arXiv subjects

Chau Yuen

Publications and source records attributed to Chau Yuen.

At least 181 records · Page 10Linked to original sources

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

Electromagnetic Hybrid Beamforming for Holographic Communications

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.

cs.IT↗

arXiv2024

Exploring the Impact of RIS on Cooperative NOMA URLLC Systems: A Theoretical Perspective

In this paper, we conduct a theoretical analysis of how to integrate reconfigurable intelligent surfaces (RIS) with cooperative non-orthogonal multiple access (NOMA), considering URLLC. We consider a downlink two-user cooperative NOMA system employing short-packet communications, where the two users are denoted by the central user (CU) and the cell-edge user (CEU), respectively, and an RIS is deployed to enhance signal quality. Specifically, compared to CEU, CU lies nearer from BS and enjoys the higher channel gains. Closed-form expressions for the CU's average block error rate (BLER) are derived. Furthermore, we evaluate the CEU's BLER performance utilizing selective combining (SC) and derive a tight lower bound under maximum ratio combining (MRC). Simulation results are provided to our analyses and demonstrate that the RIS-assisted system significantly outperforms its counterpart without RIS in terms of BLER. Notably, MRC achieves a squared multiple of the diversity gain of the SC, leading to more reliable performance, especially for the CEU. Furthermore, by dividing the RIS into two zones, each dedicated to a specific user, the average BLER can be further reduced, particularly for the CEU.

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arXiv2024

Exploring Dual-Sniffer Passive Localization: Algorithm Design and Experimental Results

In this paper, we explore a dual-sniffer passive localization system that detects the timing difference of signals from both commercial base station (eNb) and user equipment (UE) to the sniffers. We design two localization schemes for UE localization: a time of arrival (ToA) based scheme and a time difference of arrival (TDoA) based scheme. In the ToA-based scheme, we derive two ellipse equations from measured arrival times at two sniffers, enabling direct numerical computation of the estimated position. For the TDoA-based scheme, we relocate one sniffer to a different position to obtain two sets of TDoA measurements, resulting in hyperbola equations. We then apply a least squares (LS) algorithm to analytically estimate the UE's position. Simulation results validate the effectiveness of the proposed TDoA-based scheme, demonstrating improved accuracy in UE positioning.We build a platform based on the considered localization system and conduct real-world experiments. The experimental results confirm the accuracy and practicality of the TDoA-based dual-sniffer localization scheme, demonstrating improved precision in passive localization.

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arXiv2024

A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices.

cs.DC↗

arXiv2024

Robust Gaussian Splatting SLAM by Leveraging Loop Closure

3D Gaussian Splatting algorithms excel in novel view rendering applications and have been adapted to extend the capabilities of traditional SLAM systems. However, current Gaussian Splatting SLAM methods, designed mainly for hand-held RGB or RGB-D sensors, struggle with tracking drifts when used with rotating RGB-D camera setups. In this paper, we propose a robust Gaussian Splatting SLAM architecture that utilizes inputs from rotating multiple RGB-D cameras to achieve accurate localization and photorealistic rendering performance. The carefully designed Gaussian Splatting Loop Closure module effectively addresses the issue of accumulated tracking and mapping errors found in conventional Gaussian Splatting SLAM systems. First, each Gaussian is associated with an anchor frame and categorized as historical or novel based on its timestamp. By rendering different types of Gaussians at the same viewpoint, the proposed loop detection strategy considers both co-visibility relationships and distinct rendering outcomes. Furthermore, a loop closure optimization approach is proposed to remove camera pose drift and maintain the high quality of 3D Gaussian models. The approach uses a lightweight pose graph optimization algorithm to correct pose drift and updates Gaussians based on the optimized poses. Additionally, a bundle adjustment scheme further refines camera poses using photometric and geometric constraints, ultimately enhancing the global consistency of scenarios. Quantitative and qualitative evaluations on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art methods in camera pose estimation and novel view rendering tasks. The code will be open-sourced for the community.

cs.RO↗

arXiv2024

Computation Pre-Offloading for MEC-Enabled Vehicular Networks via Trajectory Prediction

Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility of the vehicles and the limited computational resources for performing task offloading in near-real-time, such schemes may require high latency, thus, become even infeasible. To address this issue, in this paper, we present a Trajectory Prediction-based Pre-offloading Decision (TPPD) algorithm for analyzing the historical trajectories of vehicles to predict their future coordinates, thereby allowing for computational resource allocation in advance. We first utilize the Long Short-Term Memory (LSTM) network model to predict each vehicle's movement trajectory. Then, based on the task requirements and the predicted trajectories, we devise a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) that enables the edge server to minimize task processing delay, while ensuring effective utilization of the available computational resources. Our simulation results verify the effectiveness of the proposed approach, showcasing that, as compared with traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.

cs.NI↗

arXiv2024

RIS-aided Trajectory Optimization in Layered Urban Air Mobility

Urban Air Mobility (UAM) relies on developing aerospace industries, where safe aviation and efficient communication are critical features of aircraft. However, it is challenging for aircraft to sustain efficient air-ground communication in urban circumstances. Without continuous air-ground communication, aircraft may experience course deviation and safety accidents. To address these problems, a reconfigurable intelligent surface(RIS)-aided trajectory optimization scheme is proposed enabling efficient air-ground communication and safe aviation in UAM with a layered airspace structure. This paper first devises a dual-plane RIS communication scheme for layered airspace. It fully engages the omnidirectional and directional signal attributes to reduce the transmission delay of the air-ground communication. Based on the dual-plane RIS configuration, we jointly develop the intra- and inter-layer trajectory scheme to optimize communication and safe aviation. In the intra-layer trajectory optimization, we propose a dual-time-scale flight scheme to improve communication capacity and horizontal flight safety. Meanwhile, we propose a safe layer-switching method to ensure collision avoidance during vertical flight in the inter-layer trajectory optimization. The communication load of the proposed scheme can be improved 40% and the time of safe separation restoration can be lessened 66% compared with the benchmarks in the layered airspace.

cs.CE↗

arXiv2024

MIMO Precoding Exploiting Extra Degrees of Freedom (DoF) in the Wavenumber Domain

In this paper, we propose an emerging wavenumber-domain precoding scheme to break the limitations of rank-1 channels that merely supports single-stream transmission, enabling simultaneous transmission of multiple data streams. The proposed wavenumber-domain precoding scheme also breaks the Rayleigh distance demarcation, regardless of the far-field and near-field contexts. Specifically, by characterizing the channel response as the superposition of a series of Fourier harmonics specified by different wavenumbers, the degree of freedom (DoF) is dependent on the cardinality of the wavenumber support, based on which the extra DoF is presented. This representation is applicable for both far-field and near-field. Different wavenumber atoms, determined within this support, constitute the codebook for MIMO precoding, in which each atom allows for the transmission of a data stream. Then, to maximize the capacity, it is required to select the wavenumbers associated with the optimal transmission direction, and optimize its power allocation. Finally, our simulation results demonstrate the significant superiority in comparison to the conventional spatial division schemes, with the potential of approaching the theoretical performance upper bound achieved by singular value decomposition (SVD).

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arXiv2024

Harnessing Stacked Intelligent Metasurface for Enhanced Cell-Free Massive MIMO Systems: A Low-Power and Cost Approach

In this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into cell-free (CF) massive multiple-input multiple-output (mMIMO) systems to enhance access point (AP) capabilities and address high power consumption and cost challenges. Specifically, we investigate the uplink performance of a SIM-enhanced CF mMIMO system and propose a novel system framework. First, the closed-form expressions of the spectral efficiency (SE) are obtained using the unique two-layer signal processing framework of CF mMIMO systems. Second, to mitigate inter-user interference, an interference-based greedy algorithm for pilot allocation is introduced. Third, a wave-based beamforming algorithm for SIM is proposed, based only on statistical channel state information, which effectively reduces the fronthaul costs. Finally, a max-min SE power control algorithm is proposed to improve the performance of UE with inferior channel conditions. The results indicate that increasing the number of SIM layers and meta-atoms leads to significant performance improvements and allows for a reduction in the number of APs and AP antennas, thus lowering the costs. In particular, the best SE performance is achieved with the deployment of 20 APs plus 1200 SIM meta-atoms. Finally, the proposed wave-based beamforming algorithm can enhance the SE performance of SIM-enhanced CF-mMIMO systems by 57\%, significantly outperforming traditional CF mMIMO systems.

cs.IT↗

arXiv2024

Joint AP-UE Association and Precoding for SIM-Aided Cell-Free Massive MIMO Systems

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems are emerging as promising alternatives to cellular networks, especially in ultra-dense environments. However, further capacity enhancement requires the deployment of more access points (APs), which will lead to high costs and high energy consumption. To address this issue, in this paper, we explore the integration of low-power, low-cost stacked intelligent metasurfaces (SIM) into CF mMIMO systems to enhance AP capabilities. The key point is that SIM performs precoding-related matrix operations in the wave domain. As a consequence, each AP antenna only needs to transmit data streams for a single user equipment (UE), eliminating the need for complex baseband digital precoding. Then, we formulate the problem of joint AP-UE association and precoding at APs and SIMs to maximize the system sum rate. Due to the non-convexity and high complexity of the formulated problem, we propose a two-stage signal processing framework to solve it. In particular, in the first stage, we propose an AP antenna greedy association (AGA) algorithm to minimize UE interference. In the second stage, we introduce an alternating optimization (AO)-based algorithm that separates the joint power and wave-based precoding optimization problem into two distinct sub-problems: the complex quadratic transform method is used for AP antenna power control, and the projection gradient ascent (PGA) algorithm is employed to find suboptimal solutions for the SIM wave-based precoding. Finally, the numerical results validate the effectiveness of the proposed framework and assess the performance enhancement achieved by the algorithm in comparison to various benchmark schemes. The results show that, with the same number of SIM meta-atoms, the proposed algorithm improves the sum rate by approximately 275% compared to the benchmark scheme.

cs.IT↗

arXiv2024

On Performance of Distributed RIS-aided Communication in Random Networks

This paper evaluates the geometrically averaged performance of a wireless communication network assisted by a multitude of distributed reconfigurable intelligent surfaces (RISs), where the RIS locations are randomly dropped obeying a homogeneous Poisson point process. By exploiting stochastic geometry and then averaging over the random locations of RISs as well as the serving user, we first derive a closed-form expression for the spatially ergodic rate in the presence of phase errors at the RISs in practice. Armed with this closed-form characterization, we then optimize the RIS deployment under a reasonable and fair constraint of a total number of RIS elements per unit area. The optimal configurations in terms of key network parameters, including the RIS deployment density and the array sizes of RISs, are disclosed for the spatially ergodic rate maximization. Our findings suggest that deploying larger-size RISs with reduced deployment density is theoretically preferred to support extended RIS coverages, under the cases of bounded phase shift errors. However, when dealing with random phase shifts, the reflecting elements are recommended to spread out as much as possible, disregarding the deployment cost. Furthermore, the spatially ergodic rate loss due to the phase shift errors is quantitatively characterized. For bounded phase shift errors, the rate loss is eventually upper bounded by a constant as $N\rightarrow\infty$, where $N$ is the number of reflecting elements at each RIS. While for random phase shifts, this rate loss scales up in the order of $\log N$. These analytical observations are validated through numerical results.

cs.IT↗

arXiv2024

Wavenumber-Domain Near-Field Channel Estimation: Beyond the Fresnel Bound

In the near-field context, the Fresnel approximation is typically employed to mathematically represent solvable functions of spherical waves. However, these efforts may fail to take into account the significant increase in the lower limit of the Fresnel approximation, known as the Fresnel distance. The lower bound of the Fresnel approximation imposes a constraint that becomes more pronounced as the array size grows. Beyond this constraint, the validity of the Fresnel approximation is broken. As a potential solution, the wavenumber-domain paradigm characterizes the spherical wave using a spectrum composed of a series of linear orthogonal bases. However, this approach falls short of covering the effects of the array geometry, especially when using Gaussian-mixed-model (GMM)-based von Mises-Fisher distributions to approximate all spectra. To fill this gap, this paper introduces a novel wavenumber-domain ellipse fitting (WDEF) method to tackle these challenges. Particularly, the channel is accurately estimated in the near-field region, by maximizing the closed-form likelihood function of the wavenumber-domain spectrum conditioned on the scatterers' geometric parameters. Simulation results are provided to demonstrate the robustness of the proposed scheme against both the distance and angles of arrival.

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arXiv2024

Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System

Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coordinates from channel information within pre-set rectangular areas, often face challenges due to the heterogeneous distribution of fingerprints inherent in non-line-of-sight (NLOS) scenarios, particularly within orthogonal frequency division multiplexing systems. To overcome this limitation, we develop a novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results reveal that AMDNLoc achieves an impressive NLOS localization accuracy of 1.46 meters on typical wireless artificial intelligence research datasets and demonstrates significant improvements in interpretability, adaptability, and scalability.

cs.IT↗

arXiv2024

Exploring Hannan Limitation for 3D Antenna Array

Hannan Limitation successfully links the directivity characteristics of 2D arrays with the aperture gain limit, providing the radiation efficiency upper limit for large 2D planar antenna arrays. This demonstrates the inevitable radiation efficiency degradation caused by mutual coupling effects between array elements. However, this limitation is derived based on the assumption of infinitely large 2D arrays, which means that it is not an accurate law for small-size arrays. In this paper, we extend this theory and propose an estimation formula for the radiation efficiency upper limit of finite-sized 2D arrays. Furthermore, we analyze a 3D array structure consisting of two parallel 2D arrays. Specifically, we provide evaluation formulas for the mutual coupling strengths for both infinite and finite size arrays and derive the fundamental efficiency limit of 3D arrays. Moreover, based on the established gain limit of antenna arrays with fixed aperture sizes, we derive the achievable gain limit of finite size 3D arrays. Besides the performance analyses, we also investigate the spatial radiation characteristics of the considered 3D array structure, offering a feasible region for 2D phase settings under a given energy attenuation threshold. Through simulations, we demonstrate the effectiveness of our proposed theories and gain advantages of 3D arrays for better spatial coverage under various scenarios.

cs.IT↗

arXiv2024

Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve

Data-driven methods have gained extensive attention in estimating the state of health (SOH) of lithium-ion batteries. Accurate SOH estimation requires degradation-relevant features and alignment of statistical distributions between training and testing datasets. However, current research often overlooks these needs and relies on arbitrary voltage segment selection. To address these challenges, this paper introduces an innovative approach leveraging spatio-temporal degradation dynamics via graph convolutional networks (GCNs). Our method systematically selects discharge voltage segments using the Matrix Profile anomaly detection algorithm, eliminating the need for manual selection and preventing information loss. These selected segments form a fundamental structure integrated into the GCN-based SOH estimation model, capturing inter-cycle dynamics and mitigating statistical distribution incongruities between offline training and online testing data. Validation with a widely accepted open-source dataset demonstrates that our method achieves precise SOH estimation, with a root mean squared error of less than 1%.

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arXiv2024

Robust Continuous-Time Beam Tracking with Liquid Neural Network

Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum at high frequencies. However, the huge overhead associated with beam training imposes a significant challenge in mmWave communications, particularly in urban environments with high background noise. To reduce this high overhead, we propose a novel solution for robust continuous-time beam tracking with liquid neural network, which dynamically adjust the narrow mmWave beams to ensure real-time beam alignment with mobile users. Through extensive simulations, we validate the effectiveness of our proposed method and demonstrate its superiority over existing state-of-the-art deep-learning-based approaches. Specifically, our scheme achieves at most 46.9% higher normalized spectral efficiency than the baselines when the user is moving at 5 m/s, demonstrating the potential of liquid neural networks to enhance mmWave mobile communication performance.

cs.IT↗

arXiv2024

FAS-RIS Communication: Model, Analysis, and Optimization

This correspondence investigates the novel fluid antenna system (FAS) technology, combining with reconfigurable intelligent surface (RIS) for wireless communications, where a base station (BS) communicates with a FAS-enabled user with the assistance of a RIS. To analyze this technology, we derive the outage probability based on the block-diagonal matrix approximation (BDMA) model. With this, we obtain the upper bound, lower bound, and asymptotic approximation of the outage probability to gain more insights. Moreover, we design the phase shift matrix of the RIS in order to minimize the system outage probability. Simulation results confirm the accuracy of our approximations and that the proposed schemes outperform benchmarks significantly.

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