Papers

Urban cycling is a sustainable mode of transportation, but cyclists remain highly vulnerable in dense traffic because of limited situational awareness and the lack of active safety assistance on conventional bicycles. This paper presents SmartBike, a low power Internet-of-Things (IoT) platform for cyclist safety, combining blind spot monitoring, rider usage detection, localization, and wireless edge-to-gateway communication. The proposed system consists of a bike-mounted IoT node and a gateway assisted monitoring interface. The node integrates three time-of-flight sensors for blind spot detection, an IMU module for motion sensing and on-board speed estimation, a GNSS module for localization, and a saddle mounted piezo-electric transducer for event-driven wake up. To satisfy the energy constraints of the IoT node, the design employs piezo-triggered activation, application specific sensing rates, and compact connectionless Bluetooth Low Energy (BLE) advertisements for low overhead transmission. The firmware is implemented in Zephyr RTOS using a lightweight state machine architecture, while the gateway performs passive BLE scanning, payload decoding, and real-time dashboard visualization. The BLE payload is reduced to 11 bytes, and power analysis shows that sensing accounts for more than 90% of the total energy budget, while wireless communication represents less overall consumption. With a 12600 mWh battery, the estimated lifetime reaches approximately 21 months under typical commuting conditions (Average 40 min per day).

Affine frequency division multiplexing (AFDM) has attracted significant attention owing to its excellent backwards compatibility, full diversity achievability, as well as strong resilience to high Doppler and various hardware impairments. Existing AFDM channel parameter estimation methods, however, either require large pilot overhead or rely on computationally intensive grid search methods. This paper proposes a grid-search-independent, low-overhead, impulse-pilot-based channel parameter estimation method for AFDM systems by exploiting its inherent shift properties. Specifically, we show that the effect of a Doppler shift on the impulse pilot is equivalent to a delay shift in the time domain with a phase rotation. Additionally, both delay and Doppler shifts lead to cyclic shifts in the affine domain. By utilizing these properties in both the time and affine domains, two linear equations are formed to estimate the integer delay and Doppler of each path. The fractional part of the Doppler is estimated from the Doppler-induced leakage around the impulse pilot by exploiting the AFDM input-output relationship. For multipath channels, a successive interference cancellation (SIC) framework is developed, in which the strongest propagation path is first estimated, reconstructed, and canceled before the remaining paths are successively estimated. Simulation results demonstrate that the proposed method significantly reduces computational complexity and allows lower pilot overhead than the benchmarks. The proposed method also achieves improved channel parameter estimation and

Integrated sensing and communications (ISAC) infrastructure can provide an external perception layer for aerial robots, but dynamic blockage makes the set of informative sensing links at the next update uncertain. A single predicted availability pattern can miss low-information outcomes, whereas protecting all patterns wastes power and may become infeasible when all links are blocked. We propose an outage-aware conformal robust power-allocation framework for multi-BS UAV tracking. A history-aware joint predictor maps radar and tracking histories to next-slot availability probabilities, and adaptive prediction sets retain plausible correlated patterns. Tracking constraints are enforced only for retained non-outage patterns, while the all-blocked pattern is treated separately as a physical sensing outage. With per-slot beam directions fixed, the resulting scalar-power allocation is a semidefinite program. A reliability bound relates serviceable-slot tracking failure to deployed-policy miscoverage and optimization infeasibility. Across paired closed-loop simulations over five random seeds, the method attains a 0.01% serviceable-slot constraint-violation rate and uses 47.0% less power than protection over all non-outage patterns.

Accurate and computationally efficient evaluation of non-linear interference (NLI) remains challenging in multi-band WDM systems comprising heterogeneous spans and nonidentical channels. In such systems, channels may have distinct spatial power profiles and multi-subcarrier structures. Spans may use different fiber types with span-dependent dispersion and loss profiles. These systems may further employ general amplification schemes, including forward and backward Raman amplification. Inter-channel stimulated Raman scattering and multiple lumped gain or loss elements may also be present. In this encompassing framework, we provide an efficient decomposition of the GN-model NLI contributions. We show that the NLI power spectral density at any frequency across the WDM comb is fundamentally driven by the spatial auto- and cross-correlations of the channels' spatial power profiles or, equivalently, by the corresponding energy spectra. For this reason we call this formalism the `correlation GN-model', or cGN. Once these correlations or spectra have been computed, the remaining calculations reduces to a fast, well-behaved one-dimensional numerical integral. Moreover, cGN fully accounts for coherent interference of NLI in every pair of spans. Such coherence is often neglected or approximated in existing analytical and semi-analytical GN-model formulations, although it can increase the NLI by several dB. In short, the cGN framework applies to any general systems over any band and any bandwidth, with very high computational efficiency. It is capable of providing the frequency spectrum of NLI at multiple frequencies across each channel or subcarrier. Its accuracy is very high because virtually no approximations are invoked. The cGN framework is available as freely downloadable software from the European Union Zenodo website.

Downlink channel state information (CSI) feedback is essential for beamforming optimization in frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. However, the feedback overhead increases with the number of antennas and subcarriers, posing a major challenge to practical deployment. Although recent deep learning (DL)-based methods reduce this overhead by compressing CSI at the user equipment (UE) and reconstructing it at the base station (BS), most of them treat CSI as a generic image and rely on convolutional or Transformer architectures. As a result, the low-rank multipath prior is insufficiently exploited, while non-negligible computation is introduced at both the UE and the BS. To address this issue, we propose a low-rank prior (LRP)-guided CSI feedback framework that performs structured sensing and reconstruction directly over rank-one channel components. Specifically, the LRP encoder conducts learnable rank-one sensing to obtain path-aware measurements at the UE, while the LRP decoder reconstructs CSI through rank-one synthesis at the BS, avoiding the iterative recovery required by classical solvers. To compensate for finite-rank approximation residuals, we extend our previous DCRNet into DCRNetV2 by incorporating the proposed LRP backbone and gated dilated-convolutional residual branches. Experiments on multiple datasets and scenarios show that the proposed methods achieve a better accuracy-complexity tradeoff than existing model-based and DL-based baselines. In particular, standalone LRP achieves more than $10$~dB NMSE improvement over model-based baselines with much lower complexity, while DCRNetV2 achieves comparable accuracy to state-of-the-art Transformer-based methods with only about one-fifth of the complexity. The source code is available at https://github.com/tangshunpu/DCRNetV2.

The emerging low-altitude economy demands unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) for reliable connectivity and environmental awareness. In particular, embodied UAV agents offer a promising means of supporting autonomous operations through a closed loop linking perception, decision-making, and physical actions. However, each UAV has access only to local observations, and effective cooperation requires exchanging local states and intentions. Directly sharing such information can incur substantial signaling overhead and hinder timely coordination in dynamic environments. To deal with this problem, this paper investigates a cooperative ISAC network of embodied UAV agents and formulates a joint token communications (TokCom) and physical control problem to minimize total propulsion energy subject to communication and sensing rate requirements. Then, we propose a state–intent TokCom (SI-TokCom) framework driven by multi-agent embodied policy learning. Specifically, separate pretrained codebooks enable compact exchanges of local states and intentions, while UAV agents jointly learn to select and compose tokens and determine physical actions based on local observations and received tokens. Simulation results show that SI-TokCom achieves 98.9% and 99.1% of the centralized baseline's communication and sensing rates, respectively. Compared with the local baseline, it improves the corresponding rates by 5.0% and 43.6%, respectively, with essentially unchanged propulsion energy. These results highlight the potential of TokCom for communication-efficient cooperation among embodied UAV agents in ISAC systems.

In this paper, we propose restoration methods for time-varying graph signals using deep algorithm unrolling (DAU). Time-varying graph signals, such as signals obtained from sensor networks, are nonuniformly distributed in space and observed as time series. Since these observed signals often contain noise and missing values, their restoration needs to consider both spatial and temporal relationships. Our approach is based on an optimization problem that models signal properties using a spatiotemporal regularizer that combines a Sobolev operator for spatial smoothness and a multi-tap FIR filter for temporal smoothness. We then unroll the iterative conjugate gradient method to solve this problem and learn the regularization parameters and filter coefficients in each iteration. Our method can be applied to supervised batch, supervised online, and unsupervised batch settings to learn these parameters. Experiments on several synthetic and real-world datasets show that the supervised batch method achieves the lowest RMSEs in almost all cases. The supervised online and the unsupervised batch methods outperform the existing methods of their own settings, and are often competitive with the existing batch methods.

A passive receiver can sense nearby objects from the echoes of a 5G downlink. When the transmitter is a low-Earth-orbit (LEO) satellite, one transmitter covers a wide area, but the receiver must handle tens of kHz of Doppler and a direct-path delay that changes within every observation interval. Such a receiver should be tested where the correct answer is known before it is tried on a satellite. This paper presents gr-ntnisac, an open GNU Radio testbed that applies an emulated LEO channel to a standard 5G New Radio downlink, sends it through two cabled USRPs, and scores the receiver against the delay and Doppler commanded for every path. The same receiver code runs in simulation, on recorded captures, and on hardware. As a first use, we measure cancellation of the direct path, the strong satellite signal that masks weak echoes. Depth is 34.8 dB on hardware and 37.6 dB in a matched simulation without radios, so the radios are not the main limit. Controlled simulation shows that the emulator's 8-tap fractional-delay filter and a simulated rotating blade at almost the direct path's delay each limit depth to about 40 dB; without either, depth reaches 71.0 dB. A diagnostic canceller that also removes the per-subcarrier response and per-symbol phase reaches 72.7 dB on the same captures. On a conducted capture, a payload-aided receiver detects a -25 dB moving path in 76.9 percent of dwells.

Maneuver detection of non-cooperative satellites is an essential task of space situational awareness. The interacting multiple model (IMM) filter has been applied to detect unannounced maneuvers, but the standard IMM assumes a fixed Markov transition probability matrix (TPM) that must be tuned without knowledge of the maneuver rate of the target. To avoid this tuning, adaptive-TPM approaches update the TPM online from statistics computed inside the IMM. However, these statistics already depend on the TPM through the mixing step, and this dependence forms a feedback loop. Under the dynamics mismatch of orbit tracking, the loop can keep the maneuver probability high in the absence of a maneuver or suppress its rise when a maneuver occurs. In this paper, the TPM is driven by the normalized innovation squared (NIS) of a reference coast filter that is not mixed with the IMM, and the NIS is mapped continuously to the coast-to-maneuver transition probability. The reference-driven IMM (RD-IMM) is evaluated with four space-based optical sensors tracking a geosynchronous target. RD-IMM detects small burns that the fixed-TPM IMM fails to detect, with few false declarations in maneuver-free runs, and avoids both failures of the closed-loop adaptation. Moreover, RD-IMM reduces the position error after a small in-track burn by more than 50% compared with the single-filter approaches.

Navigation integrity and estimator credibility use error and uncertainty information to address different questions. This perspective distinguishes actual positioning error from reported statistical characterizations, formulates integrity as control of hazardous unwarned-use risk under the requirement of a specified operation, and describes credibility as the justification of an uncertainty or risk claim by its evidence usage and modeling process. Three hypothetical scenarios show how deteriorated accuracy, a contradicted uncertainty report, and a hazardous event can lead to different credibility and integrity judgments. The resulting assessment asks what happened, why the reported claim should be believed, and whether the information is sufficient for the intended operation.

Integrated Sensing and Communication (ISAC) is envisioned to endow future 6G systems with seamless sensing capabilities. To support efficient sensing with minimum communication overhead, sparse pilots embedded within communication frames have emerged as a promising solution. Along this line of research, existing studies have achieved engaging results in maximizing the unambiguous sensing region. However, jointly maximizing the sensing region and sensing accuracy remains challenging due to the lack of a unified performance metric and an effective pilot design framework. This paper jointly optimizes the unambiguous sensing region and sensing accuracy for estimating delay-Doppler (DD) parameters in Orthogonal Frequency Division Multiplexing (OFDM)-ISAC systems, where sensing mutual information (SMI) is adopted as a unified performance metric to characterize the overall sensing capability. Specifically, the joint optimization is formulated as an SMI maximization problem by systematically resolving sensing ambiguity and optimizing sensing accuracy. In particular, based on the generalized Bezout identity, we derive a 2D (timefrequency) coprime condition, which, as far as the authors know, is the first necessary and sufficient condition to achieve the unique estimation of DD parameters in the literature. Under this unambiguous condition, we further propose an Adam-Guided Iterative Refinement (AGIR) algorithm to optimize the sensing accuracy. Numerical results demonstrate the advantage of the proposed framework over existing designs, owing to the freedom offered by the 2D coprime condition in optimizing the sensing accuracy.

Cochlear implants (CIs) restore speech access but weaken cues needed for vocal emotion recognition. Prior CI-oriented enhancement requires parallel normal/strong recordings and intensity labels. We propose SEER, a retrieval-based framework that learns which same-emotion reference helps each source remain recognizable after CI processing. A source-conditioned retriever learns CI-aware utility from sampled emotional voice conversion outcomes, while uncertainty-guided exploration avoids exhaustive pair evaluation; neither parallel recordings nor intensity labels are required. SEER improves Source macro-F1 at N8 by 7.30 points on RAVDESS and 11.66 points on ESD, with significant ESD gains across N4/N8/N16. Sixteen-listener RAVDESS gains are significant across all conditions. Exhaustive analysis finds an aggregate benefit from stronger references but little effect from matching gender or content.

Monostatic integrated sensing and communication (ISAC) requires in-band full-duplex operation, where self-interference cancellation (SIC) must suppress the strong transmit leakage while preserving weak sensing echoes. In the analog domain, this task is complicated by transmitter impairments, which generate conjugate and nonlinear SI components, and by waveforms with widely different statistics. This paper develops a flexible analog self-interference cancellation (FASIC) framework for monostatic ISAC based on the widely linear normalized least-mean-square (WL-NLMS) algorithm. FASIC generates the cancellation signal in digital baseband, subtracts it in the radio-frequency domain, and enables flexible sensing-range control via a configurable cancellation span. Its normalized adaptation remains stable across waveforms with substantially different peak-to-average power ratios (PAPRs), for which fixed-step adaptation can become unstable. We further relate transmit-waveform statistics to analog SIC performance and predict the waveform-dependent cancellation floor. Simulations validate the analysis and demonstrate stable analog SIC of approximately $30$–$34$ dB, with the low-PAPR waveforms achieving a $3.3$ dB deeper floor owing to their smaller high-order amplitude moments. Near-field static reflections within the cancellation span are suppressed while delayed and Doppler-shifted echoes are preserved. These results establish FASIC as a waveform-robust analog SIC solution and identify waveform statistics as a key consideration for sixth-generation (6G) waveform selection.

The radar cross section (RCS) is a fundamental parameter in radar engineering, as it determines the detectability of a given target by a radar system. Increasing interest in very low observability (VLO) targets has arisen recently. As a result, especial effort is put in developing numerical tools to accurately predict the electromagnetic scattering from perfect electric conductor objects. Integral equation methods are commonly used in this task. However, full-wave computation of the monostatic RCS at a single angle can be rather time-consuming, nevermind computing the RCS in a specific angular interval with a fine sampling. In fact, this is what is needed in industrial applications. A reduced-order model for fast full-wave monostatic RCS evaluation is proposed to easily achieve fine details in the desired angular domain. An open source integral equation solver, taken into account as a black box, is used to get the electromagnetic scattering in non-penetrable objects at some specific incident angles. Several radar targets will show the possibilities and capabilities of these model order reduction approaches.

The underwater sound speed distribution directly governs acoustic propagation paths, rendering it critically important for underwater acoustic communication and target localization. Conventional sound speed profile (SSP) prediction methods provide a good way to estimate the underwater sound speed distribution without on-site data measurement, thus breaking through the coverage area constraints of sonar observation equipment and making the model universal in most marine areas. However, underwater sound speed exhibits multi-scale variations, such as diurnal, quarterly, and intermittent fluctuations caused by ocean processes such as internal waves. This makes it difficult for the fixed structure models in existing methods to have good generalization ability for multi-scale sound speed distribution patterns. To tackle this problem, we proposed a lightweight hybrid basis-function empowered Kolmogorov-Arnold network (LHBF-KAN) model for multi-scale sound speed prediction. We aim to construct a multi-branch representation layer in which different basis functions respond to distinct temporal patterns, from slowly varying background trends to rapid fluctuations induced by dynamic ocean processes, allowing the model to naturally accommodate the inherently multi-scale evolution of sound speed at different depths. To prevent the multi-branch structure from increasing model size, a pruning strategy is further introduced to suppress branches with consistently low contribution during training, yielding a compact architecture, suitable for deployment on resource constrained underwater platforms.

We describe a general method for selecting an orthonormal basis of carrier waveforms that aligns the basis with delay / Doppler characteristics of a wireless channel. We show that our method enables low-complexity equalization for two channels of practical interest. The first is satellite communication and the second is communication from a ground station to an unmanned aerial vehicle (UAV). After Doppler compensation both scenarios are characterized by a first line of sight (LOS) path with zero delay and zero Doppler shift, and a second weaker path. We show that our method is robust to fractional delay and Doppler shifts. We consider orthonormal bases of carrier waveforms that are obtained from the pulsone basis of Zak-OTFS carrier waveforms by applying a generalization of the discrete affine Fourier transform (GDAFT). This family of waveforms includes AFDM and other modulations proposed for 6G. What distinguishes these bases is that the carrier waveforms are common eigenvectors of some maximal commutative subgroup S of a Heisenberg-Weyl group of discrete delay and Doppler shifts. We describe how to choose S to mitigate the damaging effects of interference between carriers. We represent the wireless channel by delay-Doppler taps and observe that a channel tap located within S multiplies every waveform by a complex phase. If all channel taps are located within S, then the channel multiplies every waveform by a complex phase, and a single tap equalizer supports reliable communication. This is the case for a linear time-invariant (LTI) channel, where S is the group of discrete time shifts and the carrier waveforms are discrete tones (OFDM). In general, we choose the subgroup S to maximize the diagonal component of the channel energy.

This paper characterizes the spectral and Ambiguity Function (AF) properties of Frequency Modulated Orthogonal Frequency Division Multiplexing (FM-OFDM) waveforms employing Phase-Shift Keying (PSK). The mainlobe structure of the AF is well approximated by the Ellipse of Ambiguity (EOA) model. The EOA produces exact closed-form expressions that describe the AF mainlobe width in time-delay and Doppler as well as the degree of coupling between them. These expressions yield three fundamental insights into the spectral and AF shapes of FM-OFDM waveforms employing PSK: (1) these waveforms possess a fixed Root Mean Square (RMS) bandwidth for a given modulation index $h$, pulse-length $T$, and number of sub-carriers $L$, (2) they are roughly twice as spectrally efficient as Constant Envelope OFDM (CE-OFDM) waveforms with the same RMS bandwidth, and (3) for practical waveform design parameters will always possess a “Thumbtack-Like” AF shape. Finally, this paper demonstrates a waveform optimization technique that minimizes the FM-OFDM waveform's Auto-Correlation Function (ACF) sidelobes using only a subset of the sub-carrier symbols as sensing “pilot” symbols leaving the rest for communications. This pilot optimization technique is then demonstrated via two illustrative design examples.

As AI models move toward clinical decision-making and personalized treatment, understanding what a model learns is important beyond predictive accuracy alone. We investigate whether personalized latent dynamics reveal clinically associated differences even when predictive fit is similar. A lightweight CNN–Transformer EEG foundation model pretrained on the Temple University EEG Corpus (TUEG) extracts segment-level representations. Using the Temple University Epilepsy Corpus (TUEP), representations are mapped to a shared latent-state space, and sparse multinomial logistic transition distributions (mLTD) are fit independently to each subject to obtain personalized transition-dependency graphs $W_n$. Analyses include $n{=}198$ subjects (99 epilepsy / 99 non-epilepsy). At $k{=}4$, epilepsy subjects exhibit substantially denser learned dependency structure ($p{=}1.1\times10^{-7}$), with the same pattern at $k{=}6$ (19.90 vs. 13.46; $p{=}5.2\times10^{-5}$). Graph-derived features provide moderate group discrimination under 5-fold subject-wise cross-validation (AUROC 0.68 at $k{=}4$; 0.65 at $k{=}6$). In contrast, held-out log-likelihood is nearly identical between groups at $k{=}4$ ($-0.992$ vs. $-0.991$; $p{=}0.95$), with similarly matched next-state prediction (AUROC 0.855 vs. 0.861; $p{=}0.54$). Thus, similar predictive fit does not imply similar learned dynamics: groups can be comparably predictable while differing substantially in the internal dynamical structure learned by personalized models. This distinction motivates evaluating learned structure alongside predictive performance in personalized clinical models.

Federated learning lets a UAV fleet train a shared GPS spoofing detector without raw receiver data leaving any aircraft, and several recent UAV-FL designs weight each client by the validation accuracy it reports about itself. We show this self-report is an exploitable attack lever: two compromised clients of ten that poison part of their data, scale their updates, and inflate their reported accuracy raise backdoor lift to +0.3036, higher than the same attack achieves without lying. We propose receiver-domain behavioral probing, in which the coordinator evaluates every submitted model on counterfactual spoofed samples built by driving each discriminative GPS feature to a benign value, weighting clients by what their models do rather than what they claim. Under independent and identically distributed clients this reduces attacker-induced lift to -0.0265, statistically indistinguishable from an honest fleet, while flagging the compromised aircraft. Unlike Byzantine-robust aggregation it needs no exact attacker count, only an honest majority: when the true count exceeds the configured value, Multi-Krum degrades from +0.0061 to +0.2837 while ours stays near baseline. Evaluation uses one public single-receiver dataset partitioned into simulated clients; we also report where the mechanism fails, under strong client heterogeneity.

A fast antenna coding technique based on pixel antennas is investigated. Antenna coding is a technique that dynamically controls the ON/OFF states of PIN diodes to adjust radiation patterns of pixel antennas. By switching the antenna coder at the baseband symbol period, this technique can modulate information on the radiation patterns in the beamspace. This allows the pixel antenna to transmit additional information, thereby enhancing the spectral efficiency (SE) of multiple-input multiple-output (MIMO) systems. Specifically in this work, we firstly introduce the concept of pixel antenna and the fast antenna coding. Then based on the beamspace channel representation, we provide the system model of MIMO with the fast antenna coding and derive the SE and bit error rate (BER). The SE enhancement achieved by fast antenna coding is also demonstrated. An optimization approach is also presented for obtaining the antenna coders that can generate the desired radiation patterns for fast antenna coding. Using the optimized antenna coders, we evaluate the SE, energy efficiency (EE), and BER of MIMO systems with fast antenna coding. The simulation results show that for a 4-by-4 MIMO system, SE is enhanced by 12 bits/s/Hz and EE can be improved by 33% when compared with conventional MIMO, demonstrating the effectiveness of the fast antenna coding technique.

Conventional wireless communication systems employ antennas with fixed radiation patterns. Enhancing system performance by increasing the number of antennas would push the limits of cost and size for communication devices. To overcome this issue, novel antenna technologies are urgently desired to break through the fundamental performance bottleneck and engineering limits. Pixel antennas provide a general framework for highly reconfigurable antennas, which can flexibly adjust radiation patterns via controlling variable lumped elements placed across adjacent pixels, unlocking novel degrees of freedom (DoF) in the beamspace domain. Recently, an emerging antenna coding technique links wireless system performance to the antenna configurations. This enables the use of pixel antennas to enhance system performance by leveraging the DoF from the antenna side. In this article, we provide a comprehensive overview on antenna coding by pixel antennas. Specifically, based on the pixel antenna model, the concept of antenna coding is firstly introduced, followed by a discussion on the classifications of antenna coding. Two cases for channel gain and multiplexing gain enhancement are highlighted to show the effectiveness of antenna coding in wireless communications. Besides, various optimization approaches for antenna coding design are investigated. Finally, the challenges and opportunities are outlined, providing guidance for future works.

This letter investigates efficient near-field beamforming designs for wireless communication systems aided by multiple extremely large-scale intelligent reflecting surfaces (Multi-XL-IRSs). Previous studies have mostly assumed far-field channel conditions, based on which angle-based beamforming is adopted for the base station (BS) and IRSs. However, this approach may lead to degraded performance in practical near-field scenarios. To address this issue, we consider near-field channel modeling and formulate an optimization problem aimed at maximizing the received power at the user equipment (UE) by jointly optimizing the beamforming at the BS and IRSs. Given the intractability under the beam training (BT)-based communication framework, the problem is revisited and reformulated. Subsequently, an efficient algorithm based on alternating optimization (AO) is proposed to solve the reformulated problem. Numerical results demonstrate that, in typical setups, our AO-based beamforming designs provide over 170% and 20% received power gains over conventional angle-based beamforming and beam focusing, respectively. Furthermore, the power scaling law derived under far-field conditions no longer holds in near-field scenarios.

This paper proposes a progressive Bayesian multiple-input multiple-output (MIMO) sensing framework for multiple targets over multiple stages. We consider a practical yet challenging scenario where the targets' angles are unknown and random parameters to be estimated, while the targets' reflection coefficients are unknown nuisance parameters. With an initial prior probability density function (PDF) for the targets' angles, we progressively update the prior PDF for each sensing stage as the posterior PDF obtained from the previous stage, based on which Bayesian transmit beamforming optimization is performed to minimize the sum posterior Cramér-Rao bound (PCRB) in estimating the targets' angles and Bayesian sensing is performed with the help of new observations in this stage. To analytically characterize the intractable and high-dimensional posterior PDF with low complexity, we propose a variational Bayesian inference based approach which derives a surrogate posterior PDF in closed form with only polynomial complexity over the number of targets, in sharp contrast to existing numerical calculation approaches with exponential complexity. Numerical results validate the efficacy of our proposed framework in progressively refining sensing performance.

Affine frequency division multiplexing (AFDM) is robust to doubly dispersive channels in high-mobility 6G systems but, like orthogonal frequency division multiplexing (OFDM), suffers from a high peak-to-average power ratio (PAPR). Spreading with a discrete affine Fourier transform (DAFT) yields DAFT-s-AFDM, whose precoder and modulator chirps can be chosen independently. For a single user with zero-offset localized allocation, we show that these two chirps merge into a single residual chirp whose rate, with the other two chirp rates fixed, alone determines their effect. At the transmitter, this rate defines a circulant spreading kernel whose fourth moment quantifies the envelope fluctuation; symmetries reduce its characterization to rates in [0, 1/4]. At a linear minimum mean-square error (MMSE) receiver, the residual chirp redistributes the mean- square error across symbols without changing its total; under a Gaussian approximation, a more uniform per-symbol error lowers the uncoded bit error rate (BER). In uncoded QPSK simulations, a zero rate yields the lowest PAPR, whereas a generic rate reduces the signal-to-noise ratio required for a BER of $10^{-3}$ by 4.4 dB at a 3.33 dB PAPR cost (exceedance probability $10^{-3}$);a rate of 1/3 captures 2.8 dB of this gain for only 0.88 dB of that cost.

Electroencephalography (EEG) is highly susceptible to electromyographic (EMG) artifacts, whose temporal heterogeneity and spatial-spectral overlap with neural activity can leave mixed sources after blind source separation. Existing artifact-removal methods are further limited by scarce reliable component-level ground truth: expert annotations are costly and subjective, while no established method provides realistic simulation-based ground truth for EMG contamination in multichannel scalp EEG. To address these limitations, we propose a framework combining a frequency-aware high-dimensional representation with Multi-Instance Learning. The representation unfolds separated components into frequency-resolved intra-components, creating a space in which mixed neural and muscular activity becomes more separable, while the weakly supervised learning formulation enables artifact-likelihood scores for individual intra-components to be learned from epoch-level labels without finer-grained ground truth. The resulting intra-component classifier supports fine-grained EMG artifact detection and score-guided attenuation. Experiments on held-out subjects show that the framework learns informative intra-component scores and reduces artifact-related spectral deviations most clearly for jaw tension, with moderate effects for raising eyebrows and limited effects for frowning.

As in all fields of engineering, control engineers constantly face a dichotomy between achieving high performance and the limited availability of resources for sensing, actuation, and control. This tension often translates into formalizing design problems with sparsity constraints; that is, the design involves decision variables whose number, spatial reach, temporal spread, or other physical expressions are much smaller than in the ideal scenario of unconstrained optimization of a task-related performance metric. Even though this fundamental limitation underlines a plethora of methodological approaches and application domains, the control literature is fragmented into several lines of work tailored to specific scenarios. This survey fills the gap by bringing together several lines of work on handling sparsity in relevant control problems such as actuator scheduling, minimal-energy controllability, maximum hands-off control, and online convex optimization, and weaving a red thread through theoretical foundations and algorithms. Beyond establishing a coherent, structured narrative of problems and solutions for handling sparsity in control scenarios, we discuss open areas and relevant directions for future research, motivated by the growing complexity of control systems and their relations with key domains such as pervasive computing, distributed learning, cyberphysical security, and ecological footprint.

Broadband in-air sonar sensors with a sparse microphone array form images through delay-and-sum beamforming, which leaves high sidelobes and an angular resolution that degrades strongly towards the sides of the field of view. Deconvolving these images requires access to the point-spread function of the complete imaging chain, including the emitter spectrum and directivity and the array imperfections. In practice, these aspects are hard to calibrate for. In this letter, we propose a self-supervised method that learns a deconvolution network together with a physics-structured model of the point-spread function, requiring only the sonar data itself and the odometry of a moving robot on which the sensor is mounted. Using acoustic flow warping, the internal representation of the model is warped and subsequently rendered through the learned point-spread function. This is then compared to the measured images under a speckle likelihood in accordance with the statistical model of image formation. In simulation, the learned model recovers the unknown emitter directivity, and the deconvolved images reach a constant angular resolution of 5 to 6 degrees over the full field of view, against 12 to 30 degrees for delay-and-sum and 7 to 23 degrees for delay-multiply-and-sum with coherence factor. The method outperforms classical deconvolution with the nominal point-spread function, and even outperforms CLEAN with the true one.

We introduce a novel second-order optimization framework for minimizing so-called variable projection functionals. We demonstrate that the proposed framework is especially usefulfor the training of variable projection based kernel methods. In particular, the problem of efficiently training variable projection support vector machines (VP-SVMs) is considered. We show the effectiveness of the proposed training methodology in a real-world application, namely we demonstrate how second-order trust region algorithms can be used to train VPSVM models to recognize road surface abnormalities based on 1D signals obtained from a tire sensor.

In this paper, we propose a deep learning-based predictive beamforming algorithm for integrated sensing and communications in vehicle-to-infrastructure networks, where echo signals are leveraged to enhance communication performance. We formulate a sum spectral efficiency maximization problem under a sensing signal-to-interference-plus-noise ratio (SINR) constraint and construct a penalty-based unconstrained surrogate objective for network training. Based on this formulation, the beamforming matrix is predicted using sensing echoes and historical beamformers from previous time slots. To this end, we develop a predictive beamforming network that processes sensing echoes and beamforming inputs through separate encoders to preserve their modality-specific characteristics. This separate design enables each modality to learn rich and complementary representations which are more effectively exploited through a cross-attention mechanism compared to naive input fusion. The resulting features are fused via cross-attention, while gated recurrent units capture temporal dependencies across time slots. Simulation results validate the performance of the proposed method.

This paper establishes a finite-aperture model for pattern-reconfigurable antennas (PRAs) and exploits its aperture-constrained Fourier representation to develop an efficient multiuser beamforming framework. First, we develop an ideal model by representing the complex radiation pattern using a Fourier series. By applying the Jacobi-Anger expansion, we relate the antenna aperture to the effective Fourier order and derive aperture-dependent power bounds for individual Fourier coefficients. Second, we employ a practical multiport network model and design three representative reconfigurable pixel antennas to verify the predicted effective Fourier orders and per-order power constraints. We then formulate weighted sum-rate maximization for multiuser downlink transmission under both models. Using bounded Fourier coefficients as auxiliary variables, we develop a coefficient-bound penalty-based weighted minimum mean-square error algorithm that separates continuous beamforming from discrete switch-state optimization. We further propose a one-bit neighborhood search that exploits the Sherman-Morrison identity to accelerate the discrete optimization. Simulation results validate the physical relevance of the ideal model and demonstrate multiuser sum-rate gains and reduced optimization runtime for densely switched antennas. The proposed model provides a physically grounded basis for PRA-enabled communication analysis and design, while the framework accommodates different beamforming objectives and PRA implementations.