. We reconcile the two by treating authority as a forward-invariant quantity: a control barrier function on the log-determinant of the drag-aware actuator-authority co-metric, enforced at torque level by a quadratic program in the allocation null space. A single design inequality renders the certified set compact and strictly interior to the actuator box, with the readiness cost of any rotor deactivation given in closed form as $\ln(n/(n{-}m))$ for symmetric designs. Tracking is sacrificed only through an explicit alignment ratio, with wrench error bounded by $\mathcal{O}(\rho^{-1/2})$ and a robust variant handles motor-parameter uncertainty with a closed-form floor shift independent of the airframe matrix. On a hexarotor and a fully-actuated octorotor the closed-form gap matches simulation to machine precision; in the authority-scarce regime greedy maximization violates the certified floor and commits wrench errors up to eighty times larger than the proposed filter, which holds invariance of the certified set at negligible tracking cost.

Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.

We study an Estimated Time of Arrival (ETA)-based traffic-coordination framework for Urban Air Mobility corridors with merging at constrained waypoints (CWPs), where approved ETAs at CWPs serve as Required Times of Arrival (RTAs). Vehicle operators submit ETA plans at the merging point for approval by corridor-management authorities before corridor entry. Corridor entry is then scheduled by enforcing pairwise ETA gaps that maintain inter-vehicle separation on shared corridor sections. We develop two trajectory bounds to compute sufficient ETA gaps: a worst-case bound based on prescribed speed limits, and a stochastic bound based on probabilistic position envelopes under acceleration uncertainty. Using these bounds, we formulate sufficient ETA-gap computation and first-come, first-served corridor entrance scheduling. Simulations show that ETA coordination improves safety over an unscheduled baseline. The worst-case bound provides stronger robustness under higher disturbance levels, whereas the stochastic bound allows higher throughput under mild disturbances while relying on probabilistic modeling assumptions.

Binaural speech enhancement for hearing aids aims to reduce noise while preserving the interaural cues needed for spatial localization. Although deep neural network-based methods achieve strong noise reduction, they often distort the rela- tionship between the left and right signals. In this paper, we propose two novel binaural cue preservation losses. First, a binaural reconstruction error loss that directly penalizes masking-induced distortion in the relationship between the left and right spectra, providing a more direct measure of the binaural consistency than conventional separate interaural level differences (ILD) and interaural phase differences (IPD) errors as in prior work. Second, a binaural cue loss that jointly models ILD and IPD to better preserve the binaural structure. Experimental results show that both proposed losses maintain strong noise reduction performance and reduce masking- induced distortion compared to the state-of-the-art baseline cue loss, while the second proposed joint binaural cue loss also outperforms the baseline in ILD preservation.

Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.

This paper investigates the joint design of beamforming and radar receive filters in a multiuser bistatic integrated sensing and communications (ISAC) system, aiming to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) under communications SINR and transmit power constraints. We consider two scenarios: transmitted signals are either known or unknown at the radar receiver. We develop tractable solutions to the resulting non-convex optimization problems in both cases. For the known-signal case, we derive closed-form radar receive filters and iteratively design beamforming using fractional programming (FP) and successive convex approximation (SCA). For the unknown case, we adopt an alternating optimization (AO) approach to jointly design the beamforming and receive filters. Numerical results demonstrate that, while both approaches achieve comparable performance under per-slot optimization, knowledge of the transmitted symbols provides significant gains in multi-slot processing via coherent integration. Moreover, the proposed ISAC designs perform close to the radar-only benchmark under moderate communication requirements.

Differentiable nonlinear model predictive control (NMPC) provides a principled way to embed optimal control structure into end-to-end learning paradigms, but its practical use is often limited by the computational and memory costs of both forward optimization and backward sensitivity propagation. This brief proposes PANDA, a matrix-free solver for differentiable NMPC. In the forward pass, PANDA combines proximal-gradient iterations with quasi-Newton acceleration and introduces an adaptive stepsize enlargement mechanism to mitigate the conservativeness of monotone stepsize reduction. The resulting stepsize behavior and its effect on local convergence are theoretically analyzed. In the backward pass, PANDA performs implicit differentiation from the residual equation and computes adjoint sensitivities using Krylov-subspace iterative methods together with automatic-differentiation-based Matrix-Vector product operators, thereby avoiding explicit Hessian and Jacobian construction. The method is evaluated on a nonconvex trailer NMPC problem embedded in an imitation learning task. The results show that PANDA achieves much faster forward and backward computation and lower memory overhead than representative differentiable optimization solvers, while maintaining effective imitation learning performance.

This letter investigates reliability constrained hybrid beamforming for transceiver separated multistatic integrated sensing and communication in vehicular networks. A target position Cramer Rao bound minimization problem is formulated under outage probability, transmit-power, and analog constant modulus constraints. To handle the constrained non convex problem, we develop a proportional-integral Lagrangian proximal policy optimization algorithm. Simulation results show that the proposed algorithm keeps the average outage probability at or below the reliability threshold, around 8%-10%, improves constraint satisfaction, and achieves stable sensing performance.

We study decentralized stochastic gradient tracking over a time-varying network of $N$ agents under a uniform window-mixing condition. Products of $\tau$ consecutive doubly stochastic mixing matrices contract disagreement by a factor $\lambda<1$, although individual matrices need not contract disagreement strictly and individual communication graphs may be disconnected. We construct a time-varying quadratic norm that turns this window contraction into an exact one-step Lyapunov identity. This leads to coupled one-step recursions for the centroid and disagreement errors, without unrolling the dynamics over communication windows. For smooth strongly convex objectives, the leading stochastic term is $\widetilde{\mathcal O}(1/(NK))$; for smooth convex objectives, it is $\mathcal O(1/\sqrt{NK})$. Both match their centralized mini-batch counterparts and yield linear speedup after a network-dependent transient.

Ambisonics delivers compact scene based spatial audio representation, yet higher order Ambisonic encoding poses difficulties for wearables and embedded hardware. Their microphone arrays are often sparse, irregular, and constrained by device specific boundary conditions. These factors make the spherical-harmonic (SH) domain encoding ill conditioned: inverse filtering amplifies noise, while deterministic neural encoders may overfit to array-specific responses or smooth ambiguous higher-order components. This paper presents DiffM2A, a geometry-adaptive conditional diffusion framework for robust Ambisonic encoding from sparse MAs with variable topologies. Its Geometry-Adaptive Spherical Harmonic Projection (GASHP) front-end constructs boundary-aware SH steering functions and applies an energy-normalized modal projection, mapping array-dependent observations to a common modal representation without explicit pseudo-inverse computation. A dual-branch Elucidated Diffusion Model then estimates complex Ambisonic coefficients, conditioned on both the raw microphone spectra and GASHP features. Sound intensity and rotational equivariance losses further enhance inter-channel phase consistency and structured behavior across SH subspaces. Evaluations on both first- and second-order Ambisonic encoding tasks, using simulated room-acoustics and real-world LOCATA recordings, demonstrate that DiffM2A outperforms conventional and neural baseline methods on signal fidelity, spectral accuracy, spatial coherence, and binaural cue preservation. Additional experiments show that these gains are largely retained across unseen five-microphone layouts and under mismatched open-array and rigid-sphere boundary models.

Semantic speech tokens should preserve linguistic content while suppressing utterance-specific acoustic and duration variation. However, existing speech-to-unit (S2U) tokenizers often retain speaker-related acoustic characteristics and duration information. To address this issue, we propose NormToken, an iterative semantic token purification framework that alternates S2U and text-to-unit (T2U) training. In each iteration, the T2U model produces text-derived tokens to supervise a newly initialized S2U tokenizer. The resulting S2U tokens are then used as targets for the next T2U iteration. This cycle drives the two models toward a shared, text-predictable token space. Experiments on Mandarin and English demonstrate improved S2U–T2U agreement and parallel-utterance token consistency. De-tokenizers trained on the initial and refined tokens further show that refined tokens maintain comparable WER and CER while improving speaker similarity in both voice cloning and text-to-speech synthesis. In voice cloning, refined tokens produce speaking rates closer to the acoustic reference, suggesting reduced dependence on source duration information. Audio samples are available at https://hanlin1004.github.io/normtoken_demopage.

Missing or degraded sequences can limit prostate multiparametric MRI. We developed MSCNet, a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions. Across ten completion tasks, task-specific MSCNet achieved mean structural similarity of 0.818 versus 0.798 for the strongest task-matched comparators; matched-capacity analyses showed larger differences in lesion fidelity and boundary preservation. In a blinded 1,000-case reader study, overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W. In a separate 200-case diagnostic assessment, AUCs for clinically significant cancer were 0.860 with acquired images, 0.841 with MSCNet and 0.797 with baseline-generated images. A locked 186-case three-hospital cohort supported multicentre transportability. These retrospective results support quality-controlled cross-modal reconstruction as an adjunct to acquired prostate MRI.

Managing dynamic User Association and Resource Allocation (UARA) in modern Heterogeneous Cellular Networks (HetNets) remains a critical open challenge. Existing mathematical optimization and Reinforcement Learning approaches face limitations in handling low-latency decision-making under dynamic traffic conditions. This paper introduces a novel orchestration scheme for game-theoretic UARA in HetNets. The proposed bilevel framework distributes UARA decisions to User Equipment through a multi-objective non-cooperative game. Overlaying the distributed game, a centralized Deep Reinforcement Learning controller orchestrates network performance by dynamically configuring the game's utility parameters, enabling transitions between power awareness, coverage enhancement, and balanced operation. Evaluated on urban HetNet topologies with 3GPP TR 38.901-compliant channel modeling, the proposed framework closely approximates the optimal policy for the considered operational objectives, while delivering higher network throughput than conventional association methods. Furthermore, it incurs low computational overhead and maintains stable performance across the evaluated traffic densities without retraining.

We propose a task-aware semantic split learning (SL) framework for wireless edge-cloud inference, in which the reliability of transmitted latent representations is dynamically adapted to their relevance for the downstream task. An autoencoder (AE)-based physical (PHY) layer enables end-to-end learning of the communication interface, while unequal error protection (UEP) is realized via mutual information (MI)-driven prioritization of latent components during training. The gradient of the estimated MI with respect to each latent component serves as a sensitivity-based proxy for task relevance, providing a fully learning-driven prioritization that adapts to both the data distribution and the downstream task. We further show that this prioritization translates into measurable physical-layer effects: MI-guided UEP assigns significantly higher transmit power to the most task-critical latent components compared to the equal error protection (EEP) baseline. Experiments on real-world IoT sensing data demonstrate consistent gains over equal and fixed-UEP baselines across SNR regimes. Additional analysis confirms ranking stability, estimator robustness and generalization across datasets and task types, indicating broad applicability of the proposed framework.

Circular manufacturing systems require functional evaluation of used components based on their physical state. Existing approaches describe this state from separate perspectives, such as design, manufacturing, and degradation, resulting in fragmented and incompatible representations. As a consequence, the physical state cannot be reliably linked to the functional behavior of the corresponding subsystem, which is a prerequisite for informed R-strategy decisions. This paper introduces the Unified Embodiment Description (UED), a state-dependent representation of mechanical components structured into two coupled layers. The first layer is a unified characteristic space, which adapts and extends as new lifecycle effects emerge. The second layer consists of functionally derived tolerance regions that link these embodiment characteristics to the functional behavior of the surrounding subsystem. A supporting UED method guides the model-building process of both layers. The UED is demonstrated in a case study on the spindle shaft of an angle grinder, in which manufacturing variations and degradation patterns such as polishing wear and scratches are quantified. These embodiment changes are embedded into the unified characteristic space and translated into functionally derived tolerance regions through experimental testing of the spindle-bearing subsystem. The results show that embodiment changes induced over the lifecycle can be consistently integrated within the unified characteristic space and that the relations between embodiment and functional behavior can be quantified to support end of life decisions. Overall, the UED provides a foundation for embodiment modeling that adapts to component state and enables decision-making based on functional evaluation for used components in circular manufacturing systems.

Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmark for instruction-aware speech retrieval, in which natural-language instructions dynamically specify relevance criteria, including semantic content, speaker identity, speaking style, environmental sounds, and their combinations. We evaluate four retrieval paradigms: large audio-language models, cascaded pipelines, self-supervised speech models, and contrastive audio-language models. Our results reveal that no current method robustly handles all retrieval intents. Text-based approaches perform relatively better at semantic retrieval but struggle with paralinguistic attributes, while speech-based models are moderately better at capturing acoustic properties but falter at following instructions. These findings highlight the need for unified architectures capable of instruction-aware speech retrieval.

Integrated sensing and communication (ISAC) and Digital Twin (DT) technology have emerged as complementary for future wireless networks that require autonomous operations involving continuous interaction between physical and digital worlds. However, existing DT-assisted ISAC frameworks sense continuously and indiscriminately while optimizing only a single task, leaving little room for persistent, multi-domain knowledge or proactive sensing control. This article proposes a Predictive, Reasoning-driven, Intelligent Sensing Module (PRISM) engine that transforms the DT from a passive, domain-specific optimizer into a persistent, network-wide reasoning system. PRISM enables decision-centric predictive perception, proactively directing sensing toward anticipated decisions needs rather than following fixed sensing schedules. Using an illustrative extremely large multiple-input multiple-output (XL-MIMO) deployment scenario with a mixed eMBB, URLLC, and mMTC device population, we show how this principle benefits visibility-region sensing for channel acquisition and supports slice-aware operation. Preliminary simulations, including this deployment scenario and the resulting knowledge error, overhead, and latency results, confirm that this decision-centric approach substantially reduces sensing overhead while preserving decision reliability and latency, supporting the proposed architecture as a practical step toward self-aware, autonomously orchestrated 6G networks.

Deep learning models for radio map prediction are trained and tested at the same carriers and cannot serve unseen frequencies. We propose a two-stream network learning a residual over an analytic prior, fusing environment and physics streams by cross-attention. Evaluated at the query frequency, this free-space and knife-edge prior absorbs dominant frequency scaling of pathloss, leaving a residual that varies little across bands. Across 150 ray-traced scenes and four training carriers (1.8–28 GHz), the method reduces RMSE by 35.3% zero-shot at unseen carriers and scenes, and by 37.6% at extrapolated 60 GHz, outperforming interpolated 10 GHz.

This paper presents metrics and measurement methods for the experimental characterization of helicopter rotor blade modulation of the wireless propagation channel. Broadband bistatic channel measurements were conducted at three widely separated carrier frequencies (302 MHz in the UHF band, 4.9 GHz in the C-band, and 27.1 GHz in the Ka-band) for three military helicopters (CH-53, Tiger, and UMAT) at the Bundeswehr Technical and Airworthiness Center for Aircraft in Manching, Germany. The time-variant channel was analyzed in terms of the Doppler Power Spectrum and the RMS Doppler spread, the Doppler spectrogram, and the Power Time Profile. The RMS Doppler spread values were found to be moderate (up to 106 Hz), which is attributable to the predominantly lateral blade motion relative to the line-of-sight path. The Power Time Profiles revealed periodic blockage attenuations of up to 15 dB at Ka-band, increasing significantly with the carrier frequency; at 302 MHz, the blockage attenuation does not exceed 2.3 dB and the RMS Doppler spread stays below 10 Hz. The measured blockage attenuation was modeled using the double knife-edge diffraction model, showing good agreement with the measurements. The distinct micro-Doppler signatures visible in the spectrograms are identified as a potential resource for helicopter classification and passive radar applications.

Neural video compression (NVC) has achieved strong compression performance, but practical random-access coding still faces two technical challenges: existing bidirectional NVCs (BVCs) usually require costly motion-first decoding, and reliable motion estimation is difficult under long-range bidirectional prediction. To address these issues, we present BiCRVC, an efficient bidirectional neural video compression framework based on coupled representation coding. Instead of coding motion and frame information with two separate codecs, BiCRVC transforms the motion representation and the current-frame latent into a unified latent representation for entropy coding. This design enables motion and frame information to be decoded from the same bitstream with one unified codec, while still reconstructing motion-aligned contexts for frame decoding. To improve motion accuracy, we introduce multi-candidate motion estimation (MCME), which combines multi-scale motion estimation and parallel accumulated motion estimation to better handle diverse and long-range motions. To reduce motion coding overhead, we further propose bidirectional motion feature propagation (BMFP), which reuses previously decoded motion features at both the encoder and decoder as temporal priors for conditional motion coding. In addition, coupled distortion training and random GOP structure training are used to encourage joint motion-frame coding and improve adaptation to hierarchical random-access structures. Experiments show that BiCRVC achieves better compression performance than state-of-the-art BVCs while providing about 30 times faster 1080p decoding than recent BVCs.

As Space-Based Information Networks (SBINs) evolve toward high-capacity, intelligence-centric paradigms, integrating sub-Terahertz (sub-THz) communication into Low Earth Orbit (LEO) satellite constellations has emerged as a critical enabler for ultra-broadband and resilient global connectivity. By exploiting the ultra-wide bandwidth of sub-THz links to reduce transmission delays, resource-constrained ground devices can seamlessly offload compute-intensive tasks to LEO edge servers. However, satellite motion, short visibility windows, and limited onboard resources make offloading decisions highly time-varying. Existing distributed offloading schemes typically require repeated inter-device state exchange and poorly adapt to time-varying LEO topology or traffic conditions. To address these limitations, a decentralized game-theoretic offloading framework empowered by large language models (LLMs) and counterfactual inference is proposed in this paper. First, a realistic offloading system is established by integrating time-varying 3D-Walker topology. Second, a game-theoretic scheme using counterfactual inference is introduced to deduce unobserved states from local histories, eliminating global information reliance. Finally, an LLM-empowered semantic fusion algorithm is integrated into the counterfactual inference to enhance adaptability through zero-shot reasoning and self-reflection. Numerical results show that L-COIN reduces offloading cost by 10.9% to 27.7% relative to state-of-the-art baselines.

This study proposes a relative orbit control law for laser debris removal missions considering the uncertainties of laser ablation and atmospheric drag. A removal spacecraft irradiates laser pulses to a target debris to generate the ablation force for deorbiting. The deorbiting force lowers the target altitude, and the removal spacecraft must follow it to maintain its relative position for continuous laser irradiation. The difficulty stems from uncertainties of the magnitude of laser ablation and external disturbances such as atmospheric drag. To tackle this problem, this study derives an adaptive control method using the Gaussian process regression to cancel the uncertainties with a nonparametric regression model. Numerical simulations verify the proposed control law under the uncertainties of laser ablation and atmospheric drag. The proposed control law can contribute to the realization of a safer and more secure mission not only for laser debris removal missions, but also for other on-orbit services.

High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation. However, existing deep learning methods predominantly treat this as a pure image completion task, resulting in over-smoothed reconstructions that fundamentally erase high-frequency scattering signatures of dynamic physical entities such as hidden vehicles. To overcome this, we propose RadioVIL, an efficient two-stage framework that reformulates joint radio map inpainting and zero-shot vehicle localization as a prior-guided physical inverse problem. Specifically, we first train a Denoising Diffusion Probabilistic Model (DDPM) to capture the structural generative prior of the environment. During inference from highly sparse measurements, we employ a Diffusion-based Mediating Intermediate Layer Optimization (DMILO) algorithm. By optimizing an L1-regularized sparse deviation term, DMILO mathematically isolates vehicle scattering anomalies layer-by-layer without unfolding the entire denoising chain. Extensive experiments demonstrate that while conventional reconstruction baselines fail to detect hidden vehicles, and the zero-shot diffusion baseline achieves only limited detection ability due to forced semantic harmonization, RadioVIL preserves authentic physical textures, yielding the best LPIPS of 0.0587 in our evaluation. Uniquely, it unlocks accurate zero-shot vehicle localization directly from sparse radio maps, securing a 75.20% Recall and a 3.31-meter average error, paving a robust way for ISAC at the 6G edge.