Aditya Guhagarkar

Aditya Guhagarkar

Wireless Tech Enthusiast

Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

Towards Efficient Scheduling in RIS-Aided Wireless Networks with Non-Linear Energy Harvesting Permalink

Published in 16th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2025

This paper presents a comparative study of scheduling mechanisms in RIS-assisted wireless networks with non-linear energy harvesting constraints. Four techniques are evaluated: Deep Q-Network (DQN), Constraint Programming (CP), Time Division Multiple Access (TDMA), and random user selection. TDMA and random selection offer simplicity but underperform in dense networks. While CP guarantees optimal decisions, its high computational complexity makes it unsuitable for real-time deployment. The DQN-based approach strikes an effective balance, achieving ~90% of CP’s throughput while reducing decision latency by over 1000x. This highlights DQN as a highly viable solution for real-time scheduling in RIS-enabled networks.

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Reinforcement Learning-Based Optimization of Relay Selection and Transmission Scheduling for UAV-Aided mmWave Vehicular Networks Permalink

Published in 27th International Symposium on Wireless Personal Multimedia Communications (WPMC), 2024

Millimeter-wave (mmWave) communications offer abundant bandwidth for vehicular networks; however, they are prone to blockages due to buildings, topology, and other environmental factors. To address these challenges, we propose a novel unmanned aerial vehicle (UAV)-aided two-way relaying system to enhance vehicular connectivity and coverage. We formulate a joint optimization problem for relay selection and transmission scheduling to minimize transmission time while ensuring throughput requirements. Proximal policy optimization, deep Q-network, and constraint programming models are employed to solve the optimization problem. Extensive evaluations reveal that the proximal policy optimization model achieves close to 100% accuracy.

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