### Meta-Learning in Wireless Communication Systems
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Related Work** & **Key Contributions** \\ \hline [28] & Proposed a channel metamodeling approach to understand the underlying channel characteristics \\ \hline [29] & Presented a new meta-learning benchmark based on channel coding \\ \hline [30] & Developed an algorithm to improve the convergence rate of federated meta-learning and a proposed resource allocation strategy. \\ \hline [31] & Proposed a framework for adaptive beam-forming design based on the idea of embedding in metric-based meta-learning. \\ \hline [32] & Proposed a MAML-based NN decoder that allows fast adaption to varying channels. \\ \hline [33] & Proposed a meta-learning-based channel estimation approach called RoemNet. \\ \hline [34] & Provided a brief introduction to meta-learning with respect to communication systems. \\ \hline [35] & Used a meta-learning-based approach to find a common initialization vector to allow faster convergence. \\ \hline [36] & Trained a decoder, in an IoT scenario, using meta-training data to allow quick adaptations to varying channel conditions. \\ \hline [37] & Compared meta-learning with GNNs trained for a single task using the beamforming problem. \\ \hline [38] & Proposed a meta-learning-based channel acquisition and passive beamforming technique using fewer pilot symbols in RIS systems. \\ \hline [39] & Employed federated meta-learning to propose a framework for 6G-enabled consumer electronics device intrusion detection within a Meta-Verse environment \\ \hline [40] & Proposed a one-dimensional Multi-Scale Dilated Convolution Neural Network (MSDCNN) and a meta-transfer metric learning using scale function (MLS) to improve time series classification in 6G-supported Intelligent Transportation Systems (ITSs). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of works that used meta-learning in different wireless communication applications. the proposed design, they incorporated multiple channels as multiple tasks to train a meta-learner that effectively learned the general parameters of these tasks and enabled an adequate initialization. RoemNet allowed them to obtain an effective channel estimation, given a small number of pilots. The authors of [34] briefly introduce meta-learning specific to communication systems. Recent literature showcases the application of meta-learning in improving wireless communication systems' performance by addressing channel variation issues. Meanwhile, [PERSON] et al. [36] proposed using meta-learning to identify a common initialization vector that would enable faster training on any channel, resulting in faster convergence, as demonstrated through various numerical results. Here, authors considered an IoT scenario where pilots from previous transmissions of other IoT devices were used as meta-training data. This allowed the demodulator to adapt to various channel conditions while sending only a few pilots. The results of this study were compared with various state-of-the-art meta-learning algorithms in a unified framework. [PERSON] et al. [37] reviewed meta-learning applications for radio transmission. They demonstrated that meta-learning could significantly reduce the sample and time complexity for adapting to new scenarios. Their research compared meta-learning with graph neural networks (GNNs) trained for single tasks, particularly in solving beamforming problems. Their findings indicated that, while GNNs are more sample-efficient, meta-learning offers reduced time complexity for adaptation. The authors of [38] investigated the problem of practical reconfigurable intelligent surface (RIS) systems such as channel acquisition and phase shift matrix design and proposed using meta-learning to automate the initialization of model parameters. This technique allowed quick adaptation to new tasks with limited samples. At the base station (BS), an inductive bias is trained using previously received pilot signals to closely approximate optimally trained models.