arXiv subjects

Vu Nguyen Ha

Publications and source records attributed to Vu Nguyen Ha.

At least 37 records · Page 2Linked to original sources

arXiv2024

Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image Classification

This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constrained settings. Utilizing EfficientViT and MobileViT as teacher models, this framework enables lightweight student models, particularly ResNet8 and ResNet16, to surpass 90% in accuracy, precision, and recall, adhering to the stringent confidence thresholds necessary for reliable classification tasks. Unlike conventional KD methods that rely on static weight distribution, our adaptive weighting mechanism responds to each teacher model's confidence, allowing student models to prioritize more credible sources of knowledge dynamically. Remarkably, ResNet8 delivers substantial efficiency gains, achieving a 97.5% reduction in parameters, a 96.7% decrease in FLOPs, an 86.2% cut in power consumption, and a 63.5% increase in inference speed over MobileViT. This significant optimization of complexity and resource demands establishes ResNet8 as an optimal candidate for EO tasks, combining robust performance with feasibility in deployment. The confidence-based, adaptable KD approach underscores the potential of dynamic distillation strategies to yield high-performing, resource-efficient models tailored for satellite-based EO applications. The reproducible code is accessible on our GitHub repository.

cs.CV↗

arXiv2024

Cognitive Semantic Augmentation LEO Satellite Networks for Earth Observation

Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques. The proposed system leverages Discrete Task-Oriented Joint Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) integrate cognitive semantic processing with inter-satellite links, enabling efficient analysis and transmission of multispectral imagery for improved object detection, pattern recognition, and real-time decision-making. Cognitive Semantic Augmentation (CSA) is introduced to enhance a system's capability to process and transmit semantic information, improving feature prioritization, consistency, and adaptation to changing communication and application needs. The end-to-end architecture is designed for next-generation satellite networks, such as those supporting 6G, demonstrating significant improvements in fewer communication rounds and better accuracy over federated learning.

cs.NI↗

arXiv2024

Seamless 5G Automotive Connectivity with Integrated Satellite Terrestrial Networks in C-Band

This paper examines integrated satellite-terrestrial networks (ISTNs) in urban environments, where terrestrial networks (TNs) and non-terrestrial networks (NTNs) share the same frequency band in the C-band which is considered the promising band for both systems. The dynamic issues in ISTNs, arising from the movement of low Earth orbit satellites (LEOSats) and the mobility of users (UEs), are addressed. The goal is to maximize the sum rate by optimizing link selection for UEs over time. To tackle this challenge, an efficient iterative algorithm is developed. Simulations using a realistic 3D map provide valuable insights into the impact of urban environments on ISTNs and also demonstrates the effectiveness of the proposed algorithm.

eess.SP↗

arXiv2024

An Experimental Study of C-Band Channel Model in Integrated LEO Satellite and Terrestrial Systems

This paper studies the channel model for the integrated satellite-terrestrial networks operating at C-band under deployment in dense urban and rural areas. Particularly, the interference channel from the low-earth-orbit (LEO) satellite to the dense urban area is analyzed carefully under the impact of the environment's characteristics, i.e., the building density, building height, and the elevation angle. Subsequently, the experimental results show the strong relationships between these characteristics and the channel gain loss. Especially, the functions of channel gain loss are obtained by utilizing the model-fitting approach that can be used as the basis for studying future works of integration of satellite and terrestrial networks (ISTNs).

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arXiv2024

Energy-Efficient Precoding and Feeder-Link-Beam Matching Design for Bent-Pipe SATCOM Systems

This paper proposes a joint optimization framework for energy-efficient linear precoding and feeder-link-beam matching design in a multi-gateway multi-beam bent-pipe satellite communication system. The proposed scheme jointly optimizes the precoding vectors at the gateway antennas and amplifying-and-matching mechanism at the satellite to maximize the system-weighted energy efficiency under the transmit power budget constraint. The technical designs are formulated into a non-convex sparsity problem consisting of a fractional-form objective function and sparsity-related constraints. To address these challenges, two iterative efficient designs are proposed by utilizing the concepts of Dinkelbach's method and the compressed-sensing approach. The simulation results demonstrate the effectiveness of the proposed scheme compared to another benchmark method.

eess.SP↗

arXiv2024

User-Centric Beam Selection and Precoding Design for Coordinated Multiple-Satellite Systems

This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT) beamforming (BF) technique. Regarding serving users at their target SINRs and minimizing the total transmit power, the scheme aims to efficiently determine satellites for users to associate with and activate the best cluster of beams together with optimizing LP for every satellite-to-user transmission. These technical objectives are first framed as a complex mixed-integer programming (MIP) challenge. To tackle this, we reformulate it into a joint cluster association and LP design problem. Then, by theoretically analyzing the duality relationship between downlink and uplink transmissions, we develop an efficient iterative method to identify the optimal solution. Additionally, a simpler duality approach for rapid beam selection and LP design is presented for comparison purposes. Simulation results underscore the effectiveness of our proposed schemes across various settings.

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arXiv2023

Satellite-based Quantum Network: Security and Challenges over Atmospheric Channel

The ultra-secure quantum network leverages quantum cryptography to deliver unsurpassed data transfer security. In principle, the well-known quantum key distribution (QKD) achieves unconditional security, which raises concerns about the trustworthiness of 6G wireless systems in order to mitigate the gap between practice and theory. The long-distance satellite-to-ground evolving quantum network distributes keys that are ubiquitous to the node on the ground through low-orbit satellites. As the secret key sequence is encoded into quantum states, it is sent through the atmosphere via a quantum channel. It still requires more effort in the physical layer design of deployment ranges, transmission, and security to achieve high-quality quantum communication. In this paper, we first review the quantum states and channel properties for satellite-based quantum networks and long-range quantum state transfer (QST). Moreover, we highlight some challenges, such as transmissivity statistics, estimation of channel parameters and attack resilience, quantum state transfer for satellite-based quantum networks, and wavepacket shaping techniques over atmospheric channels. We underline two research directions that consider the QST and wavepacket shaping techniques for atmospheric transmission in order to encourage further research toward the next generation of satellite-based quantum networks.

quant-ph↗

arXiv2023

A Hybrid Optimization and Deep RL Approach for Resource Allocation in Semi-GF NOMA Networks

Semi-grant-free non-orthogonal multiple access (semi-GF NOMA) has emerged as a promising technology for the fifth-generation new radio (5G-NR) networks supporting the coexistence of a large number of random connections with various quality of service requirements. However, implementing a semi-GF NOMA mechanism in 5G-NR networks with heterogeneous services has raised several resource management problems relating to unpredictable interference caused by the GF access strategy. To cope with this challenge, the paper develops a novel hybrid optimization and multi-agent deep (HOMAD) reinforcement learning-based resource allocation design to maximize the energy efficiency (EE) of semi-GF NOMA 5G-NR systems. In this design, a multi-agent deep Q network (MADQN) approach is employed to conduct the subchannel assignment (SA) among users. While optimization-based methods are utilized to optimize the transmission power for every SA setting. In addition, a full MADQN scheme conducting both SA and power allocation is also considered for comparison purposes. Simulation results show that the HOMAD approach outperforms other benchmarks significantly in terms of the convergence time and average EE.

eess.SP↗

arXiv2023

Harnessing the Power of Swarm Satellite Networks with Wideband Distributed Beamforming

The space communications industry is challenged to develop a technology that can deliver broadband services to user terminals equipped with miniature antennas, such as handheld devices. One potential solution to establish links with ground users is the deployment of massive antennas in one single spacecraft. However, this is not cost-effective. Aligning with recent \emph{NewSpace} activities directed toward miniaturization, mass production, and a significant reduction in spacecraft launch costs, an alternative could be distributed beamforming from multiple satellites. In this context, we propose a distributed beamforming modeling technique for wideband signals. We also consider the statistical behavior of the relative geometry of the swarm nodes. The paper assesses the proposed technique via computer simulations, providing interesting results on the beamforming gains in terms of power and the security of the communication against potential eavesdroppers at non-intended pointing angles. This approach paves the way for further exploration of wideband distributed beamforming from satellite swarms in several future communication applications.

eess.SP↗

arXiv2023

Integrated Access and Backhaul via Satellites

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

cs.NI↗

arXiv2023

Two-tier User Association and Resource Allocation Design for Integrated Satellite-Terrestrial Networks

This paper presents a study of an integrated satellite-terrestrial network, where Low-Earth-Orbit (LEO) satellites are used to provide the backhaul link between base stations (BSs) and the core network. The mobility of LEO satellites raises the challenge of determining the optimal association between LEO satellites, BSs, and users (UEs). The goal is to satisfy the UE demand while ensuring load balance and optimizing the capacity of the serving link between the BS and the LEO satellite. To tackle this complex optimization problem, which involves mixed-integer non-convex programming, we propose an iterative algorithm that leverages approximation and relaxation methods. The proposed solution aims to find the optimal two-tier satellite-BS-UE association, sub-channel assignment, power and bandwidth allocation in the shortest possible time, fulfilling the requirements of the integrated satellite-terrestrial network.

eess.SP↗

arXiv2023

Large-Scale Beam Placement and Resource Allocation Design for MEO-Constellation SATCOM

This paper presents a centralized framework for optimizing the joint design of beam placement, power, and bandwidth allocation in an MEO satellite constellation to fulfill the heterogeneous traffic demands of a large number of global users. The problem is formulated as a mixed integer programming problem, which is computationally complex in large-scale systems. To overcome this challenge, a three-stage solution approach is proposed, including user clustering, cluster-based bandwidth and power estimation, and MEO-cluster matching. A greedy algorithm is also included as a benchmark for comparison. The results demonstrate the superiority of the proposed algorithm over the benchmark in terms of satisfying user demands and reducing power consumption.

eess.SP↗

arXiv2023

LEO-to-User Assignment and Resource Allocation for Uplink Transmit Power Minimization

This paper aims to develop satellite-user association and resource allocation mechanisms to minimize the total transmit power for integrated terrestrial and non-terrestrial networks wherein a constellation of LEO satellites provides the radio access services to both terrestrial base stations (BSs) and the satellite-enabled users (SUEs). In this work, beside maintaining the traditional SatCom connection for SUEs, the LEO satellites provide backhaul links to the BSs to upload the data received from their ground customers. Taking the individual SUE traffic demands and the aggregated BS demands, we formulate a mixed integer programming which consists of the binary variables due to satellite association selection, power control and bandwidth allocation related variables. To cope with this challenging problem, an iterative optimization-based algorithm is proposed by relaxing the binary components and alternating updating all variables. A greedy mechanism is also presented for comparison purpose. Then, numerical results are presented to confirm the effectiveness of our proposed algorithms.

eess.SP↗

arXiv2022

Joint Linear Precoding and DFT Beamforming Design for Massive MIMO Satellite Communication

This paper jointly designs linear precoding (LP) and codebook-based beamforming implemented in a satellite with massive multiple-input multiple-output (mMIMO) antenna technology. The codebook of beamforming weights is built using the columns of the discrete Fourier transform (DFT) matrix, and the resulting joint design maximizes the achievable throughput under limited transmission power. The corresponding optimization problem is first formulated as a mixed integer non-linear programming (MINP). To adequately address this challenging problem, an efficient LP and DFT-based beamforming algorithm are developed by utilizing several optimization tools, such as the weighted minimum mean square error transformation, duality method, and Hungarian algorithm. In addition, a greedy algorithm is proposed for benchmarking. A complexity analysis of these solutions is provided along with a comprehensive set of Monte Carlo simulations demonstrating the efficiency of our proposed algorithms.

eess.SP↗

arXiv2022

GEO Payload Power Minimization: Joint Precoding and Beam Hopping Design

This paper aims to jointly determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates for the GEO satellite communication systems to minimize the payload power consumption and satisfy ground users' demands within a time window. Regarding constraint on the maximum number of illuminated beams per time slot, the technical requirement is formulated as a sparse optimization problem in which the hardware-related beam illumination energy is modeled in a sparsity form of the LP vectors. To cope with this problem, the compressed sensing method is employed to transform the sparsity parts into the quadratic form of precoders. Then, an iterative window-based algorithm is developed to update the LP vectors sequentially to an efficient solution. Additionally, two other two-phase frameworks are also proposed for comparison purposes. In the first phase, these methods aim to determine the MODCOD transmission schemes for users to meet their demands by using a heuristic approach or DNN tool. In the second phase, the LP vectors of each time slot will be optimized separately based on the determined MODCOD schemes.

eess.SP↗

arXiv2020

RSSI-Based Hybrid Beamforming Design with Deep Learning

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.

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arXiv2020

System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems

This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio frequency (RF) chain to transmit antenna connections can be switched off for energy saving. By explicitly considering the effect of each connection on the required power for baseband and RF signal processing, we describe the total power consumption in a sparsity form of the analog precoding matrix. However, these sparsity terms and sparsity-modulus constraints of the analog precoding make the system energy-efficiency maximization problem non-convex and challenging to solve. To tackle this problem, we first transform it into a subtractive-form weighted sum rate and power problem. A compressed sensing-based re-weighted quadratic-form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints. We then exploit alternating minimization of the mean-squared error to solve the equivalent problem where the digital precoding vectors and the analog precoding matrix are updated sequentially. The energy efficiency upper bound and a heuristic algorithm are also examined for comparison purposes. Numerical results confirm the superior performances of the proposed algorithm over benchmark energy-efficiency hybrid precoding algorithms and heuristic ones.

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arXiv2020

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

cs.NI↗