Author

Aminah Indahsari Marsuki

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Conference Jul 2026

Performance Analysis of OFDM-NOMA Hybrid Scheme in Indoor Visible Light Communication

The demand for ultra-high-speed and massive connectivity in indoor wireless environments has positioned visible light communication (VLC) as a promising complementary technology for sixth-generation (6G) networks. However, orthogonal frequency division multiple access (OFDMA) and non-orthogonal multiple access (NOMA) still face a practical trade-off between spectral efficiency and user fairness, particularly under light-emitting diode (LED) dynamic-range constraints and imperfect successive interference cancellation (SIC). This paper proposes a channel state information (CSI)-driven adaptive hybrid framework combining orthogonal frequency division multiplexing (OFDM) and NOMA for indoor multiuser VLC systems. The framework selects the transmission mode before rate evaluation through a channel-gain-ratio-based policy, thereby avoiding oracle-based maximum-rate selection. The system model includes line-of-sight (LOS)-dominant propagation, a first-order non-line-of-sight (NLOS) approximation, direct-current-biased optical OFDM (DCO-OFDM) signaling, and residual interference caused by imperfect SIC. MATLAB-based Monte Carlo simulations show that the proposed scheme achieves a sum-rate of 284.67 megabits per second at a signal-to-noise ratio (SNR) of 30 decibels, representing a 38.9 percent gain over conventional NOMA while remaining close to conventional OFDMA. The scheme also attains a Jain’s Fairness Index of 0.9773 at the same SNR and improves weak-user bit error rate (BER) relative to conventional NOMA. Sensitivity analysis indicates stable performance under variations in residual SIC, NOMA power allocation, and switching threshold. These results demonstrate a practical trade-off between throughput and fairness for next-generation indoor VLC deployments.

Natasha Fedora Barus, Aminah Indahsari Marsuki, L. Novamizanti et al. · International Conference on... · 0 citations

Conference Jul 2026

Deep Learning-Based Resource Allocation for Visible Light Communication Networks

To fulfill the ultra-low latency and high-reliability requirements of sixth-generation (6G) hyperconnectivity, this paper proposes VLC-Net, a supervised deep learning framework for real-time resource allocation in multicell visible light communication (VLC) networks employing non-orthogonal multiple access (NOMA). A feedforward deep neural network (DNN) with a Bayesian-optimized architecture was trained using twenty thousand near-optimal power allocation labels generated by a fairness-centric constrained genetic algorithm (CGA). Extensive Monte Carlo simulations for networks with four, six, eight, and ten users show that VLC-Net achieves an inference time as low as 2.65 milliseconds for ten users and an average inference time of 13.71 milliseconds across all evaluated user densities. This performance represents an approximately 388 times speedup over iterative evolutionary solvers. Furthermore, the proposed model improves Jain’s fairness index by 66 percent compared with the greedy Water Filling approach while maintaining an energy efficiency of 10.87 megabits per second per watt under high-interference conditions with ten users. Paired t tests yield probability values below 0.05 for all evaluated user densities, confirming statistically significant performance differences. These results demonstrate that VLC-Net offers a robust, scalable, and low-latency solution for practical intelligent power management in future dense indoor optical wireless network deployments.

Marcelia Chintya Hartakaadi, Aminah Indahsari Marsuki, Intan Nisa Bani et al. · International Conference on... · 0 citations