Sudeep Tanwar

Scheduling Oriented Resource Allocation Using Backscattered NOMA for Heterogeneous Network

Growing areas in the wireless communication systems are mainly rise in connectivity, automation, connected devices, artificial intelligence, etc., have attracted the interest of researchers. Nowadays, non‐orthogonal multiple access (NOMA) is thoroughly employed to improve network capabilities. A combination of NOMA w...

Spatiotemporal dynamics of urban thermal environments shaped by surface characteristics and their ecological implications

Interpretable geospatial machine learning for seasonal land surface temperature prediction using multi-source earth observation data

FIDES: A Federated Intelligence and Detection with Quantum Security for Financial Institutions

With the increasing sophistication of financial frauds, there is now a need for more advanced, secure, and scalable detection mechanisms. A fraud detection framework has been proposed that uses Federated Deep Learning (FDL) and Quantum Key Distribution (QKD) for non-IID financial data while carrying out secure communic...

Reinforcement Learning-Driven Optimal Uav Selection Framework for Efficient Uav-To-Uav Communication

Unmanned Aerial Vehicles (UAVs) have gained widespread attention in diverse applications like military, medical, aerial surveillance and many more. Presently, the problem of limited bandwidth and geographic factors has raised the need for effective and timely data transfer. Training UAVs with reinforcement learning-bas...

Quantum-Secured Onion Routing with Federated Learning for 5G-Enabled Spaceborne IoT Networks

In this paper, we recommend an Onion Routing framework powered by federated learning and augmented with E91-based quantum key distribution (QKD) to protect next-generation communication systems like 5G-supported satellite and spaceborne IoT networks. Conventional encryption techniques protect message content but are st...

Next-gen D2D communication: efficient resource allocation with clustered combinatorial matching

A maximum matching algorithm for channel allocation with a faster convergence rate that divides the entire set of cellular users and D2D groups into overlapping clusters based on channel gains and utilizes the Kuhn-Munkres algorithm for the best channel allocation to the D2D groups within the same cluster.