arXiv subjects

Shi Jin

Publications and source records attributed to Shi Jin.

At least 253 records · Page 14Linked to original sources

arXiv2024

Quantum Circuits for partial differential equations via Schrödingerisation

Quantum computing has emerged as a promising avenue for achieving significant speedup, particularly in large-scale PDE simulations, compared to classical computing. One of the main quantum approaches involves utilizing Hamiltonian simulation, which is directly applicable only to Schrödinger-type equations. To address this limitation, Schrödingerisation techniques have been developed, employing the warped transformation to convert general linear PDEs into Schrödinger-type equations. However, despite the development of Schrödingerisation techniques, the explicit implementation of the corresponding quantum circuit for solving general PDEs remains to be designed. In this paper, we present detailed implementation of a quantum algorithm for general PDEs using Schrödingerisation techniques. We provide examples of the heat equation, and the advection equation approximated by the upwind scheme, to demonstrate the effectiveness of our approach. Complexity analysis is also carried out to demonstrate the quantum advantages of these algorithms in high dimensions over their classical counterparts.

quant-ph↗

arXiv2024

Reducing Channel Estimation and Feedback Overhead in IRS-Aided Downlink System: A Quantize-then-Estimate Approach

Channel state information (CSI) acquisition is essential for the base station (BS) to fully reap the beamforming gain in intelligent reflecting surface (IRS)-aided downlink communication systems. Recently, [1] revealed a strong correlation in different users' cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property to reduce the overhead for both pilot and feedback transmission in IRS-aided downlink communication. Note that in the downlink, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users' channels, which is in sharp contrast to the uplink counterpart considered in [1]. To tackle this challenge, this paper proposes a novel ``quantize-then-estimate'' protocol in frequency division duplex (FDD) IRS-aided downlink communication. Specifically, the users quantize and feed back their received pilot signals, instead of the estimated channels, to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging their correlation, similar to the uplink scenario. Under this protocol, we manage to propose efficient user-side quantization and BS-side channel estimation methods. Moreover, we analytically quantify the pilot and feedback transmission overhead to reveal the significant performance gain of our proposed scheme over the conventional ``estimate-then-quantize'' scheme.

eess.SP↗

arXiv2024

Integrated Heterogeneous Service Provisioning: Unifying Beyond-Communication Capabilities with MDMA in 6G and Future Wireless Networks

The rapid evolution and convergence of wireless technologies and vertical applications have fundamentally reshaped our lifestyles and industries. Future wireless networks, especially 6G, are poised to support a wide range of applications enabled by heterogeneous services, leveraging both traditional connectivity-centric functions and emerging beyond-communication capabilities, particularly localization, sensing, and synchronization. However, integrating these new capabilities into a unified 6G paradigm presents significant challenges. This article provides an in-depth analysis of these technical challenges for integrative 6G design and proposes three strategies for concurrent heterogeneous service provisioning, with the aggregated goal of maximizing integration gains while minimizing service provisioning overhead. First, we adopt multi-dimensional multiple access (MDMA) as an inclusive enabling platform to flexibly integrate various capabilities by shared access to multi-dimensional radio resources. Next, we propose value-oriented heterogeneous service provisioning to maximize the integration gain through situation-aware MDMA. To enhance scalability, we optimize control and user planes by eliminating redundant control information and enabling service-oriented prioritization. Finally, we evaluate the proposed framework with a case study on integrated synchronization and communication, demonstrating its potential for concurrent heterogeneous service provisioning.

cs.NI↗

arXiv2024

Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach

In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense UAV communication network, where the GUs' service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel UAVs as a stochastic game, where each UAV jointly optimizes its trajectory, user association, and downlink power control to maximize the expectation of its locally cumulative energy efficiency under the interference and energy constraints. To cope with the scalability issue in a large-scale network, we further formulate the problem as a mean-field game (MFG), which simplifies the interactions among the UAVs into a two-player game between a representative UAV and a mean-field. We prove the existence and uniqueness of the equilibrium for the MFG, and propose a model-free mean-field reinforcement learning algorithm named maximum entropy mean-field deep Q network (ME-MFDQN) to solve the mean-field equilibrium in both fully and partially observable scenarios. The simulation results reveal that the proposed algorithm improves the energy efficiency compared with the benchmark algorithms. Moreover, the performance can be further enhanced if the GUs' service demands exhibit higher temporal correlation or if the UAVs have wider observation capabilities over their nearby GUs.

eess.SY↗

arXiv2024

Channel Customization for Low-Complexity CSI Acquisition in Multi-RIS-Assisted MIMO Systems

The deployment of multiple reconfigurable intelligent surfaces (RISs) enhances the propagation environment by improving channel quality, but it also complicates channel estimation. Following the conventional wireless communication system design, which involves full channel state information (CSI) acquisition followed by RIS configuration, can reduce transmission efficiency due to substantial pilot overhead and computational complexity. This study introduces an innovative approach that integrates CSI acquisition and RIS configuration, leveraging the channel-altering capabilities of the RIS to reduce both the overhead and complexity of CSI acquisition. The focus is on multi-RIS-assisted systems, featuring both direct and reflected propagation paths. By applying a fast-varying reflection sequence during RIS configuration for channel training, the complex problem of channel estimation is decomposed into simpler, independent tasks. These fast-varying reflections effectively isolate transmit signals from different paths, streamlining the CSI acquisition process for both uplink and downlink communications with reduced complexity. In uplink scenarios, a positioning-based algorithm derives partial CSI, informing the adjustment of RIS parameters to create a sparse reflection channel, enabling precise reconstruction of the uplink channel. Downlink communication benefits from this strategically tailored reflection channel, allowing effective CSI acquisition with fewer pilot signals. Simulation results highlight the proposed methodology's ability to accurately reconstruct the reflection channel with minimal impact on the normalized mean square error while simultaneously enhancing spectral efficiency.

cs.IT↗

arXiv2024

Schrödingerization based Quantum Circuits for Maxwell's Equation with time-dependent source terms

The Schrödingerisation method combined with the autonomozation technique in \cite{cjL23} converts general non-autonomous linear differential equations with non-unitary dynamics into systems of autonomous Schrödinger-type equations, via the so-called warped phase transformation that maps the equation into two higher dimension. Despite the success of Schrödingerisation techniques, they typically require the black box of the sparse Hamiltonian simulation, suitable for continuous-variable based analog quantum simulation. For qubit-based general quantum computing one needs to design the quantum circuits for practical implementation. This paper explicitly constructs a quantum circuit for Maxwell's equations with perfect electric conductor (PEC) boundary conditions and time-dependent source terms, based on Schrödingerization and autonomozation, with corresponding computational complexity analysis. Through initial value smoothing and high-order approximation to the delta function, the increase in qubits from the extra dimensions only requires minor rise in computational complexity, almost $\log\log {1/\varepsilon}$ where $\varepsilon$ is the desired precision. Our analysis demonstrates that quantum algorithms constructed using Schrödingerisation exhibit polynomial acceleration in computational complexity compared to the classical Finite Difference Time Domain (FDTD) format.

quant-ph↗

arXiv2024

Intelligent Adaptive Metasurface in Complex Wireless Environments

The programmable metasurface is regarded as one of the most promising transformative technologies for next-generation wireless system applications. Due to the lack of effective perception ability of the external electromagnetic environment, there are numerous challenges in the intelligent regulation of wireless channels, and it still relies on external sensors to reshape electromagnetic environment as desired. To address that problem, we propose an adaptive metasurface (AMS) which integrates the capabilities of acquiring wireless environment information and manipulating reflected electromagnetic (EM) waves in a programmable manner. The proposed design endows the metasurfaces with excellent capabilities to sense the complex electromagnetic field distributions around them and then dynamically manipulate the waves and signals in real time under the guidance of the sensed information, eliminating the need for prior knowledge or external inputs about the wireless environment. For verification, a prototype of the proposed AMS is constructed, and its dual capabilities of sensing and manipulation are experimentally validated. Additionally, different integrated sensing and communication (ISAC) scenarios with and without the aid of the AMS are established. The effectiveness of the AMS in enhancing communication quality is well demonstrated in complex electromagnetic environments, highlighting its beneficial application potential in future wireless systems.

physics.app-ph↗

arXiv2024

Schrödingerisation based computationally stable algorithms for ill-posed problems in partial differential equations

We introduce a simple and stable computational method for ill-posed partial differential equation (PDE) problems. The method is based on Schrödingerization, introduced in [S. Jin, N. Liu and Y. Yu, arXiv:2212.13969][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603], which maps all linear PDEs into Schrödinger-type equations in one higher dimension, for quantum simulations of these PDEs. Although the original problem is ill-posed, the Schrödingerized equations are Hamiltonian systems and time-reversible, allowing stable computation both forward and backward in time. The original variable can be recovered by data from suitably chosen domain in the extended dimension. We will use the backward heat equation and the linear convection equation with imaginary wave speed as examples. Error analysis of these algorithms are conducted and verified numerically. The methods are applicable to both classical and quantum computers, and we also lay out quantum algorithms for these methods. Moreover, we introduce a smooth initialization for the Schrödingerized equation which will lead to essentially spectral accuracy for the approximation in the extended space, if a spectral method is used. Consequently, the extra qubits needed due to the extra dimension, if a qubit based quantum algorithm is used, for both well-posed and ill-posed problems, becomes almost $\log\log {1/\varepsilon}$ where $\varepsilon$ is the desired precision. This optimizes the complexity of the Schrödingerization based quantum algorithms for any non-unitary dynamical system introduced in [S. Jin, N. Liu and Y. Yu, arXiv:2212.13969][S. Jin, N. Liu and Y. Yu, Phys. Rev. A, 108 (2023), 032603].

math.NA↗

arXiv2024

ISAC Prototype System for Multi-Domain Cooperative Communication Networks

Future wireless networks are poised to transform into integrated sensing and communication (ISAC) networks, unlocking groundbreaking services such as digital twinning. To harness the full potential of ISAC networks, it is essential to experimentally validate their sensing capabilities and the role of sensing in boosting communication. However, current prototype systems fall short in supporting multiple sensing functions or validating sensing-assisted communication. In response, we have developed an advanced ISAC prototype system that incorporates monostatic, bistatic, and network sensing modes. This system supports multimodal data collection and synchronization, ensuring comprehensive experimental validation. On the communication front, it excels in sensing-aided beam tracking and real-time high-definition video transmission. For sensing applications, it provides precise angle and range measurements, real-time angle-range imaging, and radio-based simultaneous localization and mapping (SLAM). Our prototype aligns with the 5G New Radio standard, offering scalability for up to 16 user equipments (UEs) in uplink transmission and 10 UEs in downlink transmission. Real-world tests showcase the system's superior accuracy, with root mean square errors of 2.3 degrees for angle estimation and 0.3 meters (m) for range estimation. Additionally, the estimation errors for multimodal-aided real-time radio SLAM localization and mapping are 0.25 m and 0.8 m, respectively.

cs.IT↗

arXiv2024

Machine Learning-Based Direct Source Localization for Passive Movement-Driven Virtual Large Array

This paper introduces a novel smartphone-enabled localization technology for ambient Internet of Things (IoT) devices, leveraging the widespread use of smartphones. By utilizing the passive movement of a smartphone, we create a virtual large array that enables direct localization using only angle-of-arrival (AoA) information. Unlike traditional two-step localization methods, direct localization is unaffected by AoA estimation errors in the initial step, which are often caused by multipath channels and noise. However, direct localization methods typically require prior environmental knowledge to define the search space, with calculation time increasing as the search space expands. To address limitations in current direct localization methods, we propose a machine learning (ML)-based direct localization technique. This technique combines ML with an adaptive matching pursuit procedure, dynamically generating search spaces for precise source localization. The adaptive matching pursuit minimizes location errors despite potential accuracy fluctuations in ML across various training and testing environments. Additionally, by estimating the reflection source's location, we reduce the effects of multipath channels, enhancing localization accuracy. Extensive three-dimensional ray-tracing simulations demonstrate that our proposed method outperforms current state-of-the-art direct localization techniques in computational efficiency and operates independently of prior environmental knowledge.

cs.IT↗

arXiv2024

Joint User Scheduling and Precoding for RIS-Aided MU-MISO Systems: A MADRL Approach

With the increasing demand for spectrum efficiency and energy efficiency, reconfigurable intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability of controlling wireless environment. However, there is still a lack of treatments to deal with the growth of the number of users and RIS elements, which may incur performance degradation or computational complexity explosion. In this paper, we investigate the joint optimization of user scheduling and precoding for distributed RIS-aided communication systems. Firstly, we propose an optimization-based numerical method to obtain suboptimal solutions with the aid of the approximation of ergodic sum rate. Secondly, to reduce the computational complexity caused by the high dimensionality, we propose a data-driven scalable and generalizable multi-agent deep reinforcement learning (MADRL) framework with the aim to maximize the ergodic sum rate approximation through the cooperation of all agents. Further, we propose a novel dynamic working process exploiting the trained MADRL algorithm, which enables distributed RISs to configure their own passive precoding independently. Simulation results show that our algorithm substantially reduces the computational complexity by a time reduction of three orders of magnitude at the cost of 3% performance degradation, compared with the optimization-based method, and achieves 6% performance improvement over the state-of-the-art MADRL algorithms.

cs.IT↗

arXiv2024

Position-Aided Semantic Communication for Efficient Image Transmission: Design, Implementation, and Experimental Results

Semantic communication, augmented by knowledge bases (KBs), offers substantial reductions in transmission overhead and resilience to errors. However, existing methods predominantly rely on end-to-end training to construct KBs, often failing to fully capitalize on the rich information available at communication devices. Motivated by the growing convergence of sensing and communication, we introduce a novel Position-Aided Semantic Communication (PASC) framework, which integrates localization into semantic transmission. This framework is particularly designed for position-based image communication, such as real-time uploading of outdoor camera-view images. By utilizing the position, the framework retrieves corresponding maps, and then an advanced foundation model (FM)-driven view generator is employed to synthesize images closely resembling the target images. The PASC framework further leverages the FM to fuse the synthesized image with deviations from the real one, enhancing semantic reconstruction. Notably, the framework is highly flexible, capable of adapting to dynamic content and fluctuating channel conditions through a novel FM-based parameter optimization strategy. Additionally, the challenges of real-time deployment are addressed, with the development of a hardware testbed to validate the framework. Simulations and real-world tests demonstrate that the proposed PASC approach not only significantly boosts transmission efficiency, but also remains robust in diverse and evolving transmission scenarios.

eess.IV↗

arXiv2024

Structured Connectivity for 6G Reflex Arc: Task-Oriented Virtual User and New Uplink-Downlink Tradeoff

To accommodate the evolving demands of unmanned operations, the future sixth-generation (6G) network will support not only communication links but also sensing-communication-computing-control ($\mathbf{SC}^3$) loops. In each $\mathbf{SC}^3$ cycle, the sensor uploads sensing data to the computing center, and the computing center calculates the control command and sends it to the actuator to take action. To maintain the task-level connections between the sensor-computing center link and the computing center-actuator link, we propose to treat the sensor and actuator as a virtual user. In this way, the two communication links of the $\mathbf{SC}^3$ loop become the uplink and downlink (UL&DL) of the virtual user. Based on the virtual user, we propose a task-oriented UL&DL optimization scheme. This scheme jointly optimizes UL&DL transmit power, time, bandwidth, and CPU frequency to minimize the control linear quadratic regulator (LQR) cost. We decouple the complex problem into a convex UL&DL bandwidth allocation problem with the closed-form solution for the optimal time allocation. Simulation results demonstrate that the proposed scheme achieves a task-level balance between the UL&DL, surpassing conventional communication schemes that optimize each link separately.

eess.SY↗

arXiv2024

Adaptive Wireless Image Semantic Transmission: Design, Simulation, and Prototype Validation

The rapid development of artificial intelligence has significantly advanced semantic communications, particularly in wireless image transmission. However, most existing approaches struggle to precisely distinguish and prioritize image content, and they do not sufficiently incorporate semantic priorities into system design. In this study, we propose an adaptive wireless image semantic transmission scheme called ASCViT-JSCC, which utilizes vision transformer-based joint source-channel coding (JSCC). This scheme prioritizes different image regions based on their importance, identified through object and feature point detection. Unimportant background sections are masked, enabling them to be recovered at the receiver, while the freed resources are allocated to enhance object protection via the JSCC network. We also integrate quantization modules to enable compatibility with quadrature amplitude modulation, commonly used in modern wireless communications. To address frequency-selective fading channels, we introduce CSIPA-Net, which allocates power based on channel information, further improving performance. Notably, we conduct over-the-air testing on a prototype platform composed of a software-defined radio and embedded graphics processing unit systems, validating our methods. Both simulations and real-world measurements demonstrate that ASCViT-JSCC effectively prioritizes object protection according to channel conditions, significantly enhancing image reconstruction quality, especially in challenging channel environments.

eess.IV↗

arXiv2024

Electromagnetic Property Sensing Based on Diffusion Model in ISAC System

Integrated sensing and communications (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area. Specifically, we first estimate the sensing channel by using both the communications and the sensing signals echoed back from the target. Then we employ the diffusion model to generate the point cloud that represents the target and thus enables 3D visualization of the target's EM property distribution. In order to minimize the mean Chamfer distance (MCD) between the ground truth and the estimated point clouds, we further design the communications and sensing beamforming matrices under the constraint of a maximum transmit power and a minimum communications achievable rate for each user equipment (UE). Simulation results demonstrate the efficacy of the proposed method in achieving high-quality reconstruction of the target's shape, relative permittivity, and conductivity. Besides, the proposed method can sense the EM property of the target effectively in any position of the sensing area.

eess.SP↗

arXiv2024

Decision Transformers for Wireless Communications: A New Paradigm of Resource Management

As the next generation of mobile systems evolves, artificial intelligence (AI) is expected to deeply integrate with wireless communications for resource management in variable environments. In particular, deep reinforcement learning (DRL) is an important tool for addressing stochastic optimization issues of resource allocation. However, DRL has to start each new training process from the beginning once the state and action spaces change, causing low sample efficiency and poor generalization ability. Moreover, each DRL training process may take a large number of epochs to converge, which is unacceptable for time-sensitive scenarios. In this paper, we adopt an alternative AI technology, namely, Decision Transformer (DT), and propose a DT-based adaptive decision architecture for wireless resource management. This architecture innovates through constructing pre-trained models in the cloud and then fine-tuning personalized models at the edges. By leveraging the power of DT models learned over offline datasets, the proposed architecture is expected to achieve rapid convergence with many fewer training epochs and higher performance in new scenarios with different state and action spaces, compared with DRL. We then design DT frameworks for two typical communication scenarios: intelligent reflecting surfaces-aided communications and unmanned aerial vehicle-aided mobile edge computing. Simulations demonstrate that the proposed DT frameworks achieve over $3$-$6$ times speedup in convergence and better performance relative to the classic DRL method, namely, proximal policy optimization.

eess.SP↗

arXiv2024

Group Movable Antenna With Flexible Sparsity: Joint Array Position and Sparsity Optimization

Movable antenna (MA) is a promising technology to exploit the spatial variation of wireless channel for performance enhancement, by dynamically varying the antenna position within a certain region. However, for multi-antenna communication systems, moving each antenna independently not only requires prohibitive complexity to find the optimal antenna positions, but also incurs sophisticated movement control in practice. To address this issue, this letter proposes a new MA architecture termed group MA (GMA), enabling the group movement of all elements collectively in a continuous manner, and simultaneously achieving flexible array architecture by antenna selection (AS). In this letter, we focus on the uniform sparse array based GMA, where equally spaced antenna elements are selected to achieve desired array sparsity. The array position and sparsity level are jointly optimized to maximize the sum rate of the multi-user communication system. Numerical results verify the necessity to optimize the position and sparsity of GMA, and considerable performance gain is achieved as compared to the conventional fixed-position antenna (FPA).

cs.IT↗

arXiv2024

Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals to sense the electromagnetic (EM) property of the target and thus identify the materials of the target. Specifically, we first establish an EM wave propagation model with Maxwell equations, where the EM property of the target is captured by a closed-form expression of the channel. We then build the mathematical model for the relative permittivity and conductivity distribution (RPCD) within a predetermined region of interest shared by multiple base stations (BSs). Based on the EM wave propagation model, we propose an EM property sensing method, in which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector. We then develop a fusion algorithm to combine data from multiple BSs, which can enhance the reconstruction accuracy of EM property by efficiently integrating diverse measurements. Moreover, the fusion is performed at the feature level of RPCD and features low transmission overhead. We further design the pilot signals that can minimize the mutual coherence of the equivalent channels and enhance the diversity of incident EM wave patterns. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification.

eess.SP↗