Predictive 5G Uplink Slicing for Blockchain-driven Smart Energy Contracts

The energy grid is facing a paradigm shift away from traditionally centralized electricity generation towards dis-tributed renewable energy resources. These so-called Smart Grids (SGs) require a mechanism for balancing power consumption and generation. In this context, Blockchain (BC)-based Smart Contracts (SCs) have emerged as a means to facilitate distrib-uted transactions without requiring trust among the involved parties. Yet, resulting communication traffic loads need to be considered. Here, 5G network slicing promises to enable the coexistence of such mission critical services on a single shared physical communication infrastructure. Nevertheless, challenges in terms of latencies and resource efficiency exist. As static slicing mechanisms can be inefficient, we propose a predictive Machine Learning (ML)-driven approach to Resource Block (RB) scheduling by harnessing the Configured Grant (CG) mechanism in the 5G uplink. The developed solution is evaluated on the particularly challenging example of an energy grid driven by SCs. Based on an energy model derived from a real-world setup, we generate corresponding SC communication traffic. For this, predictive 5G slice radio resource allocation is employed to demonstrate significant improvements in terms of latency and spectrum usage efficiency. Thus, ML-enabled 5G network slicing for mission critical SCs is evaluated within large-scalable SGs.