Papers

We address the problem of color attribute compression for 3D splats. We show that all images generated by 3D splats are linear in the coefficients for each color channel, each spherical harmonic, and each splat, and we identify a basis for the space of all such images. We identify an inner product for the coefficient space that induces the squared error loss on images. We show that orthonormalizing the coefficients with respect to this innner product before coding can yield over 2 dB gain.

Industrial PID loops are still tuned by hand: step the setpoint, look at the response, change a gain. This paper turns that procedure into an algorithm, SPIN, for Step-response Phase-portrait INspection. From a single closed-loop step response we form three phase portraits of the control deviation and count how many times each trajectory winds around its settling point. The counts are dimensionless and independent of the step size and of the clock. They show which channel is under-damped: integral, proportional, or derivative. A triangular rule reads the three counts, cutting the gain of the one band whose fault is certain and raising that of every band the same test shows to be within its limit. No model is identified and no optimization is solved. The iteration is deterministic and its gains stay inside a fixed box. Because a ringing count is itself the evidence that the loop is in the regime where cutting that band's gain reduces the count, the iteration settles into a cycle at the edge of the well-damped set. It needs no excitation beyond a routine setpoint step, so it can run on a process in operation, and it may start from arbitrary gains, including destabilizing ones. SPIN assumes a stable, self-regulating process whose gain and phase both fall with frequency, which covers the lag-plus-dead-time models common in process control, and a controller from the nested family $\mathrm{I} \subset \mathrm{PI} \subset \mathrm{PID}$ with integral action present. We validate it on a battery of process-typical plants with one fixed step size and one set of dimensionless constants throughout. Every decision is visible: the operator sees what the algorithm sees. A reference implementation and an interactive demonstration are public.

Commercial millimetre-wave (mmWave) Wi-Fi platforms based on IEEE 802.11ad/ay have demonstrated sensing capabilities, but they provide limited visibility into the packet-level measurements and synchronisation mechanisms that determine temporal coherence. We present a programmable 60~GHz IEEE 802.11ad testbed that exposes the complete complex 128-tap channel impulse response (CIR) for every detected packet. This access exposes packet-to-packet delay shifts and common phase drift that commercial-transceiver studies generally do not characterise. We develop a delay-profile alignment method and define a static-reference relative CIR phase that cancels common drift while preserving target-induced variation. We use 30~s respiration sensing to evaluate long-term phase stability for sub-wavelength motion tracking. Across 39 recordings, median absolute waveform correlation increases from 0.41 (timestamp regularised) to 0.52 (delay aligned) and 0.61 (relative-phase calibrated); breathing-rate mean absolute error decreases from 5.1 to 4.1 and 3.6~breaths/min. The results demonstrate controlled characterisation and stabilisation of communication-native WiGig CIR measurements on a fully programmable physical layer.

Low-earth orbits (LEO) satellites have recently gained wide attention for their potential to provide high-data-rate services in desert, sea, and rural areas. Currently such systems operate in the Ku band on the down-link. However, inter-operator LEO satellites produce frequent In-Line-Events (ILE) creating co-channel interference between their waveforms. This situation will be exacerbated with the launch of more constellations being launched. To address this challenge, we propose a novel interference mitigation system based on power control. Specifically, satellites from one operator will adjust its transmit power to create a sufficient received-power gap for co-frequency beams, enabling successive interference cancellation (SIC) or AI-assisted algorithms on the user-side for interference mitigation, where AI requires a smaller power gap. We use the waveforms from Starlink and EutelsatOneweb to evaluate the designed system. Results show the designed system can significantly mitigate interference and increase the overall spectrum efficiency.

Medium-term forecasting of Power Quality (PQ) parameters, on horizons of weeks to about one year, supports proactive maintenance and the early detection of limit exceedances in transmission network monitoring. Its practical value, however, depends on knowing in advance which time series can be forecast reliably at all. This article addresses that question in two stages, based on 2,807 weekly time series of PQ parameters from a long-term measurement campaign at 66 sites in the German transmission system, covering the 110 kV, 220 kV, and 380 kV levels. First, eight forecasting models are benchmarked. The STL-ARIMA hybrid achieves the highest accuracy, with an average sMAPE of 17.63% and a lower error than the seasonal naive benchmark for 72% of the time series, while accuracy varies substantially across PQ parameters and measurement sites. Second, model-free features computed from the training set alone are evaluated as measures of intrinsic predictability. SVD entropy and the Crest Factor correlate strongly with the realized forecast accuracy of all eight models and are used in a logistic regression to estimate the probability of a poor forecast before any model is applied. The resulting measures allow operators to separate time series suitable for automated forecasting from those requiring manual review.

A buoyless acoustic-to-LEO gateway with predictable contacts is analyzed. Advanceable cross-medium preparation competes with acoustic collection, while residual RF acquisition consumes contact time. Exact fluid and packet service, a fluid-optimal fixed preparation lead, tandem-queue stability, and one-contact reliability are derived. An explicit service-bias identity shows why treating acquisition as advanceable can select an insufficient lead. For lognormal preparation, conditions are characterized under which variability improves mean service while degrading reliability. Monte Carlo and packet-queue simulations corroborate the analysis with synthetic parameters; in a diagnostic case, accounting for residual RF acquisition raises the actual sustainable rate from 8.776 to 9.079 kbit/s.

Selective auditory attention decoding (sAAD) infers from electroencephalography (EEG) which speaker a listener attends to in multi-speaker scenarios. A widely used approach reconstructs the attended speech envelope from EEG, correlates it with candidate envelopes, and selects the speaker with the highest Pearson correlation. However, correlations in each decision window have high intrinsic variance, making per-window decisions unreliable, especially for short windows. Recent work introduced a hidden Markov model (HMM) that integrates correlation evidence over time and requires state-dependent emission distributions. Since labeled attention states are typically unavailable in practice, these distributions can be inferred from unlabeled observations by fitting a Gaussian mixture model (GMM). However, when the underlying Gaussians are close, unsupervised fitting becomes less reliable with limited samples. We therefore propose a multiscale GMM whose parameters are jointly estimated across multiple window lengths. The model exploits the window-length dependence of Fisher-transformed correlation estimates, whose means are approximately stable while their variances decrease with the number of samples per window. Experiments show that the multiscale GMM estimates emission parameters more accurately than single-scale fitting, particularly at short windows. Its HMM-post-processed accuracy advantage is largest for short recordings and narrows as more data become available, motivating multiscale fitting in data-constrained settings.

This paper investigates hybrid beamforming for multi-user large multiple-input multiple-output millimeter-wave unmanned aerial vehicle (UAV) downlink systems under mobility-induced channel aging and delayed beam-training feedback. Analog beam selection from compact delayed reports is a partial-observation decision, while additional candidate evaluations consume processing time and reduce the useful payload interval. We propose a mobility- and feedback-aware multi-level refinement strategy, termed MLR-TG, to improve robustness without always-on candidate search. Candidate subsets are ranked by a predicted net utility constructed from quantized complex coefficients of the reported codewords and the UAV mobility state, while the transmission regularized zero-forcing precoder is computed once from pilot-estimated effective channel state information (CSI) after analog selection. The refinement level is adaptively selected according to conflict severity and aging sensitivity. The selection rule is a two-statistic approximation of predicted-utility maximization, employs a system-size-invariant conflict score, and is calibrated offline on training data disjoint from evaluation. Simulations on a three-dimensional air-to-ground model with UAV attitude dynamics and common channel trajectories show that MLR-TG reduces system outage probability by 26.7% and improves the 5th-percentile user rate by 53.9% relative to greedy sector beamforming, while net spectral efficiency remains within 0.96%. Compared with always-on global top-3 refinement, MLR-TG improves net spectral efficiency by 5.5% while evaluating 77.9% fewer candidates, and remains within 3.4% of a noncausal-CSI level oracle in net spectral efficiency while requiring 86.9% fewer feedback bits than full-CSI reporting.

Steel wire ropes (SWRs) are critical load-bearing components whose local flaws (LFs) pose serious safety risks. Magnetic flux leakage (MFL) inspection commonly detects LFs from temporal or axial morphology, which can change with the sensing axis and operating condition. We show instead that LF responses remain localized over neighboring channels of a circular array. This observation motivates a channel-feature-oriented (CFO) detector that removes smooth channel backgrounds, applies circular matched filtering, and fuses spatially co-located tri-axis responses. Experiments performed on real-world equipment show that CFO attains the highest localization performance among three representative baselines, reaching F1@0.5/F1@0.7 scores of 73.9%/59.5%. It attains the best temporal localization performance under all four conditions and achieves at least 19.4 times the throughput of the evaluated signal-processing baselines. These results demonstrate that circular channel locality provides a robust LF representation across the evaluated operating conditions.

An orthogonal affine-precoded superimposed training sequences (AP-STS)-based architecture is developed for the cyclic prefix (CP)-aided multiple input multiple output (MIMO) direct-current-biased orthogonal time frequency space (DCO-OTFS) visible light communication (VLC) systems relying on arbitrary transmitter-receiver pulse shaping. The data and pilot symbol matrices are affine-precoded (AP) and superimposed in the delay-Doppler (DD)-domain for each transmit light-emitting diode (LED), followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver for each receiver photodiode (PD), the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided (PA) variational Bayesian inference (PA-VBI) technique is conceived for the channel state information (CSI) estimation of MIMO DCO-OTFS VLC systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided (DA) variational Bayesian inference (DA-VBI)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. Moreover, the Bayesian Cramer-Rao lower bounds (BCRLBs) are also derived for MIMO DCO-OTFS VLC systems. Finally, simulation results demonstrate that the proposed method yields superior performance in terms of normalized mean-square-error (NMSE), pilot overhead, and symbol error-rate (SER).

Laser diode (LD)-based luminaires are gaining increasing attention in automotive applications and are expected to extend to residential and commercial environments, creating opportunities for high-bandwidth visible light communication (VLC) systems. However, practical LD-based VLC links are impaired by input-signal-dependent shot noise (ISDSN), relative intensity noise (RIN), and thermal noise, which affect reliable channel estimation (CE). This work investigates their joint impact on receiver-side CE in a single-input single-output (SISO) optical orthogonal frequency division multiplexing (OOFDM) VLC system under a statistically random channel model. A statistical inference framework is developed in which the receiver exploits observed signal variations to estimate the channel under optical impairments. Closed-form expressions are derived for least squares (LS), maximum likelihood (ML), maximum a posteriori probability (MAP), minimum mean square error (MMSE), and linear MMSE (LMMSE) estimators. In addition, the Bayesian Cramer-Rao lower bound (BCRLB) is derived to benchmark mean square error (MSE) performance. Monte Carlo simulations for direct current-biased O-OFDM (DCO-OFDM) and asymmetrically clipped O-OFDM (ACO-OFDM) validate the analysis. Results show substantial CE degradation under the joint presence of ISDSN and RIN, while the MMSE estimator consistently achieves the lowest MSE, demonstrating strong potential for robust and adaptive receiver operation in practical VLC systems.

A multipath CIR estimator is proposed, followed by sparse frequency-domain (FD) signal detection, which harnesses the simultaneous-sparsity innate in the multipath CIR and FD signals across measurement vectors, conceived for optical OFDM (O-OFDM) utilized for visible light communication (VLC) systems having reduced peak-to-average power ratio. At the outset, we derive the input-output relationships for the asymmetrically clipped O-OFDM (ACO-OFDM) as well as for the direct current-biased O-OFDM (DCO-OFDM) systems. Next, traditional benchmarking methods are introduced for channel estimation (CE), including both the LMMSE and LS methods, followed by an orthogonal matching pursuit (OMP)-based technique capable of exploiting sparsity in the multipath CIR of the VLC system. Subsequently, an CE technique based on a novel group sparse OMP (GOMP) concept is proposed, which capitalizes on the group-sparsity in the delay domain of the CIR across measurement vectors, attributed to the multipath characteristics inherent in the NLoS components of the VLC channel model. Additionally, an advanced group-sparse CE technique is put forth based on the group-sparse Bayesian learning (GBL) approach, which considerably reduces the pilot overhead. A low complexity version of GBL, termed LCGBL, is also developed that reduces the computational cost of GBL significantly. Consequently, the GOMP and GBL frameworks are also extended to the data detection of the sparse FD O-OFDM symbols, which utilizes the group-sparsity of the FD O-OFDM symbols across measurement vectors. The Bayesian Cramer-Rao lower bound (BCRLB) is computed to assess the estimation performance of the proposed CE techniques. Our simulations demonstrate that, despite its reduced pilot overhead, the proposed GBL technique outperforms the other CE schemes in terms of its BER, outage probability, and NMSE, quantitatively reinforcing its superiority.

Vital Signs Sensing, enabled by the evolution of wireless communications, is inherently challenging as the chest's micro-Doppler signatures can be corrupted by multipath, body movement and noise. Additionally, the prospects of the Frequency Range (FR) 3 band, spanning 7-24 GHz, are compelling due to centimeter-scale wavelength improving phase sensitivity to chest motion, while its abundant bandwidth and rich multipath enhance temporal and spatial diversity. In this paper, we validate the feasibility of using cellular communications in the FR3 band to monitor a human's breathing and heartbeat in an indoor environment. Using Blender to model physiologically accurate vital-signs signals as ground truth and Sionna Ray Tracing simulations, we find that it is feasible to extract breathing and heartbeat waveforms at FR3 frequencies. Furthermore, we perform a comparative analysis of ISAC performance against representative sub-6 GHz (FR1) and mmWave (FR2) frequencies, indicating the utility of the study across the frequency bands.

Advanced artificial intelligence models think in tokens; contemporary communication systems carry bits. The direct way to bridge this gap is to transmit tokens, but that makes a model-specific representation part of the air interface, coupling the endpoints through a shared tokenizer, codebook, and often a neural transceiver. We take a different route: keep bits in the payload and let tokens control how those bits are generated and protected. The resulting token-bit interface transition aligns task-side tokens with source- and channel-coding units, translates token relevance into codec controls, and preserves the induced priority order across the coding chain. We instantiate it for image classification, where a vision transformer scores the task relevance of each image region from its attention maps: those scores steer block-wise JPEG rate allocation, then group the compressed bits for protection at different polar-code rates. The payload remains an explicit, reconstructable bitstream recovered by a correspondingly configured decoder. Over-the-air experiments on a software-defined radio testbed show improved accuracy–latency tradeoffs over separate source-channel coding, performance competitive with far more memory-intensive neural joint source-channel coding, and graceful degradation under channel mismatch. Token communication, then, need not transmit tokens explicitly; what it needs is an interface through which tokens determine how bits are communicated.

Practical reconfigurable intelligent surfaces (RISs) can exhibit deterministic parasitic scattering that is not captured by idealized element-wise models. As a result, such models may overestimate the gain of the intended RIS-assisted path and bias multipath prediction. This paper develops a full-wave-calibrated element-wise model using three Bragg-order basis functions to represent the intended and dominant parasitic scattering components. Environmental multipath is incorporated by identifying RIS–Rx reflection sequences with ray tracing (RT) and unfolding them by image theory into path-dependent image points. This allows direct and reflected RIS-assisted paths to be evaluated using the same per-element kernel and coherently combined with Tx–Rx bypass paths. To reduce the full-wave calibration burden for large RISs under a prescribed focusing configuration, only the three Bragg-order coefficients are transferred from a 25-by-25 calibration aperture, while the target-aperture basis functions and geometry are recomputed. At 154 GHz, the transferred coefficients keep the intended-order errors within 0.8 dB for the 50-by-50 and 100-by-100 RISs, while reducing the full-wave calibration time for the prescribed configuration from 7.56 to 1.08 h relative to direct 100-by-100 calibration. For multipath validation with the 50-by-50 RIS, the calibrated model using the same transferred coefficients keeps the nominal-Rx gain error within 0.88 dB across four PEC reflector configurations, compared with 1.75–5.51 dB for the uncalibrated general model, and reduces the error from 6.49 to 0.23 dB in the scaled indoor environment.

Integrated sensing and communication (ISAC) base stations (BSs) are emerging as critical infrastructure for 6G networks, requiring advanced over-the-air (OTA) testing methodologies that can emulate dynamic, multi-target scenarios under far field (FF) conditions. However, existing testing solutions often suffer from limited angular flexibility, insufficient bandwidth, and prohibitive costs. {To address these challenges, this paper proposes an OTA testing system that integrates a radar target simulator (RTS), a multi-port amplitude and phase modulation (APM) network, and a multi-feed linear array with a single parabolic cylindrical compact range (SPCCR).} The proposed system is capable of emulating closely spaced spatial targets with varying incident azimuth angles. This setup meets the essential conditions for ISAC BS testing, including FF characteristics, wideband and multi-band compatibility, and the capability for sensing multiple dynamic closed-spaced targets. The feasibility of the proposed method is validated through both FEKO simulation and measurement. Experimental results demonstrate that the system successfully emulates multiple closely spaced targets within an azimuth range of $\pm 10^\circ$ with high emulation accuracy while maintaining excellent quiet zone (QZ) performance.

Deep learning has led to numerous proposed architectures for Automated Emotion Recognition (AER) from electrocardiogram (ECG) data, but inconsistencies in preprocessing, training, and evaluation make direct comparisons difficult. Most studies train and validate models on individual datasets collected under homogeneous conditions, limiting variability and raising concerns about generalizability. Cross-dataset validation is sometimes used but primarily assesses model adaptability rather than true generalization. This study presents a comparative analysis of prominent deep learning architectures in AER, emphasizing model generalization over dataset adaptability. To enable this benchmark, we introduce two open-source frameworks: Affective Research on Representations and Classifications (ARRC), a standardized benchmarking toolkit, and Affective Research Dataset Toolkit (ARDT), a framework for inter-dataset training and validation. Using ARDT, we consolidate three publicly available AER datasets, CUADS, ASCERTAIN, and DREAMER, into a single dataset, increasing variability in sensor types, recording conditions, and participant demographics. We then use ARRC to evaluate three widely studied deep learning models and two CNN baselines through hyperparameter optimization and 10-fold cross-validation. Our findings provide insights into the trade-offs between classification accuracy and model complexity, establishing a reproducible benchmark for AER research. All source code for ARRC, ARDT, and model evaluation is publicly available to ensure transparency and facilitate further research.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted movable antenna (MA) secure integrated sensing and communication (ISAC) system. In this architecture, the RIS establishes indirect transmission links to provide communication services for multiple legitimate users, while the high spatial diversity gain of MA is leveraged to enhance system security. Then, we formulate an optimization problem to maximize the system total secrecy rate by jointly optimizing the MA position selection, active beamforming design for base station and passive beamforming design for RIS. The problem also accounts for practical constraints including transmit power budget, sensing beampattern mean square error (MSE), RIS unit-modulus constraint. However, it is challenging to solve this problem due to its non-convexity and strong coupling of the optimization variables. Consequently, we propose an alternating optimization (AO) framework, employing techniques including discrete binary particle swarm optimization (BPSO), successive convex approximation (SCA) and difference-of-convex (DC) programming to transform the optimization problem into convex subproblems. Based on the solution above, the convex sub-problems are solved iteratively until convergence is achieved. Numerical results demonstrate that the proposed algorithm outperforms other baseline algorithms in terms of secure communication performance.

This paper considers an unmanned aerial vehicle (UAV)-assisted 3D integrated sensing and communication (ISAC) system, where a UAV is deployed to communicate with a base station (BS) and simultaneously monitor a volume of interest (VoI). By exploiting the flexible placement of the UAV anchor node, we aim to characterize the Pareto optimal tradeoff between the communication rate and sensing CRLB. For the special case of a singleton VoI, the exact Pareto-optimal UAV anchor placement set is shown to be the line segment connecting the BS and the sensing target. For a radially truncated spherical sector VoI, we derive geometric conditions under which all Pareto optimal UAV locations are confined to an axial segment inside the inner radial boundary. For general VoIs, we prove the convexity of the regional worst-case CRLB in the inner region, derive a Pareto-optimal radius upper bound, and develop an efficient algorithm to find the Pareto-optimal UAV anchor node placement. Numerical results validate the analytical placement structures and demonstrate that our proposed design achieves significantly enlarged rate-CRLB region over benchmark schemes.

While low Earth orbit (LEO) satellites are a key enabler for 6G coverage extension, high-altitude platform stations (HAPS) offer complementary advantages in latency, footprint persistence, and deployment flexibility. We extend the BLASTER traffic-aware resource management framework [1] to jointly optimize user association, bandwidth splitting, and power control across a single LEO platform (600 km) and HAPS deployments of 1, 4, and 9 platforms (20 km) with intra-tier frequency reuse, evaluated over a full diurnal traffic profile. Increasing the HAPS count consistently improves coverage, SINR, sum-log throughput, and terrestrial energy savings; a 9-HAPS deployment matches LEO-level QoS while requiring substantially fewer active terrestrial base stations. These results position multi-HAPS architectures as a compelling, energy-efficient alternative to LEO for 6G non-terrestrial networks (NTNs).

Operational World Models (OWMs) require structured interaction with the physical world: they must observe operational state, impose controlled actions, measure consequences, preserve experience, and use that experience to support prediction and preventive decision making. This paper presents Autolab, an Internet-accessible experimental platform that provides these physical grounding functions for wireless-network OWMs. A remote researcher can inspect a live test site, reconstruct recent state history, author a time-ordered experiment containing robot, access-point, wireless-scan, and traffic operations, validate it locally, and submit the same explicit contract for registration. At the physical site, a centralized network management system (NMS) revalidates and schedules the experiment, coordinates distributed device agents, estimates robot location from raw camera observations, supervises physical execution, and records measurements and diagnostic state. The same Python validation rules are used at the authoring and registration boundaries, while destination-level commands isolate Internet users from low-level device control. An end-to-end mobility trace demonstrates how the NMS detects a large physical movement error, issues corrective motion, and restores the intended experiment trajectory without exposing that error to the script writer or robot agent. Autolab therefore contributes a reproducible Internet-to-physical experimentation and feedback infrastructure, rather than claiming a completed autonomous OWM. Its structured spatial–temporal records provide the experience needed for continuing work on generative scenarios, predictive risk evaluation, and preventive wireless-network management.

This work presents POLARIS, a training-free audio fingerprinting system that selects landmarks from a locally normalized saliency field and groups them into sparse fingerprints using Delaunay triangulation. To deal with query distortion, POLARIS adds fingerprints from two-hop Delaunay neighborhoods only at query time, without enlarging the reference index. An adaptive configuration applies this expansion only when the original fingerprints do not produce a confident match. We evaluate POLARIS on synthetic distortions from the public PEX Hard Medium benchmark, excluding queries with pitch or tempo shifts, and on a new benchmark of real re-recorded music. POLARIS achieves the best performance among the evaluated training-free methods on both benchmarks. On the real recordings, its adaptive configuration also outperforms the neural NMFP baseline with a comparable measured query time and a smaller logical reference payload. Code, dataset, and instructions for reproducing all experiments are available at https://github.com/JihengLi/POLARIS.git.

Many semantic localization tasks require determining a contextually meaningful region or state rather than minimizing the error of a full Cartesian position estimate. In such settings, aggregate localization accuracy need not align with the accuracy of the semantic decision. This paper develops a Cramér–Rao bound (CRB)-based framework that uses the local geometry of a known semantic map to guide uplink receive beamforming. Using local boundary crossing as a surrogate for semantic misclassification, we derive normal information maximization (NIM), which minimizes the CRB of the position component along the local semantic boundary normal. Under the considered single-path line-of-sight model, an optimal receive codebook can be restricted to the subspace spanned by the matched steering vector and its angular derivative, reducing the design to an allocation of measurement resources between matched and derivative spatial modes. We derive the resulting allocation for a general smooth boundary, with geofencing and intrusion detection arising as radial and tangential limiting cases. The framework is further extended to multi-region semantic maps through a distance-normalized minimax criterion and to uncertain prior locations through worst-case and prior-weighted robust formulations. Numerical results with finite-slot implementations and observation-level Monte Carlo simulations using profile maximum-likelihood (ML) estimation show that NIM allocates measurements according to the task-relevant boundary geometry and can substantially reduce semantic error relative to the classic squared position error bound (SPEB) design in the considered scenarios. The empirical results also closely follow the local CRB-based boundary-crossing approximation in the studied operating regime.

We propose a novel pulse radar framework for integrated sensing and communication (ISAC) systems supporting full-duplex (FD) ultra-reliable low-latency communication (URLLC). Motivated by the stringent delay and reliability requirements of sixth-generation (6G) URLLC services, our framework integrates pulse-based radar sensing with sporadic packet transmissions for URLLC users while accounting for residual self-interference (SI) inherent in FD systems. We adopt a time-structured approach in which the silent intervals of the radar pulse are opportunistically exploited for communication. A key focus is minimizing the total energy consumption of the base station, which is critical for sustainable radar-assisted wireless networks. To this end, we jointly optimize the sensing pulse width, beamforming vectors, and URLLC scheduling while ensuring communication and sensing quality-of-service (QoS) constraints. We formulate a non-convex mixed-integer optimization problem addressing sensing and communication requirements jointly. Sensing performance, quantified via the Cramer-Rao bound (CRB), is reformulated using the Schur complement. Finite blocklength capacity approximations capture the URLLC constraint, while probabilistic constraints are modeled using cumulative distribution functions. The original non-convex problem is decomposed into two sub-problems via an alternating optimization algorithm. Simulation results reveal important trade-offs between sensing time, energy consumption, and residual SI. Our proposed method improves system performance while explicitly accounting for SI, and demonstrates improved URLLC reliability with reduced energy consumption. The results show that proper design of the sensing duration and FD scheduling yields a flexible ISAC framework capable of robust operation even under strong interference.

Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) introduce Doppler and synchronization impairments that may require NTN-specific architectures. We test whether High-Efficiency Learning-based channel Estimation using dual Neural Attention (HELENA), originally designed for terrestrial channels, remains effective after NTN retraining and suitable across high-performance and power-constrained inference platforms. Its unchanged architecture is trained on paired receiver-compensated (NTN-1) and residual-impaired (NTN-2) datasets and compared with eight terrestrial-origin models trained on the same NTN data and the NTN-specific MDELAN-SISO. HELENA achieves the lowest observed SNR-averaged NMSE among the DL estimators in both conditions, including 55.8-62.7% lower linear-scale NMSE than MDELAN-SISO. All DL models degrade in NTN-2, demonstrating the challenge posed by residual Doppler and its associated impairments. On an RTX PRO 4500, HELENA achieves 0.0595 ms 99th-percentile (P99) inference latency, 88.1% below the 0.5 ms budget, with lower energy than its closest attention-based competitors. On a 10 W Jetson Orin NX, it retains a favorable accuracy-energy trade-off, but no model meets the P99 budget. Thus, HELENA needs no NTN-specific redesign for the evaluated task, while embedded tail latency remains an open challenge.

Integrated terrestrial–non-terrestrial networks (TN–NTNs) are a key enabler of sixth-generation (6G) connectivity across terrestrial, geostationary Earth orbit (GEO), medium Earth orbit (MEO), and low Earth orbit (LEO) domains. However, existing NTN frameworks do not jointly address service-aware serving-domain selection, multi-orbit coordination, and hierarchical multi-stream transmission. This paper proposes a service-aware multi-orbit rate-splitting multiple access (SA-MO-RSMA) framework that integrates global-common, orbit-common, and user-private streams with service-intent-aware serving-domain selection and resource orchestration. The framework operates as a system-level orchestration layer over existing 3GPP NTN architectures. A multi-criteria Orbit–Service Utility Function (OSUF) is introduced, together with metrics for service continuity, switching stability, power-normalized transmission efficiency, and synchronization robustness. Numerical results show that SA-MO-RSMA improves average per-user service utility, service continuity, switching stability, and transmission efficiency over the considered benchmark schemes, demonstrating its potential for coordinated service-aware multi-orbit TN–NTN operation.

Semantic image communication seeks to preserve task-relevant scene structure under limited channel resources, but carriers are often dense latent tensors or grid-aligned semantic layouts that do not explicitly encode region-level relations. This work introduces a segmentation-derived region adjacency graph (RAG) carrier, termed channel-adaptive RAG (CA-RAG), for joint source-channel coding-style image communication. Nodes store interpretable region attributes, edges preserve adjacency, channel-adaptive graph simplification (CGS) controls the node budget, and semantic belief propagation refines noisy graph embeddings before diffusion-based reconstruction. On Cityscapes, pre-channel RAG payloads are several times smaller than compressed class-index layouts in a 2,000-image study. Under additive white Gaussian noise at signal-to-noise ratios from 0 to 15 dB, CA-RAG reports higher semantic consistency than deep joint source-channel coding and a same-decoder layout baseline, with comparable perceptual quality. At 10 dB, the full-budget rate-sweep point reaches mean intersection over union (mIoU) = 0.329 at approximately 3.3 x 10^3 channel uses, while the default adaptive-CGS setting reports mIoU = 0.294 at approximately 2.6 x 10^3 channel uses.

The Fisher information matrix (FIM) determines the accuracy attainable in an estimation problem. For a parametric process with memory, it has a closed form only for narrow classes of models, and it cannot be computed when the model is available only as a simulator. This paper estimates the Fisher information rate of such a process, the per-observation limit of its FIM, from simulator output alone and without knowledge of the memory length. The construction uses the identity between the FIM and the local curvature of the Kullback–Leibler (KL) divergence. Directional divergence rates are estimated by context-tree weighting (CTW), and the matrix is recovered from them by least squares. We establish strong consistency in an iterated limit and a finite-sample bound, uniform over every working depth at least as large as the true memory length, which gives a mean error of order $n^{-1/3}$. The analysis also delivers a finite-sample rate for CTW-based estimation of the KL divergence rate. Experiments confirm the predicted exponents, show that the accuracy does not degrade as the working depth grows, and use the estimate to plan a sampling budget that attains a prescribed precision. The estimator stays accurate on a hidden-state source that has no finite memory length.

The physical model of multi-mode pinching-antenna systems (PASS) is proposed based on the coupled-mode theory. Within the considered model, multiple guided modes are simultaneously excited in a single waveguide and exploited as independent signal-bearing channels, thus providing additional modal degrees of freedom for signal transmission. Under the local electromagnetic perturbations introduced by PAs, the undesired guided-modes coupling is explicitly investigated, and the resulting inter-mode interference (IMI) issue is revealed. The sufficient hardware-design condition for achieving the IMI-free regime is further established, for which the practical feasibility is validated through full-wave electromagnetic simulations. Then, a tractable signal model is derived for multi-mode PASS operating over the IMI-free regime. To demonstrate the benefits of the proposed multi-mode PASS model, the integrated sensing and communications (ISAC) is studied as a representative application scenario, where the base station simultaneously communicates with a communication user and senses a target by using two guided modes. A joint baseband and pinching beamforming optimization problem is formulated for the minimization of the Cramer-Rao bound for target localization, subject to the minimum communication rate requirement, under both continuous and discrete PAs activation cases. An alternating optimization-based algorithm is developed to address the formulated non-convex problem. For the baseband beamforming, a penalty-based successive convex approximation method is invoked. For the pinching beamforming, a particle swarm optimization algorithm and a two-sided matching algorithm are proposed for the continuous and discrete PAs activation cases, respectively. Numerical results obtained in the ISAC application scenario demonstrate the superiority of the proposed multi-mode PASS model over the single-mode PASS.

3GPP has progressively expanded AI/ML lifecycle management in the radio access network, from one-sided model control to Release 20 support for two-sided CSI-feedback model pairing. Yet a basic control question remains: when monitoring detects degradation, how quickly must a corrective action take effect? To expose this dependency, we stress-test three activation and rollback strategies in a surrogate regime-shift environment using 150 independently trained PPO candidate policies, each evaluated over 20 matched noise realizations. We add 0-40 control-step delay only to corrective lifecycle commands. With no added delay, stability-gated blending reduces mean post-shift cumulative SLA deficit from 47.02 to 7.57 violation-steps relative to hard cutover; at 40 steps, the deficit rises to 44.38, only 5.6% below the hard-cutover baseline. KPI-threshold rollback loses its advantage within only a few control intervals, while blending degrades more gradually. These results do not set a physical 6G latency bound. They show why timing requirements matter for corrective actions: lifecycle performance depends on when the command takes effect. Motivated by Layer-1/Layer-2 Triggered Mobility, we examine a standards split in which Layer 3 retains lifecycle configuration while a compact Layer 2 trigger is considered only for the latency-critical subset, together with pair-consistency, local-fallback, and security/freshness requirements.