Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems
This work addresses spectral efficiency prediction for wireless communication systems, but it is incremental as it applies existing ML methods to a specific domain problem.
The paper tackled the problem of predicting spectral efficiency in massive MIMO systems using machine learning, achieving a mean average percentage error (MAPE) of less than 10% with gradient boosting and neural networks in most simulated scenarios.
Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machine learning (ML) can be successfully applied. In this paper, we study several ML approaches to solve the problem of estimating the spectral efficiency (SE) value for a certain precoding scheme, preferably in the shortest possible time. The best results in terms of mean average percentage error (MAPE) are obtained with gradient boosting over sorted features, while linear models demonstrate worse prediction quality. Neural networks perform similarly to gradient boosting, but they are more resource- and time-consuming because of hyperparameter tuning and frequent retraining. We investigate the practical applicability of the proposed algorithms in a wide range of scenarios generated by the Quadriga simulator. In almost all scenarios, the MAPE achieved using gradient boosting and neural networks is less than 10\%.