From: Recent advances on artificial intelligence and learning techniques in cognitive radio networks
Learning technique | Spectrum sensing (SS) | Decision-making | Strengths | Limitations and challenges |
---|---|---|---|---|
 |  |  | Adaptation ability to minor changes | Require training data labels |
Neural networks | × | × | Construction using few examples, | Poor generalization |
 |  |  | thus reducing the complexity | Overfitting |
Support | Â | Â | Generalization ability | Requires training data labels |
vector | × | × | Robustness against noise and outliers | and previous knowledge of the system |
machine | Â | Â | Â | Complex with large problems |
 |  |  | Multi-objective optimization | Require prior knowledge of the system |
Genetic algorithms |  | × | Dynamically configure the CR | Suitable fitness function |
 |  |  | based on environment changes | High complexity with large problems |
Game theory | Related to the capabilities of the | × | Reduces the complexity of adaptation | Requires prior knowledge of the system |
 | spectrum-sensing technique used |  | Solutions for multi-agent systems | and labeled training data |
Reinforcement | × | × | Learning autonomously using feedback | Needs learning phase of the policies |
learning | Â | Â | Self-adaptation progressively in real time | Â |
Fuzzy logic | Related to the capabilities of the | × | Simplicity, decisions are | Needs rules derivation |
 | spectrum-sensing technique used |  | directly inferred from rules | Accuracy is based on these rules |
Entropy approach | × | × | Statistical model | Requires prior knowledge of the system |
 | Related to the capabilities of the |  | Simplicity | Requires prior knowledge of the system |
Decision tree | spectrum-sensing technique used | × | Decision using tree branches | May suffer overfitting |
 |  |  |  | Requires labeled training data |
Artificial |  | × | Parallel search for solutions | Requires prior knowledge of the system |
bee colony | Â | Â | Â | Requires a fitness function |
Bayesian | × | × | Probabilistic models | Requires prior knowledge of the system |
 |  |  |  | May face computational complexity |
Markov model | × | × | Statistical models | Requires prior knowledge of the system |
 |  |  | Scalable | May face computational complexity |
Case- | Â | Â | Find acceptable solution based | Complex search in large databases |
based |  | × | on the existing case found | Requires predefined and relevant cases |
reasoning | Â | Â | in the case database | Mistakes propagation |