arXiv subjects

Shi Jin

Publications and source records attributed to Shi Jin.

At least 145 records · Page 8Linked to original sources

arXiv2025

Multi-Subarray FD-RIS Enhanced Multi-user Wireless Networks: With Joint Distance-Angle Beamforming

The concept of the frequency diverse reconfigurable intelligent surface (FD-RIS) technology has been introduced, which can enable simultaneous implementation of distance-angle beamforming in far-field communication scenarios. In order to improve the managing ability on undesired harmonic signals and the diversity of frequency offsets, this paper presents a novel multi-subarray FD-RIS framework. In this framework, the RIS is evenly divided into multiple subarrays, each employing a distinct time-modulation frequency to enable the diversity of frequency offsets. Additionally, to suppress the undesired harmonic signals, a new time-modulation technique is employed to periodically adjust the phase-shift of each element. Based on the proposed multi-subarray FD-RIS, the signal processing model is first analytically derived. To evaluate the effectiveness of the proposed multi-subarray FD-RIS, we integrate it into a multi-user communication scenario and formulate an optimization problem that aims to maximize the weighted sum rate of all users. This is achieved by jointly optimizing the active beamforming, time delays, and modulation frequencies. Subsequently, a novel iterative algorithm is proposed to effectively solve this problem with low computing complexity. To evaluate the beamforming capability of the proposed multi-subarray FD-RIS,three communication scenarios with varying spatial correlations among users are considered. Simulation results demonstrate that the proposed multi-subarray FD-RIS can significantly enhance and sustain the performance of communication networks by leveraging unique distance-angle beamforming, even when users share the same angular position where traditional RIS experiences severe degradation.

cs.IT↗

arXiv2025

Planar Diffractive Neural Networks Empowered Communications: A Spatial Modulation Scheme

Diffractive neural networks, where signal processing is embedded into wave propagation, promise light-speed and energy-efficient computation. However, existing three-dimensional structures, such as stacked intelligent metasurfaces (SIMs), face critical challenges in implementation and integration. In contrast, this work pioneers planar diffractive neural networks (PDNNs) empowered communications, a novel architecture that performs signal processing as signals propagate through artificially designed planar circuits. To demonstrate the capability of PDNN, we propose a PDNN-based space-shift-keying (PDNN-SSK) communication system with a single radio-frequency (RF) chain and a maximum power detector. In this system, PDNNs are deployed at both the transmitter and receiver to jointly execute modulation, beamforming, and detection. We conduct theoretical analyses to provide the maximization condition of correct detection probability and derive the closed-form expression of the symbol error rate (SER) for the proposed system. To approach these theoretical benchmarks, the phase shift parameters of PDNNs are optimized using a surrogate model-based training approach, which effectively navigates the high-dimensional, non-convex optimization landscape. Extensive simulations verify the theoretical analysis framework and uncover fundamental design principles for the PDNN architecture, highlighting its potential to revolutionize RF front-ends by replacing conventional digital baseband modules with this integrable RF computing platform.

eess.SP↗

arXiv2025

Cross-Modal Semantic Communication for Heterogeneous Collaborative Perception

Collaborative perception, an emerging paradigm in autonomous driving, has been introduced to mitigate the limitations of single-vehicle systems, such as limited sensor range and occlusion. To improve the robustness of inter-vehicle data sharing, semantic communication has recently further been integrated into collaborative perception systems to enhance overall performance. However, practical deployment of such systems is challenged by the heterogeneity of sensors across different connected autonomous vehicles (CAVs). This diversity in perceptual data complicates the design of a unified communication framework and impedes the effective fusion of shared information. To address this challenge, we propose a novel cross-modal semantic communication (CMSC) framework to facilitate effective collaboration among CAVs with disparate sensor configurations. Specifically, the framework first transforms heterogeneous perceptual features from different sensor modalities into a unified and standardized semantic space. Subsequently, encoding, transmission, and decoding are performed within this semantic space, enabling seamless and effective information fusion. Extensive experiments demonstrate that CMSC achieves significantly stronger perception performance than existing methods, particularly in low signal-to-noise ratio (SNR) regimes.

eess.SP↗

arXiv2025

Learning Multi-Access Point Coordination in Agentic AI Wi-Fi with Large Language Models

Multi-access point coordination (MAPC) is a key technology for enhancing throughput in next-generation Wi-Fi within dense overlapping basic service sets. However, existing MAPC protocols rely on static, protocol-defined rules, which limits their ability to adapt to dynamic network conditions such as varying interference levels and topologies. To address this limitation, we propose a novel Agentic AI Wi-Fi framework where each access point, modeled as an autonomous large language model agent, collaboratively reasons about the network state and negotiates adaptive coordination strategies in real time. This dynamic collaboration is achieved through a cognitive workflow that enables the agents to engage in natural language dialogue, leveraging integrated memory, reflection, and tool use to ground their decisions in past experience and environmental feedback. Comprehensive simulation results demonstrate that our agentic framework successfully learns to adapt to diverse and dynamic network environments, significantly outperforming the state-of-the-art spatial reuse baseline and validating its potential as a robust and intelligent solution for future wireless networks.

cs.AI↗

arXiv2025

Super-Resolution ISAC Receivers: An MCMC-Based Gridless Sparse Bayesian Learning Approach

Integrated sensing and communication (ISAC) is crucial for low-altitude wireless networks (LAWNs), where the safety-critical demand for high-accuracy sensing creates a trade-off between precision and complexity for conventional methods. To address this, we propose a novel gridless sparse Bayesian learning (SBL) framework for joint super-resolution multi-target detection and high-accuracy parameter estimation with manageable computational cost. Our model treats target parameters as continuous variables to bypass the grid limitations of conventional approaches. This SBL formulation, however, transforms the estimation task into a challenging high-dimensional inference problem, which we address by developing an enhanced gradient-based Markov chain Monte Carlo algorithm. Our method integrates mini-batch sampling and the Adam optimizer to ensure computational efficiency and rapid convergence. Finally, we validate the framework's robustness in strong clutter and provide a theoretical benchmark by deriving the corresponding Bayesian Cramer-Rao bound. Simulation results demonstrate remarkable super-resolution capabilities, successfully resolving multiple targets separated by merely 50% of the Rayleigh limit in range, 17% in velocity, and 52% in angle. At a signal-to-noise ratio of 20 dB, the algorithm achieves a multi-target detection probability exceeding 90% while concurrently delivering ultra-high accuracy, with root mean square error of 0.07 m, 0.024 m/s, and 0.015 degree for range, velocity, and angle, respectively. This robust performance, demonstrated against strong clutter, showcases its suitability for practical ISAC-LAWNs applications.

eess.SP↗

arXiv2025

How Particle-System Random Batch Methods Enhance Graph Transformer: Memory Efficiency and Parallel Computing Strategy

Attention mechanism is a significant part of Transformer models. It helps extract features from embedded vectors by adding global information and its expressivity has been proved to be powerful. Nevertheless, the quadratic complexity restricts its practicability. Although several researches have provided attention mechanism in sparse form, they are lack of theoretical analysis about the expressivity of their mechanism while reducing complexity. In this paper, we put forward Random Batch Attention (RBA), a linear self-attention mechanism, which has theoretical support of the ability to maintain its expressivity. Random Batch Attention has several significant strengths as follows: (1) Random Batch Attention has linear time complexity. Other than this, it can be implemented in parallel on a new dimension, which contributes to much memory saving. (2) Random Batch Attention mechanism can improve most of the existing models by replacing their attention mechanisms, even many previously improved attention mechanisms. (3) Random Batch Attention mechanism has theoretical explanation in convergence, as it comes from Random Batch Methods on computation mathematics. Experiments on large graphs have proved advantages mentioned above. Also, the theoretical modeling of self-attention mechanism is a new tool for future research on attention-mechanism analysis.

cs.LG↗

arXiv2025

Conditional Diffusion Model-Enabled Scenario-Specific Neural Receivers for Superimposed Pilot Schemes

Neural receivers have demonstrated strong performance in wireless communication systems. However, their effectiveness typically depends on access to large-scale, scenario-specific channel data for training, which is often difficult to obtain in practice. Recently, generative artificial intelligence (AI) models, particularly diffusion models (DMs), have emerged as effective tools for synthesizing high-dimensional data. This paper presents a scenario-specific channel generation method based on conditional DMs, which accurately model channel distributions conditioned on user location and velocity information. The generated synthetic channel data are then employed for data augmentation to improve the training of a neural receiver designed for superimposed pilot-based transmission. Experimental results show that the proposed method generates high-fidelity channel samples and significantly enhances neural receiver performance in the target scenarios, outperforming conventional data augmentation and generative adversarial network-based techniques.

cs.IT↗

arXiv2025

Discrete-Periodic Ambiguity Function of Random Communication Signals

This paper investigates the ambiguity function (AF) of communication signals carrying random data payloads, which is a fundamental metric characterizing sensing capability in ISAC systems. We first develop a unified analytical framework to evaluate the AF of communication-centric ISAC signals constructed from arbitrary orthonormal bases and independent identically distributed (i.i.d.) constellation symbols. Subsequently, we derive the discrete periodic ambiguity function (DP-AF) and provide closed-form expressions for its expected integrated sidelobe level (EISL) and average sidelobe level. Notably, we prove that the normalized EISL is invariant across all constellations and modulation bases. Finally, the theoretical findings are validated through simulations.

eess.SP↗

arXiv2025

Asymptotic uniform estimate of random batch method with replacement for the Cucker-Smale model

The Random Batch Method (RBM) [S. Jin, L. Li and J.-G. Liu, Random Batch Methods (RBM) for interacting particle systems, J. Comput. Phys. 400 (2020) 108877] is not only an efficient algorithm for simulating interacting particle systems, but also a randomly switching networked model for interacting particle system. This work investigates two RBM variants (RBM-r and RBM-1) applied to the Cucker-Smale flocking model. We establish the asymptotic emergence of global flocking and derive corresponding error estimates. By introducing a crucial auxiliary system and leveraging the intrinsic characteristics of the Cucker-Smale model, and under suitable conditions on the force, our estimates are uniform in both time and particle numbers. In the case of RBM-1, our estimates are sharper than those in Ha et al. (2021). Additionally, we provide numerical simulations to validate our analytical results.

math.NA↗

arXiv2025

Semantic Communications with World Models

Semantic communication is a promising technique for emerging wireless applications, which reduces transmission overhead by transmitting only task-relevant features instead of raw data. However, existing methods struggle under extremely low bandwidth and varying channel conditions, where corrupted or missing semantics lead to severe reconstruction errors. To resolve this difficulty, we propose a world foundation model (WFM)-aided semantic video transmission framework that leverages the predictive capability of WFMs to generate future frames based on the current frame and textual guidance. This design allows transmissions to be omitted when predictions remain reliable, thereby saving bandwidth. Through WFM's prediction, the key semantics are preserved, yet minor prediction errors tend to amplify over time. To mitigate issue, a lightweight depth-based feedback module is introduced to determine whether transmission of the current frame is needed. Apart from transmitting the entire frame, a segmentation-assisted partial transmission method is proposed to repair degraded frames, which can further balance performance and bandwidth cost. Furthermore, an active transmission strategy is developed for mobile scenarios by exploiting camera trajectory information and proactively scheduling transmissions before channel quality deteriorates. Simulation results show that the proposed framework significantly reduces transmission overhead while maintaining task performances across varying scenarios and channel conditions.

eess.IV↗

arXiv2025

Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Models

Channel estimation for massive multiple-input multiple-output (MIMO) systems is fundamentally constrained by excessive pilot overhead and high estimation latency. To overcome these obstacles, recent studies have leveraged deep generative networks to capture the prior distribution of wireless channels. In this paper, we propose a novel estimation framework that integrates an energy-based generative diffusion model (DM) with the Metropolis-Hastings (MH) principle. By reparameterizing the diffusion process with an incorporated energy function, the framework explicitly estimates the unnormalized log-prior, while MH corrections refine the sampling trajectory, mitigate deviations, and enhance robustness, ultimately enabling accurate posterior sampling for high-fidelity channel estimation. Numerical results reveal that the proposed approach significantly improves estimation accuracy compared with conventional parameterized DMs and other baseline methods, particularly in cases with limited pilot overhead.

cs.IT↗

arXiv2025

Low-Complexity MIMO Channel Estimation with Latent Diffusion Models

Deep generative models offer a powerful alternative to conventional channel estimation by learning the complex prior distribution of wireless channels. Capitalizing on this potential, this paper proposes a novel channel estimation algorithm based on latent diffusion models (LDMs), termed posterior sampling with latent diffusion for channel estimation (PSLD-CE). The core of our approach is a lightweight LDM architecture specifically designed for channel estimation, which serves as a powerful generative prior to capture the intricate channel distribution. Furthermore, we enhance the diffusion posterior sampling process by introducing an effective approximation for the likelihood term and a tailored self-consistency constraint on the variational autoencoder latent space. Extensive experimental results demonstrate that PSLD-CE consistently outperforms a wide range of existing methods. Notably, these significant performance gains are achieved while maintaining low computational complexity and fast inference speed, establishing our method as a highly promising and practical solution for next-generation wireless systems.

cs.IT↗

arXiv2025

Stacked Intelligent Metasurfaces for 6G Wireless Networks: Principles, Applications, and Research Directions

The sixth-generation (6G) wireless networks are expected to deliver ubiquitous connectivity, resilient coverage, and intelligence-driven services in highly dynamic environments. To achieve these goals, distributed wireless architectures such as cell-free massive multiple-input multiple-output (MIMO) have attracted significant attention due to their scalability and fairness. Recently, stacked intelligent metasurfaces (SIMs) have emerged as a promising evolution of reconfigurable intelligent surfaces, offering multi-layer electromagnetic domain processing with enhanced controllability and spatial degrees of freedom. By integrating SIMs into distributed wireless networks, advanced wave-domain operations can be realized, enabling efficient interference management, improved energy and spectral efficiency, and robust physical-layer security. This article provides a comprehensive overview of SIM-aided distributed wireless networks, including their application scenarios, classification, and system architectures. Key signal processing challenges, such as hierarchical frameworks, user association, and joint precoding, are discussed, followed by case studies demonstrating significant performance gains. Finally, future research directions in hardware design, energy consumption modeling, algorithm development, and artificial intelligence integration are highlighted, aiming to pave the way for scalable and intelligent 6G distributed wireless networks.

cs.IT↗

arXiv2025

Probabilistic Constellation Shaping for OFDM ISAC Signals Under Temporal-Frequency Filtering

Integrated sensing and communications (ISAC) is considered an innovative technology in sixth-generation (6G) wireless networks, where utilizing orthogonal frequency division multiplexing (OFDM) communication signals for sensing provides a cost-effective solution for implementing ISAC. However, the sensing performance of matched and mismatched filtering schemes can be significantly deteriorated due to the signaling randomness induced by finite-alphabet modulations with nonconstant modulus, such as quadrature amplitude modulation (QAM) constellations. Therefore, improving sensing performance without significantly compromising communication capability (i.e., maintaining randomness), remains a challenging task. To that end, we propose a unified probabilistic constellation shaping (PCS) framework that is compatible with both matched and mismatched filtering schemes, by maximizing the communication rate while imposing constraints on mean square error (MSE) of sensing channel state information (CSI), power, and probability distribution. Specifically, the MSE of sensing CSI is leveraged to optimize sensing capability, which is illustrated to be a more comprehensive metric compared to the output SNR after filtering (SNRout) and integrated sidelobes ratio (ISLR). Additionally, the internal relationships among these three sensing metrics are explicitly analyzed. Finally, both simulations and field measurements validate the efficiency of proposed PCS approach in achieving a flexible S&C trade-off, as well as its credibility in enhancing 6G wireless transmission in real-world scenarios.

eess.SP↗

arXiv2025

Quantum Random Feature Method for Solving Partial Differential Equations

Quantum computing holds significant promise for scientific computing due to its potential for polynomial to even exponential speedups over classical methods, which are often hindered by the curse of dimensionality. While neural networks present a mesh-free alternative to solve partial differential equations (PDEs), their accuracy is difficult to achieve since one needs to solve a high-dimensional non-convex optimization problem using the stochastic gradient descent method and its variants, the convergence of which is difficult to prove and cannot be guaranteed. The classical random feature method (RFM) effectively merges advantages from both classical numerical analysis and neural network based techniques, achieving spectral accuracy and a natural adaptability to complex geometries. In this work, we introduce a quantum random feature method (QRFM) that leverages quantum computing to accelerate the classical RFM framework. Our method constructs PDE solutions using quantum-generated random features and enforces the governing equations via a collocation approach. A complexity analysis demonstrates that this hybrid quantum-classical algorithm can achieve a quadratic speedup over the classical RFM.

quant-ph↗

arXiv2025

Pioneering Scalable Prototyping for Mid-Band XL-MIMO Systems: Design and Implementation

The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is emerging as a key enabler for future communication systems. Thanks to the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-MIMO prototype system are presented. Benefiting from the novel architecture, the proposed prototype system supports metrics aligned with standardization, including a bandwidth of 200 MHz, up to 1024 antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing (TDD) mode, the prototype enables multiuser communication with support for up to 12 users, while retaining standard communication procedures. Built on software-defined radio (SDR) platforms, the system is programmable and allows for flexible deployment of advanced algorithms. Moreover, the modular architecture ensures high scalability, making the system adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results with the proposed prototype system demonstrate real-time digital sample processing at 1167.85 Gbps, a peak data throughput of 15.81 Gbps for 12 users, and a maximal spectral efficiency approaching 80 bit/s/Hz.

eess.SP↗

arXiv2025

Next-Generation AI-Native Wireless Communications: MCMC-Based Receiver Architectures for Unified Processing

The multiple-input multiple-output (MIMO) receiver processing is a key technology for current and next-generation wireless communications. However, it faces significant challenges related to complexity and scalability as the number of antennas increases. Artificial intelligence (AI), a cornerstone of next-generation wireless networks, offers considerable potential for addressing these challenges. This paper proposes an AI-driven, universal MIMO receiver architecture based on Markov chain Monte Carlo (MCMC) techniques. Unlike existing AI-based methods that treat receiver processing as a black box, our MCMC-based approach functions as a generic Bayesian computing engine applicable to various processing tasks, including channel estimation, symbol detection, and channel decoding. This method enhances the interpretability, scalability, and flexibility of receivers in diverse scenarios. Furthermore, the proposed approach integrates these tasks into a unified probabilistic framework, thereby enabling overall performance optimization. This unified framework can also be seamlessly combined with data-driven learning methods to facilitate the development of fully intelligent communication receivers.

cs.IT↗

arXiv2025

Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network

Although symbol-level precoding (SLP) based on constructive interference (CI) exploitation offers performance gains, its high complexity remains a bottleneck. This paper addresses this challenge with an end-to-end deep learning (DL) framework with low inference complexity that leverages the structure of the optimal SLP solution in the closed-form and its inherent tensor equivariance (TE), where TE denotes that a permutation of the input induces the corresponding permutation of the output. Building upon the computationally efficient model-based formulations, as well as their known closed-form solutions, we analyze their relationship with linear precoding (LP) and investigate the corresponding optimality condition. We then construct a mapping from the problem formulation to the solution and prove its TE, based on which the designed networks reveal a specific parameter-sharing pattern that delivers low computational complexity and strong generalization. Leveraging these, we propose the backbone of the framework with an attention-based TE module, achieving linear computational complexity. Furthermore, we demonstrate that such a framework is also applicable to imperfect CSI scenarios, where we design a TE-based network to map the CSI, statistics, and symbols to auxiliary variables. Simulation results show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods and maintaining strong generalization across user numbers and symbol block lengths.

eess.SP↗