While deep learning offers significant advantages for interference cancellation in grant-free NOMA systems, there are potential limitations and drawbacks to consider:
Complexity: Deep learning models can be complex, requiring substantial computational resources during training and inference phases.
Data Dependency: Deep learning models rely heavily on large amounts of labeled data for training, which may not always be readily available or easy to obtain in practical scenarios.
Generalization Issues: Deep learning models may struggle with generalizing well to unseen data or adapting quickly to changing environments without continuous retraining.
4 .Interpretability: Deep learning models are often considered black boxes due to their complex architectures, making it challenging to interpret how decisions are made within the model.
To mitigate these limitations, a hybrid approach combining deep learning with traditional signal processing techniques could offer a more balanced solution.