arXiv subjects

Van Duc Nguyen

Publications and source records attributed to Van Duc Nguyen.

10 recordsLinked to original sources

arXiv2026

Single- and Multi-Objective Stochastic Optimization for Next-Generation Networks in the Generative AI and Quantum Computing Era

Next Generation (NG) networks move beyond simply connecting devices to creating an ecosystem of connected intelligence, especially with the support of generative Artificial Intelligence (AI) and quantum computation. These systems are expected to handle large-scale deployments and high-density networks with diverse functionalities. As a result, there is an increasing demand for efficient and intelligent algorithms that can operate under uncertainty from both propagation environments and networking systems. Traditional optimization methods often depend on accurate theoretical models of data transmission, but in real-world NG scenarios, they suffer from high computational complexity in large-scale settings. Stochastic Optimization (SO) algorithms, designed to accommodate extremely high density and extensive network scalability, have emerged as a powerful solution for optimizing wireless networks. This includes various categories that range from model-based approaches to learning-based approaches. These techniques are capable of converging within a feasible time frame while addressing complex, large-scale optimization problems. However, there is currently limited research on SO applied for NG networks, especially the upcoming Sixth-Generation (6G). In this survey, we emphasize the relationship between NG systems and SO by eight open questions involving the background, key features, and lesson learned. Overall, our study starts by providing a detailed overview of both areas, covering fundamental and widely used SO techniques, spanning from single to multi-objective signal processing. Next, we explore how different algorithms can solve NG challenges, such as load balancing, optimizing energy efficiency, improving spectral efficiency, or handling multiple performance trade-offs. Lastly, we highlight the challenges in the current research and propose new directions for future studies.

cs.IT↗

arXiv2025

Exact Coherent Structures of Sheared Double-Diffusive Convection

The interaction between shear and double-diffusive convection (DDC) in the diffusive regime (cold fresh water on top of hot salty water) plays an important role in the heat and mass transport of polar region oceans. This study computes exact coherent structures (ECS) of diffusive-regime DDC with a uniform background shear in a vertically wall-bounded flow layer. We focus on the shear-influenced regime and present two-dimensional (2D) ECS consisting of steady-state solutions and periodic orbits. The steady-state solutions include tilted convective rolls with various horizontal wavenumbers, and they are invariant under horizontal translation. All tilted convective roll states undergo saddle-node bifurcation, leading to a stable upper branch and an unstable lower branch, suggesting that they originate from the subcritical bifurcation of conduction base states. Hopf bifurcations appear on the stable upper branch of tilted convective rolls, leading to periodic orbits. Bifurcation diagrams for dimensionless parameters, including the Rayleigh number, the Prandtl number, and the diffusivity ratio, are established, suggesting subcritical behavior. Increasing shear strength stabilizes the 2D tilted convective roll, while these tilted convective rolls continue to exist in the limit of zero density ratio corresponding to sheared Rayleigh-Bénard convection. Extension to the three-dimensional (3D) domain leads to 3D streamwise-elongated roll solutions; one of them originates from a subcritical bifurcation of the corresponding 2D roll solution. Chaotic solutions from direct numerical simulations generally visit neighborhoods of these steady or periodic solutions, and these visits leave an imprint on the flow statistics.

physics.flu-dyn↗

arXiv2025

Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control

An intelligent Real-Time Sensing (RTS) system must continuously acquire, update, integrate, and apply knowledge to adapt to real-world dynamics. Managing distributed intelligence in this context requires Federated Continual Learning (FCL). However, effectively capturing the diverse characteristics of RTS data in FCL systems poses significant challenges, including severely impacting computational and communication resources, escalating energy costs, and ultimately degrading overall system performance. To overcome these challenges, we investigate how the data distribution shift from ideal to practical RTS scenarios affects Artificial Intelligence (AI) model performance by leveraging the \textit{generalization gap} concept. In this way, we can analyze how sampling time in RTS correlates with the decline in AI performance, computation cost, and communication efficiency. Based on this observation, we develop a novel Sample-driven Control for Federated Continual Learning (SCFL) technique, specifically designed for mobile edge networks with RTS capabilities. In particular, SCFL is an optimization problem that harnesses the sampling process to concurrently minimize the generalization gap and improve overall accuracy while upholding the energy efficiency of the FCL framework. To solve the highly complex and time-varying optimization problem, we introduce a new soft actor-critic algorithm with explicit and implicit constraints (A2C-EI). Our empirical experiments reveal that we can achieve higher efficiency compared to other DRL baselines. Notably, SCFL can significantly reduce energy consumption up to $85\%$ while maintaining FL convergence and timely data transmission.

cs.LG↗

arXiv2023

Double RIS-Assisted MIMO Systems Over Spatially Correlated Rician Fading Channels and Finite Scatterers

This paper investigates double RIS-assisted MIMO communication systems over Rician fading channels with finite scatterers, spatial correlation, and the existence of a double-scattering link between the transceiver. First, the statistical information is driven in closed form for the aggregated channels, unveiling various influences of the system and environment on the average channel power gains. Next, we study two active and passive beamforming designs corresponding to two objectives. The first problem maximizes channel capacity by jointly optimizing the active precoding and combining matrices at the transceivers and passive beamforming at the double RISs subject to the transmitting power constraint. In order to tackle the inherently non-convex issue, we propose an efficient alternating optimization algorithm (AO) based on the alternating direction method of multipliers (ADMM). The second problem enhances communication reliability by jointly training the encoder and decoder at the transceivers and the phase shifters at the RISs. Each neural network representing a system entity in an end-to-end learning framework is proposed to minimize the symbol error rate of the detected symbols by controlling the transceiver and the RISs phase shifts. Numerical results verify our analysis and demonstrate the superior improvements of phase shift designs to boost system performance.

cs.IT↗

arXiv2022

RIS-Assisted MIMO Communication Systems: Model-based versus Autoencoder Approaches

This paper considers reconfigurable intelligent surface (RIS)-assisted point-to-point multiple-input multiple-output (MIMO) communication systems, where a transmitter communicates with a receiver through an RIS. Based on the main target of reducing the bit error rate (BER) and therefore enhancing the communication reliability, we study different model-based and data-driven (autoencoder) approaches. In particular, we consider a model-based approach that optimizes both active and passive optimization variables. We further propose a novel end-to-end data-driven framework, which leverages the recent advances in machine learning. The neural networks presented for conventional signal processing modules are jointly trained with the channel effects to minimize the bit error detection. Numerical results demonstrate that the proposed data-driven approach can learn to encode the transmitted signal via different channel realizations dynamically. In addition, the data-driven approach not only offers a significant gain in the BER performance compared to the other state-of-the-art benchmarks but also guarantees the performance when perfect channel information is unavailable.

cs.IT↗

arXiv2021

On the Performance of Image Recovery in Massive MIMO Communications

Massive MIMO (Multiple Input Multiple Output) has demonstrated as a potential candidate for 5G-and-beyond wireless networks. Instead of using Gaussian signals as most of the previous works, this paper makes a novel contribution by investigating the transmission quality of image data by utilizing the Massive MIMO technology. We first construct a framework to decode the image signal from the noisy received data in the uplink Massive MIMO transmission by utilizing the alternating direction method of multipliers (ADMM) approach. Then, a low-pass filter is exploited to enhance the efficiency of the remaining noise and artifacts reduction in the recovered image. Numerical results demonstrate the necessity of a post-filtering process in enhancing the quality of image recovery.

cs.IT↗

arXiv2021

Deep Learning-Aided 5G Channel Estimation

Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for $5$G-and-beyond networks. In this paper, we propose a new channel estimation method with the assistance of deep learning in order to support the least squares estimation, which is a low-cost method but having relatively high channel estimation errors. This goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile used for simulations in the 5G networks under the severity of Doppler effects. Numerical results demonstrate the superiority of the proposed deep learning-assisted channel estimation method over the other channel estimation methods in previous works in terms of mean square errors.

cs.IT↗

arXiv2021

Impact of Inter-Channel Interference on Shallow Underwater Acoustic OFDM Systems

This paper investigates the impacts of Inter-Channel Interference (ICI) effects on a shallow underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication system. Considering both the turbulence of the water surface and the roughness of the bottom, a stochastic geometry-based channel model utilized for a wide-band transmission scenario has been exploited to derive a simulation model. Since the system bandwidth and the sub-carrier spacing is very limited in the range of a few kHz, the channel capacity of a UWA system is severely suffered by the ICI effect. For further investigation, we construct the signal-to-noise-plus-interference ratio (SINR) based on the simulation model, then evaluate the channel capacity. Numerical results show that the various factors of a UWA-OFDM system as subcarriers, bandwidth, and OFDM symbols affect the channel capacity under the different Doppler frequencies. Those observations give hints to select the good parameters for UWA-OFDM systems.

cs.IT↗

arXiv2019

Natural Language Generation for Non-Expert Users

Motivated by the difficulty in presenting computational results, especially when the results are a collection of atoms in a logical language, to users, who are not proficient in computer programming and/or the logical representation of the results, we propose a system for automatic generation of natural language descriptions for applications targeting mainstream users. Differently from many earlier systems with the same aim, the proposed system does not employ templates for the generation task. It assumes that there exist some natural language sentences in the application domain and uses this repository for the natural language description. It does not require, however, a large corpus as it is often required in machine learning approaches. The systems consist of two main components. The first one aims at analyzing the sentences and constructs a Grammatical Framework (GF) for given sentences and is implemented using the Stanford parser and an answer set program. The second component is for sentence construction and relies on GF Library. The paper includes two use cases to demostrate the capability of the system. As the sentence construction is done via GF, the paper includes a use case evaluation showing that the proposed system could also be utilized in addressing a challenge to create an abstract Wikipedia, which is recently discussed in the BlueSky session of the 2018 International Semantic Web Conference.

cs.AI↗

arXiv2013

Controlling colloidal phase transitions with critical Casimir forces

The critical Casimir effect provides a thermodynamic analogue of the well-known quantum mechanical Casimir effect. It acts between two surfaces immersed in a critical binary liquid mixture, and results from the confinement of concentration fluctuations of the solvent. Unlike the quantum mechanical effect, the magnitude and range of this attraction can be adjusted with temperature via the solvent correlation length, thus offering new opportunities for the assembly of nano and micron-scale structures. Here, we demonstrate the active assembly control of equilibrium phases using critical Casimir forces. We guide colloidal particles into analogues of molecular liquid and solid phases via exquisite control over their interactions. By measuring the critical Casimir particle pair potential directly from density fluctuations in the colloidal gas, we obtain insight into liquefaction at small scales: We apply the Van der Waals model of molecular liquefaction and show that the colloidal gas-liquid condensation is accurately described by the Van der Waals theory, even on the scale of a few particles. These results open up new possibilities in the active assembly control of micro and nanostructures.

cond-mat.soft↗