Self-renewal machine learning framework for wireless network optimization
Most of the efforts to tackle the complex resource allocation problem in wireless networks are towards the development of approximation algorithms which are mathematically intense and computationally expensive, and they do not always guarantee global optimality. Since it is difficult to obtain sufficient data that covers all the possible commodity flow deployments within a wireless network, we opine that an ML model should be intelligent to continuously learn new knowledge, i.e., when the model’s performance degrades on a set of new optimization instances that are not part of the base training data, it should update its knowledge through an offline self-renewal procedure.