arXiv subjects

Thang K. Nguyen

Publications and source records attributed to Thang K. Nguyen.

3 recordsLinked to original sources

arXiv2026

Secure RSMA-based Visible Light Networks under Spatial Correlation

This paper investigates the secrecy sum rate (SSR) of rate-splitting multiple access (RSMA)-based visible light communication (VLC) systems considering internal eavesdropping, where legitimate users may intercept private data intended for others. We formulate an optimization problem to maximize the SSR of the system, which is inherently non-convex due to the complex coupling of the objective function and constraints. To this end, two different approaches based on the convex-concave procedure (CCCP) and semidefinite relaxation (SDR) are leveraged to solve the non-convex parameterized problem. A central focus of this work is the investigation of channel similarity (CS), which serves as a metric for quantifying spatial correlation, and its impact on SSR performance. To mitigate the performance degradation caused by high spatial correlation, we propose a channel similarity reduction (CSR) clustering strategy that proactively minimizes CS to restore the system's degrees of freedom (DoF). Numerical results are provided to demonstrate the performance of the two proposed algorithms under various levels of CS. More importantly, the findings reveal that our proposed CSR-clustering strategy significantly outperforms existing baselines, effectively overcoming the secrecy performance ceiling caused by high spatial correlation.

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arXiv2025

Multi-user Visible Light Communications with Probabilistic Constellation Shaping and Precoding

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar $M$-pulse amplitude modulation ($M$-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex multivariate non-convex problem due to the non-convexity of the objective function. To tackle the original non-convex optimization problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima. The FA-based approach, however, suffers from high computational complexity. Thus, using zero-forcing (ZF) precoding, we propose a low-complexity design, which is solved using an alternating optimization approach. Additionally, considering the channel uncertainty, a robust design based on the concept of end-to-end learning with autoencoder (AE) is also presented. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. For instance, the joint design achieves $\mathbf{17.5\%}$ and $\mathbf{19.2\%}$ higher sum-rate for 8-PAM and 16-PAM, respectively, at 60 dB peak amplitude-to-noise ratio. Some insights into the optimal symbol distributions of the two joint design approaches are also provided. Furthermore, our results show the advantage of the proposed robust design over the non-robust one under uncertain channel conditions.

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arXiv2024

Joint Design of Probabilistic Constellation Shaping and Precoding for Multi-user VLC Systems

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar $M$-pulse amplitude modulation ($M$-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided.

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