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Shi Jin

Publications and source records attributed to Shi Jin.

At least 19 recordsLinked to original sources
arXiv2026

Asymptotic-Preserving Tensor Neural Networks for 3D High-Field Kinetic Equations with Applications to Uncertainty Quantification

Kinetic equations in the high-field regime describe particle transport dominated by electric-field acceleration and collisional relaxation. These equations are widely used in simulations of plasmas and semiconductor devices. Typical models include the Vlasov--Poisson--Fokker--Planck (VPFP) and Boltzmann--Poisson systems. Their high dimensionality and multiscale nature pose two of the major challenges to accurate and efficient numerical solution. To address these challenges, we propose an asymptotic-preserving tensor neural network (APTNN) method. Specifically, to reduce high-dimensional computational costs and memory requirements, we adopt the canonical polyadic (CP) decomposition structure of TNNs to approximate their solutions. We show that the equation residuals retain this decomposition form, allowing high-dimensional loss integrals to be evaluated through combinations of one-dimensional integrals. The resulting computational and storage costs are reduced from exponential to polynomial growth with dimension. To improve the prediction accuracy in the high-field limit, we enhance the baseline AP loss by incorporating the first moment equation. The added loss term directly constrains the limiting flux closure to reduce its approximation error in the high-field regime. The enhanced AP loss can still be evaluated dimension-wise. Numerical results show that APTNN improves prediction accuracy in the high-field regime with significantly reduced computational costs and memory usage. For a 3D3V VPFP problem with multiple random inputs, training takes less than 30 minutes on a single GPU.

math.NA↗
arXiv2026

Uplink Enhancement for Low-Altitude UAVs via Active RISs Under Existing Cellular Infrastructure

The proliferation of low-altitude aerial Internet of Things applications, such as UAV-based sensing/monitoring, demands high-performance uplinks, yet terrestrial-optimized cellular networks with downtilted base station (BS) antennas form coverage holes that severely degrade aerial uplink. To address this challenge without modifying infrastructure, active reconfigurable intelligent surfaces (RISs) are introduced. Unlike existing studies with complicated active RIS optimizations, we conduct a detailed noise analysis to simplify the transmission design. By deploying one active RIS per BS sector to fully exploit multi-sector reception, we develop a joint sector maximal signal combining scheme that improves signal quality through diversity gain. Active RISs introduce amplifier noise, raising questions about their net benefit. We derive an upper bound on the power ratio between RIS-introduced noise and BS-inherent noise, revealing that the impact of RIS noise is negligible under typical operating conditions. Based on this insight, we maximize a derived lower bound of the received signal-to-noise ratio by sequentially designing the amplitude and phase of reflection coefficients with closed-form expressions. Simulation results validate the theoretical analysis on negligible RIS-introduced noise in typical systems and demonstrate that under the same array gain, a larger RIS size rather than a higher reflection gain is preferred for enhancing the low-altitude uplink transmission.

eess.SP↗
arXiv2026

Reliable Bistatic Ranging With Unreliable Communication Payloads

This paper addresses a key challenge in bistatic integrated sensing and communications (ISAC): \textit{How to achieve reliable ranging using communication data signals that cannot be reliably recovered?} In modern communication systems, data payloads typically occupy far more time-frequency resources than pilots, offering substantially more observations to improve sensing accuracy. Exploiting them requires recovering the transmitted symbols to form sensing references. However, propagation and reflection losses can severely weaken target echoes. The sensing receiver may therefore be unable to recover data transmitted at a high rate selected for communication users. We first show that, at low signal-to-noise ratios (SNRs), using hard symbol decisions as sensing references can introduce a persistent bias in the estimated channel impulse response (CIR). This bias produces an error floor and spurious peaks in the power delay profile (PDP) that persist even after coherent accumulation over a large number of observations. To address this limitation, we develop a pilot-initialized expectation-maximization (EM) estimator that iteratively refines the CIR using posterior symbol moments. Under local regularity conditions and successful initialization, we establish that the EM method converges to the local maximum-likelihood (ML) solution for a sufficiently large number of OFDM data symbols. The converged estimator is asymptotically unbiased, where the CIR estimation mean-square error (MSE) at each delay bin decays as $O(1/M)$ by accumulating $M$ data symbols. Thus, estimation error can vanish through sufficient accumulation even while symbol recovery remains highly unreliable. Numerical results validate the proposed method, demonstrating substantially higher target detection probabilities than pilot-only and hard-decision-based bistatic ranging under the same low-SNR conditions.

eess.SP↗
arXiv2026

Movable Antenna Aided Robust Wireless Transmission: From Model-Based Optimization to Scalable Reinforcement Learning

This paper investigates robust joint antenna positioning and precoding for multiuser movable antenna (MA) systems under imperfect channel state information (CSI). First, we identify the mismatch between the spatial statistics of densely sampled MA channels and the discrete Fourier transform (DFT) basis used for conventional robust precoding. To address this, we develop a novel discrete prolate spheroidal sequence (DPSS)-based a posteriori channel model. By exploiting the spatial covariance structure of MA channels, this model is better suited to modeling MA channel uncertainty. Building on this model, we develop an efficient iterative algorithm for robust joint antenna positioning and precoding to maximize the expected sum rate. By transforming the coupled optimization of antenna positioning and precoding at each iteration into direct comparisons of closed-form scores, we design a low-complexity position replacement scheme that effectively reuses matrix computations across candidate sets. To further improve efficiency and scalability, we exploit the inherent tensor equivariance (TE) of the problem to develop a reinforcement learning framework. To explicitly account for channel uncertainty, we develop novel pairwise user-interaction and CSI-conditioned equivariant attention mechanisms to encode channel statistics and adaptively fuse them with the available CSI. We further introduce distance-biased spatial attention to incorporate the geometry of candidate positions into attention weighting, thereby enhancing spatial feature learning. Simulation results show that the proposed methods improve sum-rate performance while reducing online runtime under imperfect CSI. These methods also achieve near-optimal performance under perfect CSI, closely matching the exhaustive-search method. Moreover, the learning framework generalizes across different numbers of users and MAs without retraining.

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arXiv2026

3D Gaussian Splatting: A New Paradigm for Wireless Environment Representation

Wireless channels are shaped by the surrounding propagation environment, making explicit environment representation an effective approach for characterizing channels. By combining explicit environment modeling with learnable radio frequency characteristics, 3D Gaussian splatting (3DGS) emerges as a new paradigm for wireless environment representation. This article establishes a systematic perspective on 3DGS-enabled wireless communications in terms of environment representation. We first clarify the rationale for extending 3DGS from visual scene modeling to wireless communications and formulate a unified framework for wireless environment representation. Building on this framework, we further investigate the applicability of 3DGS-based representations to a broad range of communication and sensing tasks, and characterize key considerations for practical deployment. Finally, emerging directions are highlighted, including cross-scene generalization, near-field channel modeling, real-world data collection, and lifecycle management.

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arXiv2026

Quantum circuits of elastic wave equations via Schrödingerisation

We construct explicit quantum circuits for the velocity-stress formulation of elastic wave equations in homogeneous media using Schrödingerisation. After rewriting the equations in symmetric matrix form(SMF), we use Fourier spectral discretization for source-driven periodic problems and staggered-grid finite differences for Dirichlet, Neumann, and impedance boundary conditions. The Schrödingerisation method transforms any linear PDEs into a system of Schrödinger-type PDEs -with unitary evolution- using the warped phase transformation that maps the equations in one higher dimension. For periodic problems, we develop circuits by combining controlled Fourier-mode rotations. For physical boundaries, interleaved encoding swaps the spatial and field-component registers, enabling a more efficient circuit construction using shift operators. We analyze $p$th-order Trotter-Suzuki formulas and derive splitting-error and CNOT-gate estimates, under the assumptions of smooth auxiliary initialization and solution-state recovery. Comparison with classical partitioned splitting methods identifies a polynomial improvement in spatial-resolution dependence for the quantum state-evolution subroutine. Noiseless circuit simulations of a forced periodic problem and an impedance problem illustrate the constructions and quantify their errors against exact solutions and classical reference computations.

quant-ph↗
arXiv2026

Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads

Integrated sensing and communication (ISAC) plays a crucial role in 6G, enabling innovative applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilot and random data payload components, poses challenges for target detection due to two reasons: 1) these two components cause coupled shifts in both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in the bistatic setting. Unfortunately, these challenges could not be tackled by existing target detection algorithms. In this paper, a generalized likelihood ratio test (GLRT)-based detector is derived by leveraging the known deterministic pilots and the statistical characteristics of the unknown random data payloads. Due to the analytical intractability of exact performance characterization, we perform an asymptotic analysis for the false alarm probability and detection probability of the proposed detector. The results reveal a deterministic-random trade-off: the deterministic pilots enhance detection through a target-induced mean shift, while the random data payloads provide an additional covariance-domain target signature but simultaneously introduce statistical uncertainty due to their unknown realizations. Simulations validate the theoretical findings and demonstrate the effectiveness of the proposed detector, which highlights the necessity of designing a dedicated detector to fully exploit the signaling resources assigned to random data payloads.

cs.IT↗
arXiv2026

DT-JEPA: Learning to Predict Future Representations for Decision-Making

Decision Transformer (DT) has demonstrated strong performance in various offline reinforcement learning (RL) tasks by conditioning action generation on expected future returns and historical trajectories. However, treating expected future returns as the condition provides an incomplete description of the future, as trajectories with different state transitions and behaviors may share similar returns. Moreover, expected future returns inherently depend on task-specific reward functions, which makes policy learning reliant on reward-labeled datasets and restricts its scalability to tasks with similar but unseen rewards. Recently, Joint Embedding Predictive Architectures (JEPA) have emerged as a promising paradigm of self-supervised learning-based world models, which learn predictive representations of future dynamics in latent space rather than reconstructing future states in the original space. Inspired by JEPA, we propose DT-JEPA, an offline pretraining and online fine-tuning framework that learns general knowledge from reward-free offline trajectories and achieves excellent adaptation to unseen tasks. Instead of predicting a single deterministic future embedding, we design a novel probabilistic predictor to model the conditional distribution of future representations in the latent space, thereby capturing both predictive information and uncertainty about future dynamics. Theoretically, we show that 1) the JEPA objective seeks to maximize a variational lower bound on the mutual information between historical representations and future trajectories; and 2) predictive future-conditioned policy learning admits a finite-sample performance bound with an O(N-1/4) estimation term. Experiments on D4RL benchmarks demonstrate that DT-JEPA achieves efficient adaptation across varying offline RL tasks, outperforming traditional DT-based and other offline RL methods.

cs.LG↗
arXiv2026

Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy

6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especially those enabled by the recently developed foundation models, have been recognized as promising candidates. Foundation models are large-scale AI models with general-purpose feature extraction capabilities, and once trained on massive amounts of data, they can be adapted to solve a wide range of downstream tasks, either in a zero-shot manner or with few-shot fine-tuning. This article provides a comprehensive overview of how foundation models are reshaping physical-layer processing and wireless resource management across three progressive paradigms. First, we examine the adaptation of off-the-shelf pre-trained foundation models to various wireless tasks. Second, we explore wireless-native foundation models, built from scratch on wireless data to bridge cross-domain modality gaps and capture universal wireless-domain physical characteristics. Third, we highlight agentic foundation models, which elevate static data processing into autonomous, reasoning-driven network orchestration. Furthermore, we discuss the impact of applying foundation models to emerging 6G frontiers, including integrated sensing and communications (ISAC), new multiple-input multiple-output (MIMO) architectures, semantic communications, and system-level network autonomy. Finally, we identify critical open challenges and opportunities, charting a promising path toward fully intelligent and adaptive wireless networks.

eess.SP↗
arXiv2026

Wave Processing Unit: Closed-Form Matrix Synthesis for Wave Computing in Multiuser MIMO Communications

Wave computing offers a compelling route to bypass the latency and energy bottlenecks of digital baseband processing. However, existing wave-domain processors rely on task-specific, non-convex iterative optimization, which incurs substantial configuration overhead and limits real-time applications. In this paper, we propose the wave processing unit (WPU), a programmable planar Mach--Zehnder interferometer (MZI) network that accelerates matrix--vector multiplications in MIMO communications. First, we model the tunable MZI element as a parameterized $2\times2$ unitary transformation, and establish a closed-form synthesis framework for the WPU that decomposes an arbitrary unitary matrix into a cascade of MZI elements arranged in a compact rectangular topology. To handle arbitrary complex-valued operators, we further extend the method through singular value decomposition (SVD) and a hardware-efficient semi-unitary synthesis, avoiding redundant hardware and reducing the compilation complexity. We then apply the WPU to multiuser downlink precoding by an efficient WPU synthesis method, and compare the proposed architecture with hybrid and wave-domain ones. Numerical results demonstrate machine-precision synthesis with negligible residuals for up to $64\times64$ operators, with approximately $380\times$ faster configuration than iterative wave computing baselines. For 64-antenna, 8-user downlink precoding, WPU requires 87.5% fewer RF chains than the fully digital architecture while achieving the same sum rate under ideal hardware. These results highlight the potential of the WPU for precise, hardware-efficient wave-domain processing with rapid configuration.

eess.SP↗
arXiv2026

Dual Secure Sensing and Communications in MA-Enabled Networked ISAC Systems

Integrated sensing and communication (ISAC) is envisioned as a cornerstone technology for next-generation networks, yet it introduces significant security risks in both communication and sensing domains. To mitigate the coverage limitations inherent in conventional mono-static or bi-static setups, this paper proposes a networked ISAC framework empowered by movable antennas (MAs). By leveraging flexible antenna reconfiguration, MAs provide additional spatial degrees of freedom to significantly boost beamforming gain and interference suppression. Specifically, we investigate a novel dual-domain security paradigm where networked base stations collaboratively protect uplink (UL) and downlink (DL) transmissions from communication eavesdropping while simultaneously safeguarding target sensing data against sensing eavesdroppers (SEs). To this end, we formulate a joint optimization problem to maximize the sum of secrecy rates, subject to sensing requirements, SE eavesdropping probability constraint, and practical MA deployment constraints, by jointly optimizing receive combiners, transmit beamformers, UL power allocation, and MA positions. To address this non-convex problem with highly coupled variables, an efficient alternating optimization algorithm is proposed, integrating closed-form solutions for receive combiners, successive convex approximation for transmit beamforming and UL power, and a tailored sensing-aware multi-candidate particle swarm optimization for the high-dimensional MA positioning. Extensive numerical results validate the effectiveness of the proposed algorithms, demonstrating that the proposed MA-enabled networked cooperative ISAC design significantly outperforms conventional fixed-position antenna-based designs in terms of security performance, while achieving notable power- and antenna-saving gains.

eess.SP↗
arXiv2026

RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations

Wireless propagation modeling is foundational to the evaluation and design of modern communication systems. To achieve accurate wireless propagation prediction, recent learning-based approaches leverage scene geometry and materials, demonstrating promising generalization across transceiver locations and unseen scenes. However, these approaches remain largely confined to a fixed configuration of array size, receiver orientation, and transmit beam. In this work, we introduce RFWeave, a learned wireless propagation model designed to generalize across scenes, transceiver locations, and transceiver configurations. To accommodate varying array structures, RFWeave incorporates variable-length transceiver tokens that represent antenna elements individually. Furthermore, to capture the geometry between transmit and receive antenna elements, RFWeave aggregates their pairwise spatial relations into propagation tokens. A configuration-conditioned decoder predicts the spatial spectrum and received power. RFWeave is trained on a large-scale simulated dataset spanning 10,000 indoor scenes and more than 5 million channel records under diverse transceiver configurations. Extensive evaluations show that RFWeave achieves state-of-the-art performance across all evaluated generalization settings, improving spatial spectrum structural similarity (SSIM) by up to 15.7% and reducing received power mean absolute error (MAE) by up to 35.4% over the strongest baselines on unseen scenes. Additional experiments on measured DICHASUS data and in beam management further demonstrate RFWeave's broader applicability to real-world channels and downstream wireless tasks.

eess.SP↗
arXiv2026

SiteEvo: An LLM Agent for Autonomous Discovery of Site-Adaptive Massive MIMO Detectors

Designing a massive multiple-input multiple-output (MIMO) detector that is tailored to each specific deployment site can improve detection performance, yet most existing detectors, including deep-unfolding methods, adapt mainly within human-prescribed algorithmic structures. This letter proposes SiteEvo, a site-adaptive agentic discovery framework that extends adaptation from parameter tuning to detector-algorithm discovery. Rather than selecting or recombining predefined architectures, the large language model (LLM) agent synthesizes complete executable detector programs under a common design contract by combining its pretrained communication knowledge, site-specific wireless evidence, and experience accumulated during the search. Each proposal defines its own processing structure and trainable parameterization, and the parameters are optimized by a communication-system backend. The resulting high-performance detector is further structurally refined to reduce block-level computational cost under a bit error rate (BER)-preservation constraint and is finally frozen for deployment without online LLM inference. Simulations across three sites demonstrate BER reductions by factors of 2.6 to 6.0 over the best-performing competing detector at each site at 6 dB, while structural refinement reduces block-level computational cost by 16.7-34.1% under a validation-BER constraint.

cs.IT↗
arXiv2026

UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems

Unmanned aerial vehicle-enabled synthetic aperture radar (SAR) offers significant potential for providing communication services to ground users while sensing the surrounding environment in low-altitude integrated sensing and communication (ISAC) systems. However, its practical deployment is challenged by known non-ULM trajectories and unknown position errors, which violate conventional SAR assumptions and cause severe image defocusing. We address these non-ideal impacts by formulating the ISAC SAR imaging as an exact linear inverse problem to accurately model arbitrary trajectories with multiple-input multiple-output orthogonal frequency division multiplexing waveforms. Based on this formulation, we develop a diffusion posterior sampling (DPS)-based super-resolution SAR framework, where a pre-trained diffusion model provides a data-driven image prior and the exact physical model enforces measurement consistency during posterior inference. The inference process is further accelerated for practical implementation. For unknown position errors, we develop a joint iterative DPS and motion compensation (MoCo) optimization algorithm with discrete cosine transform reparameterization and coarse-to-fine spectral scheduling. Simulations demonstrate that our framework achieves high-accuracy reconstruction across diverse trajectories and remarkable super-resolution, maintaining greater than 21.3 dB peak signal-to-noise ratio (PSNR) even under half physical resolution truncation. Under unknown motion errors, the joint DPS and MoCo algorithm achieves 23.65 dB PSNR with a residual position-error estimation error of 0.19 cm.

eess.SP↗
arXiv2026

Integer-Forcing Precoding: A Geometric Perspective and Low-Complexity Algorithm Design

The joint coefficient--scaling design of integer-forcing (IF) precoding couples a full-rank integer coefficient matrix with a positive diagonal scaling matrix, resulting in a mixed discrete--continuous optimization problem. We first distinguish the original full-rank formulation and its finite-field invertibility requirement from the unimodular restriction studied in the subsequent analysis. Under this restriction, we formulate coefficient selection as the unimodular minimum-sum basis problem (UMSBP). By parameterizing the scaling matrix with its normalized diagonal vector, we define a deterministic exact single-output UMSBP oracle whose preimages partition the scaling manifold into disjoint coefficient-labelled regions. The restricted objective is consequently represented as the lower envelope of fixed-coefficient quadratic functions. Based on this formulation, we derive an exact decomposition into fixed-coefficient scaling optimization, coefficient-consistency verification, cross-region exploration, and candidate comparison. We prove that exact UMSBP evaluation is NP-hard through a polynomial-time reduction from exact Euclidean CVP. For a fixed coefficient matrix, we derive an equivalent convex formulation, its normalized stationarity condition, and a matrix-balancing interpretation. Under the stated positivity and finite-projective-diameter condition

cs.IT↗
arXiv2026

A Multi-Scenario UAV RF Dataset with Real-World Acquisition and Signal Processing Benchmarking

We present a real-world multi-scenario unmanned aerial vehicle (UAV) radio frequency (RF) dataset, namely DRFF-R2, which is collected using a dedicated acquisition platform under diverse operational conditions. All signals are acquired within a unified framework to ensure consistency in hardware configuration and environmental settings. The dataset is systematically organized into seven well-defined subsets corresponding to different operational and signal composition scenarios to facilitate structured experimentation. Each file follows a clearly annotated naming convention to enable convenient data indexing and reproducible analysis. The dataset contains RF recordings from 26 UAV units spanning 8 distinct models, captured across varying flight states, altitudes, speeds, acquisition days, and receiver configurations. By covering diverse acquisition settings and signal compositions, the dataset provides a comprehensive resource for future UAV RF signal research, including RF fingerprinting (RFF) identification, model-level recognition, flight state analysis, time-varying RFF study, and interference-aware signal processing.

eess.SP↗
arXiv2026

XL-ChannelDiff: An Efficient Diffusion-Based Multi-Domain Near-Field Channel Extrapolation Framework for XL-MIMO Systems

Accurate channel state information (CSI) acquisition is essential for unleashing the performance gains of extremely large-scale multiple-input multiple-output (XL-MIMO) systems. However, in near-field regions, CSI acquisition is much more challenging than in the far field due to the high-dimensional channel representation and spherical wavefront propagation. To address this, in this paper, we propose an efficient multi-domain near-field channel extrapolation framework for XL-MIMO systems. Leveraging the conditional denoising diffusion implicit model (CDDIM), our approach enables accurate channel extrapolation across the antenna, frequency, and spatial domains. Specifically, we design a physics-aware CDDIM backbone that incorporates position-embedded patch tokenization and a mask-guided multi-head attention mechanism, enabling the model to exploit position-dependent channel correlations induced by near-field spherical-wave propagation. To ensure high-fidelity extrapolation, we incorporate a Wasserstein GAN (WGAN) discriminator that provides adversarial supervision to the CDDIM during both the training and reverse sampling phases. Additionally, a RePaint-style refinement scheme is introduced to optimize the sampling trajectory, further boosting extrapolation accuracy. Extensive experiments demonstrate the superiority of the proposed framework, achieving superior extrapolation accuracy and robust generalization across diverse domains, varied configurations, and severe masking conditions. Codes are available at https://github.com/Lmyxxn/XL-ChannelDiff.

eess.SP↗
arXiv2026

Statistical Analysis of 3D Ambiguity Functions for Random MIMO-OFDM Signals

Random communication symbols allow integrated sensing and communications (ISAC) systems to exploit payload signals for sensing, but the resulting matched-filter response varies with the transmitted data. Existing statistical analyses of sensing with random orthogonal frequency division multiplexing (OFDM) waveforms have largely focused on delay and Doppler, while the spatial dimension introduced by multi-input multi-output (MIMO) arrays and precoding has received much less attention. This paper develops a three-dimensional ambiguity function (3D-AF) for random MIMO-OFDM waveforms over delay, Doppler, and angle domains. For point-target sensing, we express the 3D-AF as a quadratic form of the random communication symbols, with a deterministic matrix that captures the delay and Doppler shifts, the transmit and receive array responses, and the precoders on different subcarriers. Based on this representation, we derive a closed-form expression for the expected squared 3D-AF and show explicitly how the constellation fourth-order moment (or kurtosis) affects the ambiguity sidelobes. The same characterization yields expected integrated sidelobe level (EISL) and expected sidelobe level (ESL) metrics over mixed delay-Doppler-angle search domains. The framework is further extended to coherent multi-symbol processing with slow-time Doppler, leading to the corresponding fast-slow time (FST) approximation. These results provide a statistical characterization of the sensing ambiguity induced by random MIMO-OFDM communication signals in delay, Doppler, and angle.

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