Mohanraju Muppala
Biography
Mohanraju Muppala is a seasoned Marine IT Technology and Shipping Industry expert with over 14 years of experience, having served in senior roles within leading international shipping companies. His expertise covers vessel IT infrastructure, PMS, marine ERP administration, and large-scale digital transformation initiatives in compliance with global maritime standards. Recognized by India?s largest television network under the ?Young Talent in category, He has consistently delivered innovative solutions that enhance operational efficiency, cybersecurity, and environmental compliance across the maritime sector.
His expertise spans AI-driven marine monitoring systems, IoT-enabled vessel networks, and regulatory compliance tools such as CII (Carbon Intensity Indicator) calculation modules integrated with AIS and voyage data. As the author of Digital SeasNet ? A Global Guide to Marine Technology and Cybersecurity Standards and the forthcoming Smart Oceans: AI and IoT for Real-Time Marine Monitoring, he bridges the gap between cutting-edge research and practical maritime applications.
Research Interest
Interests focus on using advanced Marine and IT Technologies to improve resilience and efficiency. He explores Marine IT, Cybersecurity artificial intelligence (AI), digital twins, predictive analytics, and data governance. His work includes AI-driven demand forecasting, risk management, and real-time decision-making through digital Technologies.
Abstract
AI-Driven Optimization Frameworks for Next-Generation Satellite Communication Systems
The rapid proliferation of non-terrestrial networks (NTNs), consisting mega-constellations in low Earth orbit (LEO), has ushered in an era of unprecedented complexity in satellite communication (SatCom) systems. Traditional optimization methods, typically relies on static models and heuristic approaches, are increasingly inadequate for managing dynamic resources, mitigating interference, ensuring quality of service (QoS) in these vast, time-variant networks. This paper investigates the transfiguring potential of artificial intelligence (AI), particularly machine learning (ML) and deep reinforcement learning (DRL), in addressing these critical challenges. We propose a novel hierarchical AI framework for integrated SatCom optimization, encompassing beam hopping, power allocation, and handover management. A core contribution is a bespoke DRL agent, the Dual-TimeScale Resource Manager (DTSRM), which operates across two time scales: a fast-timescale for power and bandwidth allocation and a slow-timescale for beam placement and handover decisions. Simulation results demonstrate that the proposed framework achieves a 38% improvement in overall system throughput and a 52% reduction in handover-induced latency compared to a conventional greedy algorithm benchmark. Furthermore, the DTSRM agent shows robust adaptability to sudden traffic shifts, maintaining a consistent QoS where traditional methods fail. This research underscores the necessity of AI-driven solutions for the next generation of SatCom systems and provides a scalable architecture for their implementation.
Index Terms?Artificial Intelligence, Machine Learning, Deep Reinforcement Learning, Satellite Communications, Non-Terrestrial Networks, Beam Hopping, Resource Allocation, Handover Management.
