Armin Makvandi

Armin Makvandi, Yousef Seifi Kavian

In this paper, a machine learning-based decentralized time division multiple access (TDMA) algorithm for visible light communication (VLC) Internet of Things (IoT) networks is proposed. The proposed algorithm is based on Q-learning, a reinforcement learning algorithm. This paper considers a decentralized condition in which there is no coordinator node for sending synchronization frames and assigning transmission time slots to other nodes. The proposed algorithm uses a decentralized manner for synchronization, and each node uses the Q-learning algorithm to find the optimal transmission time slot for sending data without collisions. The proposed algorithm is implemented on a VLC hardware system, which had been designed and implemented in our laboratory. Average reward, convergence time, goodput, average delay, and data packet size are evaluated parameters. The results show that the proposed algorithm converges quickly and provides collision-free decentralized TDMA for the network. The proposed algorithm is compared with carrier-sense multiple access with collision avoidance (CSMA/CA) algorithm as a potential selection for decentralized VLC IoT networks. The results show that the proposed algorithm provides up to 61% more goodput and up to 49% less average delay than CSMA/CA.

Armin Makvandi, Md. Zoheb Hassan, Md. Jahangir Hossain

The highly dynamic nature of vehicular networks necessitates proactive and site-specific radio resource management (RRM) to achieve ultra-reliable low-latency communications. While Network Digital Twins (NDTs) have emerged as a promising enabler, ray-tracing remains time-consuming, challenging accurate RRM under latency constraints. We propose AdaPTwin, an adaptive multi-fidelity predictive NDT for proactive and latency-aware RRM in vehicular networks. Unlike single- and multi-fidelity NDTs with fixed fidelity levels, AdaPTwin dynamically adjusts NDT fidelity based on network conditions. The framework adopts a hierarchical cloud-edge architecture, where computationally intensive fidelity selection is performed periodically in the cloud, and the proactive RRM loop operates in real-time at the edge. The edge-based proactive RRM task consists of channel prediction between vehicles and roadside units (RSUs) via trajectory forecasting and look-ahead ray tracing, followed by RRM execution. A transformer model enhanced with continual and transfer learning enables vehicular trajectory prediction while adapting to new environments and traffic patterns. Ray-tracing is performed using NVIDIA Sionna by exploiting a dynamically updated virtual environment to ensure realistic radio propagation within the NDT. Furthermore, a joint RSU beamforming and vehicle-RSU association problem is formulated to maximize proportionally fair sum-rate, and it is efficiently solved using a scalable multi-start iterative coordinate descent algorithm. Comparisons against reactive, single-fidelity, and non-adaptive predictive NDTs under realistic vehicular conditions confirm that AdaPTwin successfully adapts to diverse scenarios where other frameworks fail. Ultimately, AdaPTwin achieves up to 90% sum-rate gain and 80% outage probability reduction compared to non-adaptive NDTs, while maintaining real-time performance.