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Table 1 Learning techniques applications in CR, strengths and limitations

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    Generalization ability Requires training data labels
vector × × Robustness against noise and outliers and previous knowledge of the system
machine     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    Self-adaptation progressively in real time  
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     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-    Find acceptable solution based Complex search in large databases
based   × on the existing case found Requires predefined and relevant cases
reasoning    in the case database Mistakes propagation