## 4 Use Cases  
In this section, we attempt to highlight different and rather innovative wireless communication areas and how they can be facilitated by using meta-learning and compare it with specific classes of learning algorithms. We used the classic communication problem of designing optimal decoders and demodulators in RF-WCSs, OWCs, and MCSs. A typical communication link comprises transmitting and receiving nodes, where the receiver is equipped with demodulators and decoders that assist in estimating the channel. For this purpose, a known training sequence (pilot symbols) is transmitted to estimate the channel state information (CSI) by utilizing the shared knowledge between the transmitter and the receiver. Simple least mean square (LS) or linear minimum mean square error (LMMSE) methods are then used for channel estimation. This method has proven to be effective in many channel models; however, they fail to perform adequately in scenarios with limitations in the availability of channel models, e.g., in visible light communication or molecular communication. In MIMO systems, the pilot's length is smaller compared to the number of antennas leading to poor performance of the aforementioned techniques. Additionally, the pilot symbols reduce the overall spectral efficiency of the communication link, as 8% of the information in IEEE 802.11 models comprises pilot symbols in a 20 MHz channel [47]. In a traditional machine learning-based system, the received signal is fed as an input, and the transmitted symbols are retrieved at the output, where the demodulation and decoding are treated as a classification task. Neural network architectures are trained over extensive datasets comprising pilot symbols, allowing them to learn different distributions and correlations, which are then used for channel estimation. The training dataset directly affects the performance of the overall system, during testing, along with the adequacy of the parametric model. Some of the recent works include [48], where the authors investigate the mean square error (MSE) difference between the machine learning-based channel estimation and the minimum mean square error (MMSE) estimation channel estimation and showed that there is an upper bound of the MSE difference even if the inputs are not from the training dataset. In [49], [PERSON] et al. proposed channel estimation based on training neural networks to learn MIMO channel statistics over a dataset, which is then used to jointly design pilots and estimate the channel, hence diminishing the need for covariance matrices. They showed that their proposed NN structure improved the normalized mean square error (NMSE) as compared to the techniques that used FFT pilots' NN-based channel estimation. The wide range of wireless communication standards and protocols operates in a diverse environment. Hence, the non-trivial components, such as encoders and decoders, should adapt according to the varying traffic statistics. The conventional machine learning protocols discussed previously train a model specific to a particular situation. Therefore, a more desired approach would be to train models that perform adequately in all configurations. More recently, meta-learning has drawn attention as it operates at a higher abstraction level and uses data from different configurations. We consider a typical IoT scenario, with k = 1, 2... K devices and a base station (BS). The data bits are mapped to complex constellation symbols \(s_{k}\), drawn from the set of all constellation symbols \(S\). The signal received at the BS is then defined as  
\[y_{k}=h_{k}x_{k}+n_{k} \tag{5}\]  
where \(h_{k}\) is the complex channel gain, \(n_{k}\) is zero-mean additive white Gaussian noise (AWGN) with power spectral density \(N_{o}/2\) and \(x_{k}\) is the transmitted data after going through random transformations, which are given by the conditional distributions \(P_{k}(.|s_{k})\) that model the non-linearities of the IoT devices.