References of "Trinh, van Chien 50040507"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailDeep Learning-Aided 5G Channel Estimation
Le, Ha An; Trinh, van Chien UL; Nguyen, Tien Hoa et al

in Deep Learning-Aided 5G Channel Estimation (2021, January 06)

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 45 (2 UL)
Full Text
Peer Reviewed
See detailCoverage Probability and Ergodic Capacity of Intelligent Reflecting Surface-Enhanced Communication Systems
Trinh, van Chien UL; Tu, Lam Thanh; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2020)

This paper studies the performance of a single-input single-output (SISO) system enhanced by the assistance of an intelligent reflecting surface (IRS), which is equipped with a finite number of elements ... [more ▼]

This paper studies the performance of a single-input single-output (SISO) system enhanced by the assistance of an intelligent reflecting surface (IRS), which is equipped with a finite number of elements under Rayleigh fading channels. From the instantaneous channel capacity, we compute a closed-form expression of the coverage probability as a function of statistical channel information only. A scaling law of the coverage probability and the number of phase shifts is further obtained. The ergodic capacity is derived, then a simple upper bound to simplify matters of utilizing the symbolic functions and can be applied for a long period of time. Numerical results manifest the tightness and effectiveness of our closed-form expressions compared with Monte-Carlo simulations. [less ▲]

Detailed reference viewed: 99 (12 UL)
Full Text
Peer Reviewed
See detailPareto-Optimal Pilot Design for Cellular Massive MIMO Systems
Le, Anh Tuan; Trinh, van Chien UL; Reza Nakhai, Mohammad et al

in IEEE Transactions on Vehicular Technology (2020)

We introduce a non-orthogonal pilot design scheme that simultaneously minimizes two contradicting targets of channel estimation errors of all base stations (BSs) and the total pilot power consumption of ... [more ▼]

We introduce a non-orthogonal pilot design scheme that simultaneously minimizes two contradicting targets of channel estimation errors of all base stations (BSs) and the total pilot power consumption of all users in a multi-cell massive MIMO system, subject to the transmit power constraints of the users in the network. We formulate a multi-objective optimization problem (MOP) with two objective functions capturing the contradicting targets and find the Pareto optimal solutions for the pilot signals. Using weighted-sum-scalarization technique, we first convert the MOP to an equivalent single-objective optimization problem (SOP), which is not convex. Assuming that each BS is provided with the most recent knowledge of the pilot signals of the other BSs, we then decompose the SOP into a set of distributed non-convex optimization problems to be solved at individual BSs. Finally, we introduce an alternating optimization approach to cast each one of the resulting distributed optimization problems into a convex linear matrix inequality (LMI) form. We provide a mathematical proof for the convergence of the proposed alternating approach and a complexity analysis for the LMI optimization problem. Simulation results confirm that the proposed approach significantly reduces pilot power, whilst maintaining the same level of channel estimation error as in [1]. [less ▲]

Detailed reference viewed: 46 (3 UL)
Full Text
Peer Reviewed
See detailRobust Probabilistic-Constrained Optimization for IRS-Aided MISO Communication Systems
Le, Anh Tuan; Trinh, van Chien UL; Di Renzo, Marco

in IEEE Wireless Communications Letters (2020)

Taking into account imperfect channel state information, this letter formulates and solves a joint active/passive beamforming optimization problem in multiple-input single-output systems with the support ... [more ▼]

Taking into account imperfect channel state information, this letter formulates and solves a joint active/passive beamforming optimization problem in multiple-input single-output systems with the support of an intelligent reflecting surface. In particular, we introduce an optimization problem to minimize the total transmit power subject to maintaining the users' signal-to-interference-plus-noise-ratio coverage probability above a predefined target. Due to the presence of probabilistic constraints, the proposed optimization problem is non-convex. To circumvent this issue, we first recast the proposed problem in a convex form by adopting the Bernstein-type inequality, and we then introduce a converging alternating optimization approach to iteratively find the active/passive beamforming vectors. In particular, the transformed robust optimization problem can be effectively solved by using standard interior-point methods. Numerical results demonstrate the effectiveness of jointly optimizing the active/passive beamforming vectors. [less ▲]

Detailed reference viewed: 70 (7 UL)