Deep learning techniques have been widely applied in wireless communication systems to enhance resilience and reduce computational complexity. This paper investigates both traditional and deep learning-based approaches for real-time relay selection in a cooperative communication system with multiple energy-harvesting relays and signal space diversity. The assumed relay decoding scheme is decode-and-forward (DF), with selection criteria based on successful decoding from the source, sufficient energy availability, and the best channel to the destination. The system performance is evaluated in terms of outage probability. Monte Carlo simulations are used to determine the exact outage probability of the system and to generate datasets for training machine learning models. The traditional machine learning models implemented include Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machines (SVMs). The deep learning-based method used is the deep neural network (DNN). Two datasets—one with six features and another with nine features—were used for training and testing. The 6-feature datasets are comparatively less random and complex than the 9-feature datasets. The results indicate that among traditional models KNN achieves the highest accuracy and is thus used as a benchmark to compare against DNN performance. For the 9-feature datasets, both KNN and DNN struggle to accurately approximate the exact outage probability, suggesting that the 9-feature datasets are too complex and noisy for effective modeling. However, on the 6-feature datasets, KNN achieves 77% accuracy, while DNN achieves a significantly higher accuracy of 99%. Due to its high accuracy, the DNN model closely approximates the exact outage probability while offering greater computational efficiency compared to the KNN model. These results underscore the potential of deep learning in optimizing real-time relay selection for energy-harvesting cooperative communication systems.
Oun et al. (Wed,) studied this question.