|Authors||O. Nassef, T. Mahmoodi, F. Michelinakis, K. Mahmood and A. Elmokashfi|
|Title||Optimising Performance for NB-IoT UE Devices through Data Driven Models|
|Project(s)||5G-VINNI: 5G Verticals INNovation Infrastructure|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Journal||Journal of Sensor and Actuator Networks|
|Publisher||Multidisciplinary Digital Publishing Institute|
This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.