## 2 Conventional Learning  
In traditional learning, the primary objective is to maximize the effectiveness of model parameters \(\theta\), such as weights in neural networks, through the application of a designated training algorithm. Therefore, the process commences with the careful selection of a model and an appropriate training algorithm. This selection holds significant importance as it determines the inductive bias \(\omega\) employed by the learning procedure to extrapolate from the training data to unseen test data. If we are given a training dataset \(D_{tr}\), then the predictive model \(\hat{y}=f_{\theta}(x)\) can be trained by solving an objective function \(L(D;\theta,\omega)\):  
\[\theta^{*}=\underset{\theta}{argmin}L(D_{tr};\theta,\omega) \tag{1}\]where \(\theta^{*}\) represents the optimized parameters that minimize the loss over the dataset \(D_{tr}\). This parameter vector encapsulates the learned knowledge from the training data and serves as the foundation for making predictions on unseen data. (\(\omega\)) in (1) is the inductive bias that plays a key role in learning. It includes the model class, (e.g., neural networks with specific architectures), the learning procedure (e.g., optimization algorithms like Stochastic Gradient Descent), and hyperparameters (e.g., learning rate, initialization). In conventional algorithms, the inductive bias is predetermined; however, in meta-learning, the goal is to optimize the learning algorithm itself so that it can quickly adapt to new tasks. This involves not only learning the model parameters \(\theta\) but also learning the inductive bias \(\omega\) that can best generalize across different tasks. By doing so, a meta-learner becomes capable of learning new tasks with minimal additional data, essentially learning how to learn. This is a key difference from conventional learning, where the inductive bias is static and must be carefully crafted by the practitioner. Meta-learning seeks to automate this aspect, making the learning process more adaptive and efficient. In wireless communication systems, conventional learning has been applied to various aspects, such as channel estimation and decoding. For instance, a machine learning-based decoder in a wireless system would typically be trained on extensive data representing different channel conditions. It would then use this learned knowledge to decode signals under similar conditions effectively. While conventional learning has been instrumental in advancing wireless communication technologies, it faces several limitations:  
* Data Dependency: It requires a considerable amount of task-specific data for training. This can be a challenge in scenarios with limited or evolving data. * Adaptability: Conventional learning models are generally trained for specific scenarios and lack the flexibility to adapt to new or unseen conditions without extensive retraining. * Computational Complexity: The process can be computationally intensive, particularly for complex models like deep neural networks. As the field of wireless communication evolves, there is a growing need for models that can quickly adapt to changing conditions and learn from limited data. This has led to an increased interest in meta-learning approaches, which offer greater flexibility and efficiency in learning across a variety of tasks and conditions. Table 2 highlights fundamental differences between conventional learning and meta-learning, especially as they apply to wireless communication systems.