Rian Ferdian
Papers
1
Total Citations
19
H-Index
1
About
Rian Ferdian is a researcher whose work sits at the intersection of wireless communications and intelligent sensing, with a particular focus on indoor localization technologies. His most cited paper, "Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network" (2022), has garnered 19 citations and addresses a critical challenge in next-generation networks: achieving accurate, infrastructure-compatible indoor positioning. Ferdian’s key contribution lies in combining received signal strength indicator (RSSI) methods with probabilistic neural networks and leaky coaxial cables—a novel approach that enhances two-dimensional localization without requiring dense sensor arrays. This work is especially relevant for 5G and future communication systems, where seamless indoor navigation and location-aware services are increasingly vital. By demonstrating that existing cable infrastructure can be repurposed for high-accuracy positioning, Ferdian’s research offers a cost-effective, scalable solution for smart buildings, industrial IoT, and emergency response. His findings not only advance the theoretical understanding of RSSI-based localization but also provide a practical pathway toward integrating location intelligence into the fabric of future wireless networks.
Research Focus
Key Achievements
Top Papers
- 1