Papers

In special relativity, a particle can experience constant acceleration, but at the same time, its motion is constrained by the velocity limit imposed by the speed of light $c$. Inspired by this principle, we propose a method for enforcing velocity bounds in control systems by replacing $c$ with the maximum attainable speed of the system. We refer to this as the “Minkowski wrap," the operation of deforming the phase portrait of a system so as to enforce desired speed limits. We apply this idea to shape the input generated by a state-feedback stabilizing controller and time-optimal controller considering controlling a double integrator system. By applying Pontryagin's Maximum Principle, we show that the time-optimal control of a wrapped double-integrator system is bang-bang. The proposed method transforms the classical double-integrator dynamics into a “wrapped" system that respects velocity bounds without the need for clipping, offering an explicit nonlinear feedback control strategy conducive to safety applications.

Tendon-driven continuum manipulators are widely used in medical applications, where accurate tip-position estimation is essential for precise navigation and instrument positioning. However, patient anatomy and procedural setup impose task-dependent unknown shaft configurations, while friction, slack, and compliance introduce hysteresis, making tip estimation challenging. This paper presents a motor-history-conditioned gated recurrent unit (GRU) residual estimator for three-dimensional catheter tip estimation without direct shaft-configuration sensing. First, an initial multidirectional sweep strategy is applied to calibrate a geometric catheter model backbone, and encode the motor-angle and drive-torque response into a shaft-configuration context vector. During subsequent motion, the context conditions a GRU that predicts a task-space residual correcting this backbone, relying on motor measurements alone. The context remains fixed for the current shaft configuration, while the recurrent state captures the evolving actuation history. Across four disposable intra-cardiac echocardiography catheters and 16 bent shaft configurations, the method achieves 3.3mm open-loop tip RMSE, a 59% reduction relative to the constant-curvature baseline.

This work proposes a novel polar code design scheme using puncturing and extending aiming to improve the successive cancellation list decoding (SCLD) performance. The proposed code design is conducted with Reed-Muller (RM) codes called base codes. For a base code, puncturing is first performed to gain a degree of freedom, called transmission holes. To this end, we develop a puncturing scheme minimizing the loss in the minimum distance of the base code. Then, we propose a novel extending scheme to produce additional bits which are transmitted through the transmission holes without rate loss. The extending scheme repeats some coded bits and/or intermediate bits induced during the encoding of base code. The repeated bits are decided in such a way to maximize the SCLD performance by utilizing reinforcement learning (RL). Moreover, we introduce a novel idea to reduce the learning space of RL, which greatly expedites the design of extended bits. While the proposed design scheme assumes RM codes as base codes, we show that it can be applied to various types of polar code. Simulation results show that the polar codes designed with the proposed scheme have notably improved SCLD performance compared to existing polar code designs.

The manipulation of Deformable Linear Objects (DLOs) such as cables poses a significant challenge for automation due to their infinite degrees of freedom and non-linear dynamics. In this paper we present a machine learning based optimal control approach for the manipulation of DLOs. This approach is divided into two main components: modeling and control. For modeling the dynamics of the DLO, we propose a learning based approach using a bidirectional Long Short-Term Memory (biLSTM) network. The biLSTM network is trained on synthetic data generated by the MuJoCo physics engine. For manipulating the DLO, a model predictive control strategy that employs Model Predictive Path Integral (MPPI) control is selected. The proposed approach is evaluated through simulation and experiments. The results demonstrate the effectiveness of the proposed method in achieving accurate and efficient manipulation of DLOs.

Distributed state estimation algorithms have been extended to switching networks, which model unreliable communication networks. Yet existing results apply only to specific classes of systems, such as neutrally stable systems. In this paper, we analyze a distributed observer under the assumption that the system matrix commutes with the coupling matrices. This not only enables a refined analysis that leads to a necessary and sufficient condition for stability over switching networks, but also covers a broader class of systems than existing results, including unstable systems such as vehicular platoons.

Multi-agent reinforcement learning (MARL) commonly trains decentralized policies from scratch, requiring agents to acquire individual task competence and coordination simultaneously. Yet many multi-agent problems admit a compatible single-agent counterpart in which the underlying task can be learned in isolation. We introduce Multi-Agent Observation Transformation for Existing Single-Agent Policies (MATES), an input-side adaptation framework for tasks whose multi-agent observations preserve the solo-task information while exposing separately identifiable neighbor information. From multi-agent experience, MATES learns a small adapter that maps this observation into the format expected by a frozen single-agent policy, inducing actions suited to the shared environment without updating the single-agent policy itself. MATES leaves the pretrained policy's internal architecture unchanged and retains the objectives and update procedures of the underlying MARL algorithm. We evaluate MATES using both on- and off-policy algorithms on lifelong pathfinding, navigation, and cooperative discovery, spanning discrete and continuous observation and action spaces. Across all evaluated settings, MATES optimizes only 3.5-7.3% as many parameters as full-policy training while consistently outperforming MARL training from scratch. It approaches the performance of full fine-tuning, remains competitive overall with demonstration-based baselines, and retains strong task performance at team sizes not encountered during training. These results provide evidence that, under this observation structure, effective multi-agent behavior can be learned without modifying the policy that encodes individual competence.

Speech enhancement (SE) models typically rely on supervised learning with paired data examples where clean speech is synthetically degraded. This paradigm limits performance in real-world scenarios where the target environment's specific acoustic characteristics are unknown. We propose a fully unpaired SE framework that uses principled Diffusion Schrödinger Bridges (DSB) to learn a stochastic transport process between a clean and a degraded speech distribution. Algorithms for learning transport maps are computationally heavy since they require simulating differential equations during training, usually at each training step. Therefore, we propose using a high-efficiency Mamba Diffusion Model designed for end-to-end waveform processing. We compare against state-of-the-art methods for speech enhancement, both paired and unpaired, as well as a classical signal processing algorithm. Experimental results show that we are on par or better than the baselines while being orders of magnitude faster during inference. Furthermore, we show that the flexibility of the DSB formulation allows our model to generalize across SE tasks, offering a robust and efficient solution for real-world speech restoration.

Personalized head-related transfer functions (HRTFs) are essential for spatial audio rendering, but densely measuring an individual's HRTFs is costly and time-consuming. HRTF upsampling reduces this burden by estimating dense HRTFs from sparse measurements. Recent learning-based methods have achieved promising performance, but many remain tied to predefined measurement configurations. In this work, we propose GeoAtt, a variable-context HRTF upsampling framework that uses a single trained model across varying measurement configurations. GeoAtt performs geometry-aware, query-conditioned spatial aggregation over the available measurements independently at each frequency bin, followed by frequency-domain modeling using Conformer blocks. The relative geometry between the target and measured directions is incorporated as an additive bias in the cross-attention. Experiments on the SONICOM dataset show that a single trained model achieves the lowest log-spectral distortion across all four canonical Listener Acoustic Personalization (LAP) challenge measurement configurations and further generalizes to configurations that are not explicitly included during training.

Joint information and energy transfer over inter satellite free space optical links is commonly based on explicit resource splitting, which couples communication reliability and energy delivery. This paper proposes a constant total power Stokes orthogonal architecture that conveys information through polarization while preserving the received optical power for photovoltaic conversion. Complementary polarization branches support balanced information detection through their AC components, while their DC components drive matched multi junction photonic power converters. The developed framework jointly characterizes pointing impaired propagation, bit error rate, outage, and nonlinear harvested power. The analysis establishes that the two information symbols only exchange the branch powers, leaving the unordered input power pair unchanged. Hence, the total harvested energy remains symbol invariant even under nonlinear conversion, without an explicit information energy power split or linear efficiency approximation. Numerical results confirm the analytical model, with the closed form BER approximation remaining within 0.43 percent of the exact evaluation over the examined pointing conditions. Compared with ideal lossless power splitting, the proposed architecture avoids the conventional BER versus harvested power tradeoff and provides an analytical baseline for power neutral joint information and energy transfer in inter satellite optical links.

This article introduces the LUneburg-lens-aided recoNfigurable Array (LUNA), an antenna architecture that unifies multiple-input multiple-output (MIMO) and network-controlled repeater (NCR) functionalities in the Luneburg lens-enabled hardware platform. A Luneburg lens, fabricated from graded-index dielectric materials, passively converts the radiation of a low-gain feed into a highly directional beam without active phase shifting, while a dense passive feed bank and a reconfigurable feed-selection network electronically switch the beam directions with minimal hardware complexity and power consumption. We commence by reviewing the basic principles and application history of Luneburg lenses in radar and wireless communications, which motivates their role in 6G-and-advanced networks. Then, we highlight how a Luneburg lens and a reconfigurable feed array construct both LUNA-MIMO and LUNA-NCR, where the lens and feed bank can be reused across functions and frequency bands. Case studies demonstrate that LUNA achieves the satisfactory spectral and energy efficiency with a few radio-frequency chains, and it also improves positioning performance for sensing tasks. Finally, some open problems and research directions are provided to inspire follow-up research on LUNA.

Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect a model's true decision process and may therefore mislead clinicians. We investigate this problem using a Vision Transformer-based breast MRI classifier trained on the ODELIA Breast MRI Challenge dataset and evaluate multiple saliency methods, including Last-layer Attention, Attention Rollout, Grad-SAM, Gradient Attention Rollout, GMAR, Grad-CAM, and HiResCAM. Our study highlights two often-overlooked challenges in perturbation-based faithfulness evaluation. First, method rankings depend strongly on the perturbation strategy, varying across intensity-based perturbations and transformer-based attention masking. Second, benchmarking saliency methods requires distinguishing between class-specific and class-agnostic explanations. To enable fair comparisons, we introduce non-class-specific variants of gradient-based methods and evaluate both settings separately. Across protocols, Grad-CAM and Gradient Attention Rollout consistently emerged as the strongest class-specific methods, although their relative ranking depended on the evaluation design. These findings expose important limitations of current saliency-based explainability approaches and highlight the need for more robust and standardized evaluation frameworks for trustworthy clinical AI systems.

Fine tracking in inter-satellite optical links must compensate residual line-of-sight (LOS) motion despite sensor noise, vibration, model mismatch, and actuator latency. This paper develops a receiver-side physics-informed residual digital twin (PIR-Twin) that combines a nominal LOS transition, Gaussian optics, a nonlinear quadrant-photodetector observation, and extended Kalman filter synchronization. A normalized autoregressive ridge model learns the transition mismatch from independent calibration estimates, while the known delayed fine-steering-mirror correction remains separate from the physical LOS dynamics. The synchronized twin predicts the LOS at the command-actuation instant and enables proactive fine tracking. A controlled evaluation compares open-loop, reactive, nominal-predictive, and PIR-Twin operation using disjoint tuning, calibration, and test realizations. Under the nominal scenario, PIR-Twin reduces RMS pointing error by 14.1% relative to reactive tracking and also improves actuation-time prediction accuracy. Extended robustness tests show that the proposed method retains the lowest mean pointing error over a broad actuator-delay range and under increased time-varying LOS-motion amplitudes without residual-model retraining. The results demonstrate that correcting systematic short-horizon model mismatch, rather than relying on nominal extrapolation alone, is the key mechanism enabling effective predictive fine tracking.

Inter-satellite optical links demand high-precision fine tracking while preserving sufficient received power for data communication, yet these functions are often treated separately at the receiver. This paper proposes a dual-function optical receiver that integrates data reception and fine tracking on a shared, intentionally defocused receiver plane. The architecture combines a central data lens with an annular four-segment tracking detector, creating a fundamental tradeoff between datapower collection and angular-estimation capability. A scalar Fresnel wave-optical model is developed together with nonlinear two-dimensional calibration and a noise-aware worstcase angular-accuracy framework. The receiver geometry is then jointly optimized to maximize the guaranteed fine-tracking range subject to a minimum data-power constraint. Results demonstrate that appropriate co-design of the central aperture and defocus substantially enlarges the usable fine-tracking region while maintaining the required communication-path power. The proposed framework provides a receiver-level benchmark for analyzing and designing integrated communication-and-tracking architectures in future optical inter-satellite terminals.

Future 5G-Advanced and 6G networks increasingly reuse uplink communication waveforms for positioning and sensing. This raises privacy concerns, as user equipments (UEs) may unintentionally reveal precise timing information even when positioning services are not explicitly requested. While prior works show generic orthogonal frequency division multiplexing (OFDM) pilots can be manipulated to degrade time-of-arrival (ToA) estimation without compromising data links, this paper extends these concepts to a realistic 5G new radio (NR) up-link framework including Sounding Reference Signal (SRS), Demodulation Reference Signal (DMRS), Physical Uplink Shared Channel (PUSCH), and standardized 3GPP channel models. We investigate several UE-side privacy mechanisms: optimized pilot distortion, artificial noise, artificial multipath, and delay spoofing. Through 3GPP-compliant sample-level simulations, we assess their impact via localization, communication, and consistency-based detection metrics. The resulting analysis highlights the trade-offs among privacy, communication reliability, and detectability, providing key insights into waveform-level obfuscation for future integrated sensing and communication (ISAC) systems.

Conventional iterative receiver usually begins with channel estimation using sparse pilot observations and follows with data detection based on the channel estimates, and then updates channel estimation using decision feedback, and so on. In Artificial Intelligence (AI)-based receiver design, it is also crucial to make use of such progressively enriched data observations to enhance the generative channel estimation. However, the statistical characteristics and reliability of the data decisions always evolve with the channel estimation processes, which brings great challenges to the design of the overall learning framework and algorithms. In this paper, we propose Diff-Rx, an incrementally conditioned diffusion-based receiver with co-designed data detection and channel estimation, for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Specifically, we develop a condition-adaptive post-training method which enables the generative channel estimator to adapt to the conditioning inputs that are progressively enriched by soft data decisions, and also to implicitly align the pilot- and data-induced channel feature spaces to mitigate potential estimation errors. The threshold-free soft decisions provide smooth condition updates without condition-specific reliability tuning. We further develop a conditional diffusion transformer that is capable of performing robust channel estimation under noisy observations and various pilot patterns while reducing the conventional multi-step diffusion generation to one single step. Simulations on both statistical and site-specific ray-tracing channels show that, Diff-Rx exhibits consistent gains across different noise levels, pilot densities and modulation schemes, and works well at a pilot density as low as 1/32 while achieving significant improvement in channel estimation and data detection performances.

We study a dissipativity-based model predictive control (MPC) scheme for energy-optimal constrained output stabilization of a discrete-time nonlinear SISO port-Hamiltonian system described by difference and differential representation. For the optimal control problem to be solved in each MPC step, we establish a measure turnpike behavior. The turnpike set in our work is obtained from the underlying port-Hamiltonian structure without assuming existence of steady-states or periodic orbits. In particular, unlike existing results which typically establish the turnpike property w.r.t. a controlled forward invariant set such as optimal steady-states or optimal periodic orbits, we do not require the turnpike set to be forward control invariant. For MPC, we establish recursive feasibility and show practical stability to the turnpike set w.r.t. the MPC closed loop leveraging both dissipativity and the port-Hamiltonian structure. Finally, we demonstrate our results using two numerical examples.

In this work, we propose a communication-free framework for vision-based formation control of fully actuated underwater robots subject to sensing constraints, collision-avoidance requirements, and input saturations. Recentered barrier Lyapunov functions encode sensing and collision-avoidance constraints, while command-filtered backstepping extends the design to the second-order vehicle dynamics. The resulting control objective is enforced through a quadratic program that explicitly accounts for actuator limits. Conservative sensing domains provide margins from the physical limits and are adaptively relaxed when necessary, allowing temporary violation of the conservative bounds. The proposed approach is validated through realistic Software-in-the-Loop (SITL) simulations in Gazebo.

Cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) is widely adopted as a reference waveform for integrated sensing and communication (ISAC). However, its high peak-to-average power ratio (PAPR) requires power amplifier back-off, thereby reducing the available sensing link budget. Frequency-modulated OFDM (FM-OFDM) provides a constant-envelope signal with 0 dB PAPR and has demonstrated reliable communication performance in high-mobility scenarios, but its sensing characteristics remain largely unexplored. This paper characterizes FM-OFDM as a sensing waveform based on its bandwidth, ambiguity function, sidelobe behavior, and Doppler estimation capability. The analysis shows that its data dependent sidelobe floor is incoherent and decreases through across frame integration, whereas the corresponding CP-OFDM floor remains unchanged. Consequently, under equal occupied bandwidth and transmit power, frame-level integration reverses the single-symbol performance ordering and provides FM-OFDM with approximately 20 dB of additional dynamic range for weak-target detection. Moreover, the zero-delay cut of the FMOFDM ambiguity function is shown to be deterministic and independent of the transmitted data realization, in contrast to linearly modulated waveforms, while a closed-form expression is derived for the sidelobe floor away from the zero-delay cut. Since the nonlinear mapping between the data symbols and time-domain samples prevents the direct application of the conventional CP-OFDM range-Doppler processing chain, a weighted phase-increment Doppler estimator is developed. The proposed estimator enables closed-form prediction of the sensing floor and its crossover point with a minimum computational complexity.

In this paper, a very compact 2 x 2 wideband beamsteering array with monocone antennas is designed and validated for NATO Band III (1350-2400 MHz) tactical communications with interference suppressing ability. The small 4 x 4 x 4 cm$^3$ size of the array enables placing it for example on vehicles with limited space. The array is manufactured and measured, and the interference suppressing capability thereof is studied with a minimum variance distortionless response beamformer and a phase-only beamformer in the presence of a single interferer. It is shown that the designed antenna array can significantly improve the signal-to-noise ratio over 10 dB for angles separated over 15$^\circ$ from the signal-of-interest, and over 20 dB for separation larger than 43$^\circ$ across the operational band.

This paper presents a modular control barrier function (CBF) framework for safe free-flying robotic spacecraft operations during tumbling target capture. Motivated by latest ESA guidelines for safe close proximity operations, safety zones and requirements are translated into dedicated CBFs. The 13-DoF system is decomposed into translational, attitude, and robotic subsystems, each equipped with a safety filter that minimally modifies nominal control inputs in a lightweight quadratic program. The filters enforce a conical approach corridor, collision avoidance zone, attitude line-of-sight pointing, angular velocity limits, robotic joint limits, link-base collision avoidance, and actuator constraints. Dynamic coupling between subsystems is handled by treating upstream safe control commands as known interconnection inputs in the downstream safety filters, preserving modularity while supporting system-level safety. The framework is validated in an on-orbit servicing scenario, including final approach, angular rate synchronization, and tumbling target grasping, using the high-fidelity astrodynamics simulator Basilisk. Monte Carlo simulation results demonstrate runtime efficiency and operational safety for various tumbling rates.

AI-based autonomous agents, typically hosted at data centers, must acquire state information from robots or edge devices in order to issue informed control decisions. Managing uncertainty about the state is particularly consequential in safety-critical settings, in which average-case guarantees are insufficient. In this context, we study a sequential decision maker process that jointly decides which actions to take and when to probe given access to an arbitrary state prediction model. We propose online conformal state probing (OCSP), an action and probing policy that certifies worst-case reliability levels without relying on distributional assumptions. OCSP is designed to provably control the missed query error (MQE), i.e., the fraction of instances where probing would have been beneficial, while minimizing the probing rate. OCSP can be applied to existing pre-trained value-based control policies without requiring retraining or fine-tuning. We validate OCSP through numerical simulations to verify theoretical guarantees and to assess performance trade-offs as a function of the calibration of the state predictor.

Current source inverters (CSIs) are emerging as competitive alternatives to voltage source inverters (VSIs) for high-speed motor drives because they inherently suppress harmonic currents, offer output-voltage boosting, and reduce motor-side electromagnetic interference. Sensorless control of CSI-fed permanent magnet synchronous machines (PMSMs) remains less mature than VSI counterparts, primarily because conventional implementations require measurement of all circuit state variables. This paper proposes a weak-resonance-based approximation that reconstructs stator currents from inverter modulation commands and feedforward capacitor-current estimation, thereby reducing the sensing requirement to the dc-link current and two terminal voltages. The proposed observer–PLL structure is analysed for computational complexity and validated through simulations and experiments on a 100~W, 100~krpm GaN-based two-stage CSI prototype (buck + CSI). Quantitative results demonstrate accurate rotor-position tracking and high-bandwidth operation while maintaining the reduced sensor set. Proposed method performs well in simulation and experiment, achieving a position estimation error below $5^\circ$ at 100~krpm.

Classical coherent binary phase-shift keying (BPSK) reception over additive white Gaussian noise (AWGN) channels is analytically well understood, and the optimum hard-decision detector is known. Therefore, the aim of using neural receivers in this study is not to replace the classical AWGN-BPSK detector. Instead, the AWGN-BPSK setting is deliberately selected as a theoretically verifiable benchmark for analyzing how compactly neural receiver architectures can represent a known decision behavior. This paper compares multi-layer perceptron (MLP) and Kolmogorov–Arnold Network (KAN) receivers using a multi-SNR Nyquist-rate BPSK dataset. The models are evaluated using bit error rate (BER), mean squared error (MSE), and trainable parameter count. Beyond reporting accuracy alone, this work emphasizes a common-loss parameter-efficiency perspective: when two receivers reach a comparable practical BER or loss region, the receiver with fewer parameters is more attractive for real-time deployment. The results show that both MLP and KAN receivers reproduce the expected AWGN-BPSK detection trend, while a compact KAN configuration reaches a comparable operating region with substantially fewer trainable parameters. In particular, the KAN receiver with 3281 parameters achieves a test BER of 2.45 x 10^-4, while the MLP baseline with 8513 parameters achieves a test BER of 2.50 x 10^-4. This corresponds to approximately 61.5% fewer trainable parameters at a comparable BER operating point. This reduction is important for real-time neural receivers because it affects memory footprint, parameter access, inference latency, energy consumption, and hardware feasibility on embedded, software-defined radio, FPGA, ASIC, and edge communication platforms.

Indoor positioning benchmarks rarely co-locate multiple technologies with high-accuracy ground truth, limiting cross-method and cross-technology comparison. We evaluate four range-based positioning methods on fused Ultra-wideband, Bluetooth Low Energy, and WiFi ranges recorded at 48 reference points in an industrial facility with available ground truth. The evaluated methods range from least-squares positioning, with and without robust weighting, through a Bayesian grid filter to a ResNet regressor that we report under two protocols, interpolation and spatial generalisation. Per-technology ranging quality spans roughly two orders of magnitude, which caps any geometry-based solver that weighs anchors equivalently. On fused input the grid filter is competitive with deep learning on median error, while the ResNet's advantage concentrates on the upper tail. Holding reference points out of training increases the learned regressor's median error several-fold, exposing a spatial-generalisation penalty invisible under random splits. The dataset and evaluation code are publicly available.

Partial audio deepfakes manipulate only selected speech regions, making them difficult to be localized. Existing methods exploit boundary cues for partial deepfake localization, but primarily focus on identifying boundary positions rather than modeling the feature changes that characterize authenticity transitions. Meanwhile, the internal characteristics of continuous bona fide and spoofed segments remain underexplored. In this paper, we propose Boundary and Intra-Segment Learning (BISL), which introduces boundary learning to model feature differences between adjacent frames and distinguish authenticity transitions from general acoustic variations. In addition, intra-segment learning captures the overall characteristics of continuous bona fide and spoofed segments while enhancing feature consistency within each segment. By jointly learning frame, boundary, and segment information, BISL enables more effective fine-grained partial audio deepfake localization. Experiments on multiple localization benchmarks show that BISL achieves an EER of 2.52% and an F1-score of 97.40% on PartialSpoof, outperforming the compared methods, while maintaining competitive performance on HAD and improved cross-dataset performance on LPS. The code will be made publicly available upon acceptance.

This paper investigates the joint protection of confidential data and legitimate-user directional information in integrated sensing and communication (ISAC) networks. We consider a multiuser downlink in which passive multi-antenna eavesdroppers (Eves) attempt to decode confidential signals while exploiting legitimate-user reflections for unauthorized angular sensing. To address both threats, we develop a robust covariance-design framework that jointly limits Eve decoding, maintains the transmitter's accuracy in estimating the Eves' directions and shifts the dominant passive-sensing response toward prescribed deceptive directions. A Bayesian angular prior and the corresponding Bayesian Cramer-Rao bound (BCRB) characterize the transmitter's eavesdropper-angle estimation accuracy. Angular uncertainty is represented through geometry-consistent samples, such that each candidate Eve direction jointly determines the corresponding transmitter-Eve channel, user-Eve bearing, and deceptive direction. The resulting design balances worst-user secrecy, sensing accuracy, sensing privacy, and deception power. Robust Eve-decoding constraints are handled through finite sufficient conditions with intersample margins, while continuous ghost dominance is enforced using interval sum-of-squares (SOS) constraints. The resulting nonconvex problem is addressed through successive convex approximation (SCA) and semidefinite relaxation. Numerical results show that the proposed design effectively preserves secrecy and sensing privacy under Eve-angle uncertainty, provides controlled angular deception in single- and multiple-Eve scenarios, and outperforms benchmarks.

Integrated sensing and communications (ISAC) spans radiofrequency (RF), visible-light, optical-fiber, power-line, and acoustic systems, yet technology transfer across these domains remains underexplored. This article examines bidirectional knowledge and technology transfers centred on RF-ISAC. It shows how VLC motivates power-domain sensing, fiber enables differential referencing and distributed processing, and PLC inspires topology-aware monitoring and adaptive probing. Conversely, RF-ISAC contributes joint signal design, nuisance suppression, and weak-return recovery. The article highlights transferable principles, required adaptations to improve ISAC, and information potentially lost across physical media.

This paper proposes a model-free current control method for permanent magnet synchronous motors (PMSMs) based on disturbance feedforward and residual feedback. An ultra-local current model incorporates motor dynamics, parameter uncertainties, cross-coupling effects, and other nonideal factors into generalized lumped disturbances. An extended state observer (ESO) estimates these lumped disturbances and compensates for them through feedforward action, transforming the original PMSM current-control problem into regulation of a simplified post-compensation residual system. A state-feedback H-infinity controller for the residual system is then learned directly from operating data using off-policy integral reinforcement learning. Owing to the simplified residual dynamics, the value function and control policies are parameterized in quadratic and linear forms, reducing the learning problem to low-dimensional parameter estimation without neural-network approximation. The proposed method requires neither prior knowledge nor online identification of PMSM electrical parameters: input-gain mismatch is incorporated into the ESO-estimated lumped dynamics, while the residual-feedback policy is obtained from operating data. Comparative simulations against deadbeat predictive current control, model-based H-infinity control, and model-free predictive current control show fast current tracking, low current distortion, and strong robustness to large parameter variations. With the learned H-infinity policy fixed and without retraining or retuning, nearly unchanged control performance is maintained when stator resistance, stator inductance, and permanent-magnet flux linkage are simultaneously varied to 20% and 200% of their nominal values.

Distributed integrated sensing and communication (DISAC) networks require multi-target tracking methods whose per-node computation and inter-base-station communication remain bounded as both the network and target populations grow. Existing multisensor fusion methods provide a strong theoretical foundation, but scalability is seldom treated as the primary design objective. We develop a grouped-measurement belief-propagation multi-target tracking (MTT) method and combine it with a proposed event-triggered target-handover protocol. When a tracked target is predicted to become observable at another base station, the owner transfers a predicted track message while retaining its local copy. Under bounded local workload and neighbor degree, we show that all variants of the proposed handover methods have network-size-independent per-node loads. Simulations demonstrate that handover reduces boundary track loss relative to uncoordinated processing and approaches coordinated-processing accuracy with lower communication overhead.

Sixth-generation (6G) applications require radio access networks (RANs) to support reliable communication and seamless sensing across their operating regions. In coupled RAN deployments, shared downlink transmitting and uplink receiving sites constrain the network's ability to accommodate asymmetric links and different sensing geometries. De-Coupled RAN (DC-RAN) separates these functions, allowing independently deployed and coordinated base stations to extend uplink and downlink communication and sensing coverage. How this flexibility translates into balanced communication and sensing services, however, remains insufficiently explored. This article revisits the evolution from coupled to DC-RAN from the perspective of service-balanced integrated sensing and communication (ISAC). It examines how architectural choices affect the availability of both services, with communication-sensing coverage symmetry capturing their spatial alignment under application-specific quality requirements. Practical challenges include preserving communication consistency, maintaining sensing continuity, and coordinating distributed resources. Two case studies illustrate how DC-RAN can support coverage symmetry alongside consistent communication, and how complementary observations can sustain continuous and accurate sensing. These examples inform a discussion of future research toward service-balanced ISAC.