Malik Muhammad Saad

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About

Malik Muhammad Saad is a rising researcher at the forefront of autonomous systems and next-generation wireless networks. His work centers on two critical, interconnected domains: the intelligent path planning of unmanned aerial vehicles (UAVs) and the optimization of edge computing architectures for 5G-enabled applications. Saad’s most influential contribution is his comprehensive 2021 overview of UAV path planning, which has garnered 60 citations and serves as a foundational reference for researchers exploring everything from military surveillance to commercial package delivery. This work systematically addresses the core challenge of enabling compact, powerful flying robots to navigate complex environments autonomously. Building on this, his 2025 research introduces a novel Twin Delayed DDPG (TD3) reinforcement learning approach for edge server selection in 5G networks. This work tackles a critical bottleneck in real-time applications like Industrial IoT and Cooperative Intelligent Transport Systems (C-ITS), where efficient server selection is paramount for low-latency performance. By bridging the gap between autonomous aerial systems and intelligent edge computing, Saad is charting a clear path for the future of distributed, latency-sensitive applications in the 5G era and beyond.