arXiv subjects

Yi Gong

Publications and source records attributed to Yi Gong.

At least 19 recordsLinked to original sources

arXiv2026

Exploring the Initial Performance of NB-IoT NTN over GEO: Measurement and Analysis

With the standardization of Non-Terrestrial Networks (NTN) to provide direct satellite connectivity to massive, low-power Internet of Things (IoT) devices, 3GPP IoT-NTN bridges the worlds of cellular and satellite communications. While holding great potential for global connectivity with IoT devices, there exist several concerns about the system performance of IoT-NTN over Geostationary Earth Orbit (GEO), which covers multiple dimensions such as end-to-end delay and energy consumption considering the ultra-long transmission distance from ground IoT terminals to GEO satellite. To answer these concerns, we have conducted the first comprehensive and in depth measurement for NB-IoT NTN over GEO. Based on real NB-IoT NTN testbeds including both Skylo and Tiantong, measurements covering more than six months confirm that the available implementation of NB-IoT NTN remains in the initial stage. Amplified by ultra-long Round-Trip Time (RTT) between ground IoT terminals and GEO satellite, there exists plenty of time and energy consumption during the access process. Interestingly, it also reveals that as an energy-saving mechanism, Power Saving Mode (PSM) fundamentally reshapes NB-IoT NTN traffic into a bursty and access-driven communication pattern and thus has a considerable influence on the end-to-end delay and energy consumption. Finally, we propose corresponding optimization schemes to reduce the delay and energy, which lays a good foundation for the implementation of NB-IoT NTN in the near future.

cs.NI↗

arXiv2026

Unlearning Is Not Just Erasing: Temporal Decoupling via Generation Inequality

Large language models (LLMs) require effective unlearning to address privacy regulations and safety concerns. However, achieving precise forgetting without compromising general utility remains challenging. Existing sequence- and token-level methods penalize target outputs without modeling their context-dependent retrieval paths, which can disrupt linguistic structure or suppress benign knowledge. We present ADU, a fine-grained, training-based framework that shifts unlearning from token erasure to contextual attention-pathway decoupling. Exploiting the functional distinction between local and global attention heads, ADU identifies preplan positions that retrieve persistent sensitive anchors and fixes their candidate paths under the original model. It then trains attention-projection adapters to suppress attention mass along these paths while preserving local-attention structure and retain-set language modeling. Post-training activation exchange tests whether the modified attention-output module transmits the learned forgetting effect. ADU achieves the strongest aggregate performance among evaluated baselines on the TOFU and WMDP benchmarks, including a Forget Quality of (0.93) on TOFU. It preserves 87--98% of model utility (92.9% on average versus 81.9% for baselines) while reducing side effects in benign contexts.

cs.CL↗

arXiv2026

Low-Complexity Gridless Single-Snapshot DoA Estimation via Truncated Hankel Newton-MUSIC

Reconfigurable antenna arrays can provide enhanced spatial Degrees of Freedom (DoFs) for Integrated Sensing And Communication (ISAC) systems, enabling high-resolution Direction of Arrival (DoA) estimation. In highly dynamic scenarios, however, DoA estimation must be performed within short coherence intervals, which often restricts processing to a single snapshot. Conventional subspace methods then suffer from rank deficiency, while Hankel-based spatial smoothing incurs high computational cost when the array size is large. This paper proposes a low-complexity gridless Truncated Hankel NewtonMUSIC framework for single-snapshot DoA estimation. The proposed method constructs a truncated Hankel matrix with a fixed row dimension to recover an effective signal subspace while reducing the cost of correlation construction and subspace decomposition. When the truncation length is independent of the array size, the dominant complexity scales linearly with the number of antenna ports. To reduce grid-induced quantization errors, coarse grid estimates are further refined by a secondorder Newton update in the continuous angular domain. Simulation results show that the proposed method achieves DoA estimation accuracy close to square Hankel Newton-MUSIC while substantially reducing runtime. For large arrays, it provides more than two orders of magnitude runtime reduction compared with conventional square Hankel MUSIC, making it suitable for realtime sensing in reconfigurable antenna-enabled ISAC systems.

eess.SP↗

arXiv2026

Plug-and-Play Volumetric Reconstruction for Compressive Sensing Light-Sheet Microscopy

We investigate volumetric reconstruction for compressive sensing light-sheet microscopy (CS-LSM), where fast volumetric imaging is achieved by encoding multiple axial planes into each camera exposure. To recover the underlying volume from highly multiplexed measurements, we propose a plug-and-play (PnP) framework that flexibly incorporates any user-specified denoiser into the reconstruction process. Building on a slice-based formulation, we further introduce an axial-coupled model that exploits correlations between adjacent slices to improve volumetric continuity. For efficient computation, we derive a Woodbury-based update for the data-consistency step in both the slice-based and axial-coupled formulations, and employ a Gauss-Seidel sweep for the denoising step in the axial-coupled model. Under a weakly convex regularization assumption, we establish subsequential convergence of the proposed algorithm. Experiments on synthetic and real zebrafish-heart data demonstrate that the proposed framework successfully recovers cellular structures from compressed measurements, and provide practical insights into the comparative performance of commonly used denoisers within the PnP framework under the CS-LSM setup.

cs.CV↗

arXiv2026

Event-Adaptive Motion Planning with Distilled Vision-Language Model in Safety-Critical Situations

Robot navigation in safety-critical scenarios faces significant challenges from unforeseen semantic events, where collisions arise primarily from the unpredictable behaviors of dynamic agents rather than unseen objects. While large vision-language models (VLMs) offer remarkable capabilities in commonsense reasoning, frequently invoking them within the continuous control loop introduces severe computational latency, fundamentally destabilizing physical execution. To address these challenges, we propose event-adaptive motion planning (EAMP), an efficient framework for VLM-based robot navigation. Specifically, a prompt-configurable semantic event trigger (PC-SET) selectively activates semantic intervention by continuously monitoring short temporal clips for behavioral anomalies. Upon triggering, an event-triggered distilled SemNav-VLM, fine-tuned via physically verified semantic distillation, maps detected anomalies into discrete strategy-level decisions. Subsequently, a semantic model predictive control (SMPC) module translates these strategies into dynamic reconfigurations of optimization objectives and geometric references. Extensive experiments in safety-critical logistics scenarios demonstrate that EAMP effectively aligns high-level reasoning with low-level control, significantly improving dynamic safety margins over existing baselines while preserving real-time efficiency.

cs.RO↗

arXiv2026

Sensing-Assisted Channel Estimation for Flexible-Antenna Systems: A Unified Framework

Flexible-antenna systems, which use a small number of radio frequency (RF) chains to dynamically access a large set of candidate antenna locations, have emerged as a hardware-efficient architecture for 6G networks. Acquiring accurate channel state information (CSI) is critical for these systems, but it typically incurs a prohibitive pilot overhead that scales with the massive number of candidate locations. To address this bottleneck, we propose a unified sensing-assisted channel estimation framework tailored for flexible-antenna systems. It reduces the full CSI reconstruction problem to a consistent two-stage process: it first resolves the dominant DOAs from the uplink data symbols by exploiting the spatial geometry, requiring no dedicated sensing pilot, and then calibrates the associated path gains using a minimal number of calibration pilots. Building on this pipeline, we develop two Newton-MUSIC algorithms tailored to different propagation environments. For line-of-sight (LOS)-dominant environments with uncorrelated sources, we propose SOC-Newton-MUSIC, which leverages second-order covariance (SOC) for low-complexity DOA sensing. For non-line-of-sight (NLOS) environments with coherent multipath, where the number of sources may exceed the number of activated RF chains, we propose FOC-Newton-MUSIC, which exploits fourth-order cumulants (FOC) to restore source identifiability and structurally expand the available spatial degrees of freedom (DOFs) through a continuous difference co-array. In both cases, by reformulating the spatial spectrum search as a continuous optimization problem, we replace exhaustive dense grid searches with parallelized Newton refinements.

eess.SP↗

arXiv2026

Fronto-parietal and fronto-temporal EEG coherence as predictive neuromarkers of transcutaneous auricular vagus nerve stimulation response in treatment-resistant schizophrenia: A machine learning study

Response variability limits the clinical utility of transcutaneous auricular vagus nerve stimulation (taVNS) for negative symptoms in treatment-resistant schizophrenia (TRS). This study aimed to develop an electroencephalography (EEG)-based machine learning (ML) model to predict individual response and explore associated neurophysiological mechanisms. We used ML to develop and validate predictive models based on pre-treatment EEG data features (power, coherence, and dynamic functional connectivity) from 50 TRS patients enrolled in the taVNS trial, within a nested cross-validation framework. Participants received 20 sessions of active or sham taVNS (n = 25 each) over two weeks, followed by a two-week follow-up. The prediction target was the percentage change in the positive and negative syndrome scale-factor score for negative symptoms (PANSS-FSNS) from baseline to post-treatment, with further evaluation of model specificity and neurophysiological relevance.The optimal model accurately predicted taVNS response in the active group, with predicted PANSS-FSNS changes strongly correlated with observed changes (r = 0.87, p < .001); permutation testing confirmed performance above chance (p < .001). Nine consistently retained features were identified, predominantly fronto-parietal and fronto-temporal coherence features. Negligible predictive performance in the sham group and failure to predict positive symptom change support the predictive specificity of this oscillatory signature for taVNS-related negative symptom improvement. Two coherence features within fronto-parietal-temporal networks showed post-taVNS changes significantly associated with symptom improvement, suggesting dual roles as predictors and potential therapeutic targets. EEG oscillatory neuromarkers enable accurate prediction of individual taVNS response in TRS, supporting mechanism-informed precision neuromodulation strategies.

cs.LG↗

arXiv2026

A prior information informed learning architecture for flying trajectory prediction

Trajectory prediction for flying objects is critical in domains ranging from sports analytics to aerospace. However, traditional methods struggle with complex physical modeling, computational inefficiencies, and high hardware demands, often neglecting critical trajectory events like landing points. This paper introduces a novel, hardware-efficient trajectory prediction framework that integrates environmental priors with a Dual-Transformer-Cascaded (DTC) architecture. We demonstrate this approach by predicting the landing points of tennis balls in real-world outdoor courts. Using a single industrial camera and YOLO-based detection, we extract high-speed flight coordinates. These coordinates, fused with structural environmental priors (e.g., court boundaries), form a comprehensive dataset fed into our proposed DTC model. A first-level Transformer classifies the trajectory, while a second-level Transformer synthesizes these features to precisely predict the landing point. Extensive ablation and comparative experiments demonstrate that integrating environmental priors within the DTC architecture significantly outperforms existing trajectory prediction frameworks

cs.CV↗

arXiv2026

ALTER: Asymmetric LoRA for Token-Entropy-Guided Unlearning of LLMs

Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a LLMs should not know is important for ensuring alignment and thus safe use. However, effective unlearning in LLMs is difficult due to the fuzzy boundary between knowledge retention and forgetting. This challenge is exacerbated by entangled parameter spaces from continuous multi-domain training, often resulting in collateral damage, especially under aggressive unlearning strategies. Furthermore, the computational overhead required to optimize State-of-the-Art (SOTA) models with billions of parameters poses an additional barrier. In this work, we present ALTER, a lightweight unlearning framework for LLMs to address both the challenges of knowledge entanglement and unlearning efficiency. ALTER operates through two phases: (I) high entropy tokens are captured and learned via the shared A matrix in LoRA, followed by (II) an asymmetric LoRA architecture that achieves a specified forgetting objective by parameter isolation and unlearning tokens within the target subdomains. Serving as a new research direction for achieving unlearning via token-level isolation in the asymmetric framework. ALTER achieves SOTA performance on TOFU, WMDP, and MUSE benchmarks with over 95% forget quality and shows minimal side effects through preserving foundational tokens. By decoupling unlearning from LLMs' billion-scale parameters, this framework delivers excellent efficiency while preserving over 90% of model utility, exceeding baseline preservation rates of 47.8-83.6%.

cs.CL↗

arXiv2026

Low-complexity Design for Beam Coverage in Near-field and Far-field: A Fourier Transform Approach

In this paper, we study efficient beam coverage design for multi-antenna systems in both far-field and near-field cases. To reduce the computational complexity of existing sampling-based optimization methods, we propose a new low-complexity yet efficient beam coverage design. To this end, we first formulate a general beam coverage optimization problem to maximize the worst-case beamforming gain over a target region. For the far-field case, we show that the beam coverage design can be viewed as a spatial-frequency filtering problem, where angular coverage can be achieved by weight-shaping in the antenna domain via an inverse FT, yielding an infinite-length weighting sequence. Under the constraint of a finite number of antennas, a surrogate scheme is proposed by directly truncating this sequence, which inevitably introduces a roll-off effect at the angular boundaries, yielding degraded worst-case beamforming gain. To address this issue, we characterize the finite-antenna-induced roll-off effect, based on which a roll-off-aware design with a protective zoom is developed to ensure a flat beamforming-gain profile within the target angular region. Next, we extend the proposed method to the near-field case. Specifically, by applying a first-order Taylor approximation to the near-field channel steering vector (CSV), the two-dimensional (2D) beam coverage design (in both angle and inverse-range) can be transformed into a 2D inverse FT, leading to a low-complexity beamforming design. Furthermore, an inherent near-field range defocusing effect is observed, indicating that sufficiently wide angular coverage results in range-insensitive beam steering. Finally, numerical results demonstrate that the proposed FT-based approach achieves a comparable worst-case beamforming performance with that of conventional sampling-based optimization methods while significantly reducing the computational complexity.

cs.IT↗

arXiv2025

Hierarchical Test of Lorentz Invariance with Gamma-Ray Burst Spectral-Lag Measurements

Gamma-ray bursts (GRBs) are among the most potent probes of Lorentz invariance violation (LIV), offering direct constraints on the quantum gravity energy scale ($E_{\rm QG}$) based on observations of energy-dependent time lags. Individual GRBs with well-defined positive-to-negative lag transitions have been used to set lower limits on $E_{\rm QG}$, but they suffer from uncertainties of spectral-lag measurements and systematics due to theoretical modeling of each burst. Here, we combine observations of 32 GRBs with positive-to-negative lag transitions to derive a statistically robust constraint on $E_{\rm QG}$ through hierarchical Bayesian inference. We find that the dominant systematic uncertainty in LIV constraints arises from the intrinsic lag modeling. Accounting for this uncertainty with cubic spline interpolation, we derive robust limits of $E_{\rm QG,1} \ge 4.37 \times 10^{16}$~GeV for linear LIV and $E_{\rm QG,2} \ge 3.02 \times 10^{8}$~GeV for quadratic LIV. We find that the probability for LIV, i.e., $E_{\rm QG,1}$ being below the Planck scale, is estimated to be around 90\%, which we conclude as no significant evidence for LIV signatures in current GRB spectral lag observations. Our hierarchical approach provides a rigorous statistical framework for future LIV searches and can be extended to incorporate multi-messenger observations.

astro-ph.HE↗

arXiv2025

MA-enhanced Mixed Near-field and Far-field Covert Communications

In this paper, we propose to employ a modular-based movable extremely large-scale array (XL-array) at Alice for enhancing covert communication performance. Compared with existing work that mostly considered either far-field or near-field covert communications, we consider in this paper a more general and practical mixed-field scenario, where multiple Bobs are located in either the near-field or far-field of Alice, in the presence of multiple near-field Willies. Specifically, we first consider a two-Bob-one-Willie system and show that conventional fixed-position XL-arrays suffer degraded sum-rate performance due to the energy-spread effect in mixed-field systems, which, however, can be greatly improved by subarray movement. On the other hand, for transmission covertness, it is revealed that sufficient angle difference between far-field Bob and Willie as well as adequate range difference between near-field Bob and Willie are necessary for ensuring covertness in fixed-position XL-array systems, while this requirement can be relaxed in movable XL-array systems thanks to flexible channel correlation control between Bobs and Willie. Next, for general system setups, we formulate an optimization problem to maximize the achievable sum-rate under covertness constraint. To solve this non-convex optimization problem, we first decompose it into two subproblems, corresponding to an inner problem for beamforming optimization given positions of subarrays and an outer problem for subarray movement optimization. Although these two subproblems are still non-convex, we obtain their high-quality solutions by using the successive convex approximation technique and devising a customized differential evolution algorithm, respectively. Last, numerical results demonstrate the effectiveness of proposed movable XL-array in balancing sum-rate and covert communication requirements.

eess.SP↗

arXiv2025

A Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction

Cardiac contraction is a rapid, coordinated process that unfolds across three-dimensional tissue on millisecond timescales. Traditional optical imaging is often inadequate for capturing dynamic cellular structure in the beating heart because of a fundamental trade-off between spatial and temporal resolution. To overcome these limitations, we propose a high-performance computational imaging framework that integrates Compressive Sensing (CS) with Light-Sheet Microscopy (LSM) for efficient, low-phototoxic cardiac imaging. The system performs compressed acquisition of fluorescence signals via random binary mask coding using a Digital Micromirror Device (DMD). We propose a Plug-and-Play (PnP) framework, solved using the alternating direction method of multipliers (ADMM), which flexibly incorporates advanced denoisers, including Tikhonov, Total Variation (TV), and BM3D. To preserve structural continuity in dynamic imaging, we further introduce temporal regularization enforcing smoothness between adjacent z-slices. Experimental results on zebrafish heart imaging under high compression ratios demonstrate that the proposed method successfully reconstructs cellular structures with excellent denoising performance and image clarity, validating the effectiveness and robustness of our algorithm in real-world high-speed, low-light biological imaging scenarios.

cs.CV↗

arXiv2025

Advancing the Control of Low-Altitude Wireless Networks: Architecture, Design Principles, and Future Directions

This article introduces a control-oriented low-altitude wireless network (LAWN) that integrates near-ground communications and remote estimation of the internal system state. This integration supports reliable networked control in dynamic aerial-ground environments. First, we introduce the network's modular architecture and key performance metrics. Then, we discuss core design trade-offs across the control, communication, and estimation layers. A case study illustrates closed-loop coordination under wireless constraints. Finally, we outline future directions for scalable, resilient LAWN deployments in real-time and resource-constrained scenarios.

eess.SP↗

arXiv2025

Channel Estimation for Wideband XL-MIMO: A Constrained Deep Unrolling Approach

Extremely large-scale multiple-input multiple-output (XL-MIMO) enables the formation of narrow beams, effectively mitigating path loss in high-frequency communications. This capability makes the integration of wideband high-frequency communications and XL-MIMO a key enabler for future 6G networks. Realizing the full potential of such wideband XL-MIMO systems depends critically on acquiring accurate channel state information. However, channel estimation is significantly challenging due to inherent wideband XL-MIMO channel characteristics, including near-field propagation, beam split, and spatial non-stationarity. To effectively capture these channel characteristics, we formulate channel estimation as a maximum a posteriori problem, which facilitates the use of prior channel knowledge. We then propose an unrolled proximal gradient descent algorithm with learnable step sizes, which employs a dedicated neural network for proximal mapping. This design empowers the proposed algorithm to implicitly learn prior channel knowledge directly from data, thereby eliminating the need for explicit regularization functions. To improve the convergence, we introduce a monotonic descent constraint on the layer-wise estimation error and provide theoretical analyses to characterize the algorithm's convergence behavior. Simulation results show that the proposed unrolling-based algorithm outperforms the traditional and deep learning-based methods.

eess.SP↗

arXiv2025

Integrated Sensing and Communications Over the Years: An Evolution Perspective

Integrated Sensing and Communications (ISAC) enables efficient spectrum utilization and reduces hardware costs for beyond 5G (B5G) and 6G networks, facilitating intelligent applications that require both high-performance communication and precise sensing capabilities. This survey provides a comprehensive review of the evolution of ISAC over the years. We examine the expansion of the spectrum across RF and optical ISAC, highlighting the role of advanced technologies, along with key challenges and synergies. We further discuss the advancements in network architecture from single-cell to multi-cell systems, emphasizing the integration of collaborative sensing and interference mitigation strategies. Moreover, we analyze the progress from single-modal to multi-modal sensing, with a focus on the integration of edge intelligence to enable real-time data processing, reduce latency, and enhance decision-making. Finally, we extensively review standardization efforts by 3GPP, IEEE, and ITU, examining the transition of ISAC-related technologies and their implications for the deployment of 6G networks.

eess.SP↗

arXiv2024

Energy-Efficient SLAM via Joint Design of Sensing, Communication, and Exploration Speed

To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots performing simultaneous and continuous sensing and communication. This paper focuses on analyzing the energy efficiency of robot operation for lifelong SLAM by jointly considering sensing, communication and mechanical factors. The system model is built based on a robot equipped with a 2D light detection and ranging (LiDAR) and an odometry. The cloud point raw data as well as the odometry data are wirelessly transmitted to data center where real-time map reconstruction is realized based on an unsupervised deep learning based method. The sensing duration, transmit power, transmit duration and exploration speed are jointly optimized to minimize the energy consumption. Simulations and experiments demonstrate the performance of our proposed method.

cs.RO↗

arXiv2024

Convolutional Dictionary Learning Based Hybrid-Field Channel Estimation for XL-RIS-Aided Massive MIMO Systems

Extremely large reconfigurable intelligent surface (XL-RIS) is emerging as a promising key technology for 6G systems. To exploit XL-RIS's full potential, accurate channel estimation is essential. This paper investigates channel estimation in XL-RIS-aided massive MIMO systems under hybrid-field scenarios where far-field and near-field channels coexist. We formulate this problem using dictionary learning, which allows for joint optimization of the dictionary and estimated channel. To handle the high-dimensional nature of XL-RIS channels, we specifically adopt a convolutional dictionary learning (CDL) formulation. The CDL formulation is cast as a bilevel optimization problem, which we solve using a gradient-based approach. To address the challenge of computing the gradient of the upper-level objective, we introduce an unrolled optimization method based on proximal gradient descent (PGD) and its special case, the iterative soft-thresholding algorithm (ISTA). We propose two neural network architectures, Convolutional ISTA-Net and its enhanced version Convolutional ISTA-Net+, for end-to-end optimization of the CDL. To overcome the limitations of linear convolutional filters in capturing complex hybrid-field channel structures, we propose the CNN-CDL approach, which enhances PGD by replacing linear convolution filters with CNN blocks in its gradient descent step, employing a learnable proximal mapping module in its proximal mapping step, and incorporating cross-layer feature integration. Simulation results demonstrate the effectiveness of the proposed methods for channel estimation in hybrid-field XL-RIS systems.

eess.SP↗