arXiv subjects

Shi Jin

Publications and source records attributed to Shi Jin.

At least 163 records · Page 9Linked to original sources

arXiv2025

Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments

Foundation model-based semantic transmission has recently shown great potential in wireless image communication. However, existing methods exhibit two major limitations: (i) they overlook the varying importance of semantic components for specific downstream tasks, and (ii) they insufficiently exploit wireless domain knowledge, resulting in limited robustness under dynamic channel conditions. To overcome these challenges, this paper proposes a foundation model-based adaptive semantic image transmission system for dynamic wireless environments, such as autonomous driving. The proposed system decomposes each image into a semantic segmentation map and a compressed representation, enabling task-aware prioritization of critical objects and fine-grained textures. A task-adaptive precoding mechanism then allocates radio resources according to the semantic importance of extracted features. To ensure accurate channel information for precoding, a channel estimation knowledge map (CEKM) is constructed using a conditional diffusion model that integrates user position, velocity, and sparse channel samples to train scenario-specific lightweight estimators. At the receiver, a conditional diffusion model reconstructs high-quality images from the received semantic features, ensuring robustness against channel impairments and partial data loss. Simulation results on the BDD100K dataset with multi-scenario channels generated by QuaDRiGa demonstrate that the proposed method outperforms existing approaches in terms of perceptual quality (SSIM, LPIPS, FID), task-specific accuracy (IoU), and transmission efficiency. These results highlight the effectiveness of integrating task-aware semantic decomposition, scenario-adaptive channel estimation, and diffusion-based reconstruction for robust semantic transmission in dynamic wireless environments.

eess.IV↗

arXiv2025

Schrodingerization based quantum algorithms for the time-fractional heat equation

We develop a quantum algorithm for solving high-dimensional time-fractional heat equations. By applying the dimension extension technique from [FKW23], the $d+1$-dimensional time-fractional equation is reformulated as a local partial differential equation in $d+2$ dimensions. Through discretization along both the extended and spatial domains, a stable system of ordinary differential equations is obtained by a simple change of variables. We propose a quantum algorithm for the resulting semi-discrete problem using the Schrodingerization approach from [JLY24a,JLY23,JL24a]. The Schrodingerization technique transforms general linear partial and ordinary differential equations into Schrodinger-type systems--with unitary evolution, making them suitable for quantum simulation. This is accomplished via the warped phase transformation, which maps the equation into a higher-dimensional space. We provide detailed implementations of this method and conduct a comprehensive complexity analysis, demonstrating up to exponential advantage--with respect to the inverse of the mesh size in high dimensions~--~compared to its classical counterparts. Specifically, to compute the solution to time $T$, while the classical method requires at least $\mathcal{O}(N_t d h^{-(d+0.5)})$ matrix-vector multiplications, where $N_t $ is the number of time steps (which is, for example, $\mathcal{O}(Tdh^{-2})$ for the forward Euler method), our quantum algorithms requires $\widetilde{\mathcal{O}}(T^2d^4 h^{-8})$ queries to the block-encoding input models, with the quantum complexity being independent of the dimension $d$ in terms of the inverse mesh size $h^{-1}$. Numerical experiments are performed to validate our formulation.

math.NA↗

arXiv2025

RSU-Assisted Resource Allocation for Collaborative Perception

As a pivotal technology for autonomous driving, collaborative perception enables vehicular agents to exchange perceptual data through vehicle-to-everything (V2X) communications, thereby enhancing perception accuracy of all collaborators. However, existing collaborative perception frameworks often assume ample communication resources, which is usually impractical in real-world vehicular networks. To address this challenge, this paper investigates the problem of communication resource allocation for collaborative perception and proposes RACooper, a novel RSU-assisted resource allocation framework that maximizes perception accuracy under constrained communication resources. RACooper leverages a hierarchical reinforcement learning model to dynamically allocate communication resources while accounting for real-time sensing data and channel dynamics induced by vehicular mobility. By jointly optimizing spatial confidence metrics and channel state information, our approach ensures efficient feature transmission, enhancing the effectiveness of collaborative perception. Simulation results demonstrate that compared to conventional baseline algorithms, RACooper achieves significant improvements in perception accuracy, especially under bandwidth-constrained scenarios.

eess.SY↗

arXiv2025

Quantum Algorithms for Solving Generalized Linear Systems via Momentum Accelerated Gradient and Schrödingerization

In this paper, we propose a quantum algorithm that combines the momentum accelerated gradient method with Schrödingerization [S. Jin, N. Liu and Y. Yu, Phys. Rev. Lett, 133 (2024), 230602][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603], achieving polynomial speedup over its classical counterpark in solving linear systems. The algorithm achieves a query complexity of the same order as the Schrödingerization based damped dynamical system method, namely, linear dependence on the condition number of the matrix, and can overcome the practical limitations of existing non-Schrödingerization-based quantum linear system algorithms. These limitations stem from their reliance on techniques such as VTAA and RM, which introduce substantial quantum hardware resource overhead. Furthermore, it demonstrates both theoretically and experimentally that the auxiliary variables introduced by our method do not dominate the error reduction at any point, thereby preventing a significant increase in the actual evolution time compared to the theoretical prediction. In contrast, the damped method fails to meet this criterion. This gives new perspectives for quantum algorithms for linear systems, establishing a novel analytical framework for algorithms with broader applicability, faster convergence rates, and superior solution quality.

quant-ph↗

arXiv2025

Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization

Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However, federated Adam (FedAdam) algorithms suffer from a threefold increase in uplink communication overhead compared to federated SGD (FedSGD) algorithms, which arises from the necessity to transmit both local model updates and first and second moment estimates from distributed devices to the centralized server for aggregation. Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server. To further reduce the communication overhead, the updates of local model parameters and moment estimates incorporate a shared sparse mask (SSM) into the sparsification process, eliminating the need for three separate sparse masks. Theoretically, we develop an upper bound on the divergence between the local model trained by FedAdam-SSM and the desired model trained by centralized Adam, which is related to sparsification error and imbalanced data distribution. By minimizing the divergence bound between the model trained by FedAdam-SSM and centralized Adam, we optimize the SSM to mitigate the learning performance degradation caused by sparsification error. Additionally, we provide convergence bounds for FedAdam-SSM in both convex and non-convex objective function settings, and investigate the impact of local epoch, learning rate and sparsification ratio on the convergence rate of FedAdam-SSM. Experimental results show that FedAdam-SSM outperforms baselines in terms of convergence rate (over 1.1$\times$ faster than the sparse FedAdam baselines) and test accuracy (over 14.5\% ahead of the quantized FedAdam baselines).

cs.LG↗

arXiv2025

Quantum simulation of a class of highly-oscillatory transport equations via Schrödingerisation

In this paper, we present quantum algorithms for a class of highly-oscillatory transport equations, which arise in semiclassical computation of surface hopping problems and other related non-adiabatic quantum dynamics, based on the Born-Oppenheimer approximation. Our method relies on the classical nonlinear geometric optics method, and the recently developed Schrödingerisation approach for quantum simulation of partial differential equations. The Schrödingerisation technique can transform any linear ordinary and partial differential equations into Hamiltonian systems evolving under unitary dynamics, via a warped phase transformation that maps these equations to one higher dimension. We study possible paths for better recoveries of the solution to the original problem by shifting the bad eigenvalues in the Schrödingerized system. Our method ensures the uniform error estimates independent of the wave length, thus allowing numerical accuracy, in maximum norm, even without numerically resolving the physical oscillations. Various numerical experiments are performed to demonstrate the validity of this approach.

math.NA↗

arXiv2025

A Novel Privacy Enhancement Scheme with Dynamic Quantization for Federated Learning

Federated learning (FL) has been widely regarded as a promising paradigm for privacy preservation of raw data in machine learning. Although, the data privacy in FL is locally protected to some extent, it is still a desideratum to enhance privacy and alleviate communication overhead caused by repetitively transmitting model parameters. Typically, these challenges are addressed separately, or jointly via a unified scheme that consists of noise-injected privacy mechanism and communication compression, which may lead to model corruption due to the introduced composite noise. In this work, we propose a novel model-splitting privacy-preserving FL (MSP-FL) scheme to achieve private FL with precise accuracy guarantee. Based upon MSP-FL, we further propose a model-splitting privacy-preserving FL with dynamic quantization (MSPDQ-FL) to mitigate the communication overhead, which incorporates a shrinking quantization interval to reduce the quantization error. We provide privacy and convergence analysis for both MSP-FL and MSPDQ-FL under non-i.i.d. dataset, partial clients participation and finite quantization level. Numerical results are presented to validate the superiority of the proposed schemes.

math.OC↗

arXiv2025

UAV Individual Identification via Distilled RF Fingerprints-Based LLM in ISAC Networks

Unmanned aerial vehicle (UAV) individual (ID) identification is a critical security surveillance strategy in low-altitude integrated sensing and communication (ISAC) networks. In this paper, we propose a novel dynamic knowledge distillation (KD)-enabled wireless radio frequency fingerprint large language model (RFF-LLM) framework for UAV ID identification. First, we propose an RFF-LLM framework based on the modified GPT-2 model to improve the identification accuracy in complex outdoor environments. Then, considering the parameter overhead of the RFF-LLM, we design a dynamic KD strategy to compress the model. Specifically, the proximal policy optimization (PPO) algorithm is employed to dynamically adjust the distillation temperature, overcoming the local optimum dilemma inherent in static KD. As a next step, the knowledge of the RFF-LLM is adequately transferred to the lightweight Lite-HRNet model. Finally, our experiments are conducted based on the self-built drone RFF dataset of Release one, namely DRFF-R1, by collecting the I/Q signals of 20 commercial UAVs in channel 149. The experiment results show that the proposed framework achieves 98.38% ID identification accuracy with merely 0.15 million parameters and 2.74 ms response time, which outperforms the benchmarks.

cs.CR↗

arXiv2025

Cylindrical RIS-Assisted Low-Complexity Transmission with Differentiated Visible Regions Exploiting Statistical CSI

Reconfigurable intelligent surfaces (RIS), recognized as a critical enabler for 6G networks, exhibit unprecedented capabilities in electromagnetic wave manipulation and wireless channel reconfiguration. By leveraging existing network infrastructure, RIS can cost-effectively create signal hotspots in low-altitude environments, ensuring robust connectivity to support the sustainable development of the low-altitude economy. However, achieving optimal phase shift design in multi-user scenarios faces two major challenges: the high-dimensional optimization introduced by massive RIS elements, and the persistent coupling of multi-user signals caused by shared RIS reflections. This paper utilize the visible region of an RIS arranged as the uniform cylindrical array (UCA) to reduce the complexity of phase shift design. Under the UCA architecture, RIS elements are categorized into two types: user-specific units and multi-user shared units. We then determine the optimal phase shifts by iteratively optimizing the phase shifts of multi-user shared units while directly configuring those of user-specific units based on a derived closed-form solution. The proposed approach significantly reduces optimization complexity, which is further corroborated by numerical simulation results demonstrating its substantial impact on both system performance and computational efficiency compared to the conventional RIS with uniform planar array.

cs.IT↗

arXiv2025

A Fluid Antenna Enabled Physical Layer Key Generation for Next-G Wireless Networks

As a promising physical layer security technique, physical layer key generation (PLKG) enables legitimate users to obtain secret keys from wireless channel without security infrastructures. However, in harsh propagation environments, the channel characteristic becomes unsatisfactory, the key generation rate (KGR) is significantly deteriorated. In this paper, we propose a novel fluid antenna (FA) enabled PLKG system to address this challenge. Specifically, we first derive the closed-form expression of the KGR for FA array, and then jointly optimize the precoding matrix and the antenna positions via a particle swarm optimization (PSO) algorithm. Next, to further reduce the computational complexity of the optimization procedure, we develop an alternating optimization (AO) algorithm, which combines the projected gradient descent (PGD) and the PSO. Simulation results demonstrate that by exploiting the additional spatial degree of freedom (DoF), our FA enabled PLKG system is superior to the benchmarks, such as the conventional fixed-position antenna (FPA) array and the reconfigurable intelligent surface (RIS). It is worth highlighting that compared to the conventional uniform planar antenna (UPA), the FA enabled PLKG achieves a 35.42\% KGR performance improvement under PSO algorithm and a 67.73\% KGR performance improvement under AO algorithm, respectively.

eess.SP↗

arXiv2025

Tensor-Structured Bayesian Channel Prediction for Upper Mid-Band XL-MIMO Systems

The upper mid-band balances coverage and capacity for the future cellular systems and also embraces XL-MIMO systems, offering enhanced spectral and energy efficiency. However, these benefits are significantly degraded under mobility due to channel aging, and further exacerbated by the unique near-field (NF) and spatial non-stationarity (SnS) propagation in such systems. To address this challenge, we propose a novel channel prediction approach that incorporates dedicated channel modeling, probabilistic representations, and Bayesian inference algorithms for this emerging scenario. Specifically, we develop tensor-structured channel models in both the spatial-frequency-temporal (SFT) and beam-delay-Doppler (BDD) domains, which leverage temporal correlations among multiple pilot symbols for channel prediction. The factor matrices of multi-linear transformations are parameterized by BDD domain grids and SnS factors, where beam domain grids are jointly determined by angles and slopes under spatial-chirp based NF representations. To enable tractable inference, we replace environment-dependent BDD domain grids with uniformly sampled ones, and introduce perturbation parameters in each domain to mitigate grid mismatch. We further propose a hybrid beam domain strategy that integrates angle-only sampling with slope hyperparameterization to avoid the computational burden of explicit slope sampling. Based on the probabilistic models, we develop tensor-structured bi-layer inference (TS-BLI) algorithm under the expectation-maximization (EM) framework, which reduces computational complexity via tensor operations by leveraging the bi-layer factor graph for approximate E-step inference and an alternating strategy with closed-form updates in the M-step. Numerical simulations based on the near-practical channel simulator demonstrate the superior channel prediction performance of the proposed algorithm.

eess.SP↗

arXiv2025

Criticality-Based Dynamic Topology Optimization for Enhancing Aerial-Marine Swarm Resilience

Heterogeneous marine-aerial swarm networks encounter substantial difficulties due to targeted communication disruptions and structural weaknesses in adversarial environments. This paper proposes a two-step framework to strengthen the network's resilience. Specifically, our framework combines the node prioritization based on criticality with multi-objective topology optimization. First, we design a three-layer architecture to represent structural, communication, and task dependencies of the swarm networks. Then, we introduce the SurBi-Ranking method, which utilizes graph convolutional networks, to dynamically evaluate and rank the criticality of nodes and edges in real time. Next, we apply the NSGA-III algorithm to optimize the network topology, aiming to balance communication efficiency, global connectivity, and mission success rate. Experiments demonstrate that compared to traditional methods like K-Shell, our SurBi-Ranking method identifies critical nodes and edges with greater accuracy, as deliberate attacks on these components cause more significant connectivity degradation. Furthermore, our optimization approach, when prioritizing SurBi-Ranked critical components under attack, reduces the natural connectivity degradation by around 30%, achieves higher mission success rates, and incurs lower communication reconfiguration costs, ensuring sustained connectivity and mission effectiveness across multi-phase operations.

cs.NI↗

arXiv2025

Quantum simulation of Helmholtz equations via Schr{ö}dingerization

The Helmholtz equation is a prototypical model for time-harmonic wave propagation. Numerical solutions become increasingly challenging as the wave number $k$ grows, due to the equation's elliptic yet noncoercive character and the highly oscillatory nature of its solutions, with wavelengths scaling as $1/k$. These features lead to strong indefiniteness and large system sizes. We present a quantum algorithm for solving such indefinite problems, built upon the Schrödingerization framework. This approach reformulates linear differential equations into Schrödinger-type systems by capturing the steady state of damped dynamics. A warped phase transformation lifts the original problem to a higher-dimensional formulation, making it compatible with quantum computation. To suppress numerical pollution, the algorithm incorporates asymptotic dispersion correction. It achieves a query complexity of $\mathcal{O}(κ^2\text{polylog}\varepsilon^{-1})$, where $κ$ is the condition number and $\varepsilon$ the desired accuracy. For the Helmholtz equation, a simple preconditioner further reduces the complexity to $\mathcal{O}(κ\text{polylog}\varepsilon^{-1})$. Our constructive extension to the quantum setting is broadly applicable to all indefinite problems.

math.NA↗

arXiv2025

Quantum simulation of multiscale linear transport equations via Schrödingerization and exponential integrators

In this paper, we present two Hamiltonian simulation algorithms for multiscale linear transport equations, combining the Schrödingerization method [S. Jin, N. Liu and Y. Yu, Phys. Rev. Lett, 133 (2024), 230602][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603] and exponential integrator while incorporating incoming boundary conditions. These two algorithms each have advantages in terms of design easiness and scalability, and the query complexity of both algorithms, $\mathcal{O}(N_vN_x^2\log N_x)$, outperforms existing quantum and classical algorithms for solving this equation. In terms of the theoretical framework, these are the first quantum Hamiltonian simulation algorithms for multiscale linear transport equation to combine the Schrödingerization method with an effective asymptotic-preserving schemes, which are efficient for handling multiscale problems with stiff terms.

quant-ph↗

arXiv2025

Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems

This paper investigates a joint beamforming and resource allocation problem in downlink reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems to minimize the average delay, where data packets for each user arrive at the base station (BS) stochastically. The sequential optimization problem is inherently a Markov decision process (MDP), thus falling within the remit of reinforcement learning. To effectively handle the mixed action space and reduce the state space dimensionality, a hybrid deep reinforcement learning (DRL) approach is proposed. Specifically, proximal policy optimization (PPO)-Theta is employed to optimize the RIS phase shift design, while PPO-N is responsible for subcarrier allocation decisions. The active beamforming at the BS is then derived from the jointly optimized RIS phase shifts and subcarrier allocation decisions. To further mitigate the curse of dimensionality associated with subcarrier allocation, a multi-agent strategy is introduced to optimize the subcarrier allocation indicators more efficiently. Moreover, to achieve more adaptive resource allocation and accurately capture the network dynamics, key factors closely related to average delay, such as the number of backlogged packets in buffers and current packet arrivals, are incorporated into the state space. Furthermore, a transfer learning framework is introduced to enhance the training efficiency and accelerate convergence. Simulation results demonstrate that the proposed algorithm significantly reduces the average delay, enhances resource allocation efficiency, and achieves superior system robustness and fairness compared to baseline methods.

cs.AI↗

arXiv2025

ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks

Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next. We model such process by an interacting particle system with attractive and repulsive forces and the Allen-Cahn force arising in the modeling of phase transition. The dynamics of the system is a reaction-diffusion process which can separate particles without blowing up. This induces an Allen-Cahn message passing (ACMP) for graph neural networks where the numerical iteration for the particle system solution constitutes the message passing propagation. ACMP which has a simple implementation with a neural ODE solver can propel the network depth up to one hundred of layers with theoretically proven strictly positive lower bound of the Dirichlet energy. It thus provides a deep model of GNNs circumventing the common GNN problem of oversmoothing. GNNs with ACMP achieve state of the art performance for real-world node classification tasks on both homophilic and heterophilic datasets. Codes are available at https://github.com/ykiiiiii/ACMP.

cs.LG↗

arXiv2025

Deep Learning-based Position-domain Channel Extrapolation for Cell-Free Massive MIMO

To reduce channel acquisition overhead, spatial, time, and frequency-domain channel extrapolation techniques have been widely studied. In this paper, we propose a novel deep learning-based Position-domain Channel Extrapolation framework (named PCEnet) for cell-free massive multiple-input multiple-output (MIMO) systems. The user's position, which contains significant channel characteristic information, can greatly enhance the efficiency of channel acquisition. In cell-free massive MIMO, while the propagation environments between different base stations and a specific user vary and their respective channels are uncorrelated, the user's position remains constant and unique across all channels. Building on this, the proposed PCEnet framework leverages the position as a bridge between channels to establish a mapping between the characteristics of different channels, thereby using one acquired channel to assist in the estimation and feedback of others. Specifically, this approach first utilizes neural networks (NNs) to infer the user's position from the obtained channel. {The estimated position, shared among BSs through a central processing unit (CPU)}, is then fed into an NN to design pilot symbols and concatenated with the feedback information to the channel reconstruction NN to reconstruct other channels, thereby significantly enhancing channel acquisition performance. Additionally, we propose a simplified strategy where only the estimated position is used in the reconstruction process without modifying the pilot design, thereby reducing latency. Furthermore, we introduce a position label-free approach that infers the relative user position instead of the absolute position, eliminating the need for ground truth position labels during the localization NN training. Simulation results demonstrate that the proposed PCEnet framework reduces pilot and feedback overheads by up to 50%.

cs.IT↗

arXiv2025

CP-OFDM Achieves the Lowest Average Ranging Sidelobe Under QAM/PSK Constellations

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC): What is the optimal communication-centric ISAC waveform for ranging? Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

cs.IT↗