Transforming Data Sharing via Advanced Peer-to-Peer Communication and Networking

Abstract 5G networks promise super-fast internet, but efficiently sharing resources is a big challenge, especially when users directly connect with each other (Peer-to-Peer). This research explores a new way to allocate resources in 5G P2P communication. We use machine learning to predict how much data will be used and adjust resource sharing in real-time. This means that users with the most urgent needs get priority, and resources are distributed fairly. Our method significantly improved network performance. Simulations showed a 30% increase in data transfer speeds, a 25% reduction in delays, and an 18% overall efficiency boost. Compared to older methods, our approach better balances the load and minimizes data loss. Extensive testing in a 5G environment confirmed the system's effectiveness, demonstrating its ability to handle many users and adapt to changing conditions. In conclusion, our research shows that P2P communication, combined with intelligent resource allocation, can unlock the full potential of 5G networks for data transfer. Keywords: 5G Networks, Peer-to-Peer Communication, Resource Allocation, Data Transmission, Machine Learning, Internet of Things (IoT), Edge Computing, Network Slicing, Artificial Intelligence (AI), Blockchain, Cloud Computing, Latency Optimization, Spectrum Management, Smart Cities, Wireless Sensor Networks.