## 2 Literature Review
The performance of this technique is preferable to those in the abovementioned literature. However, this approach is prone to the phase shift in CSI caused by the timing offset between the Wi-Fi transmitter and receiver. Additionally, performance can be considerably degraded by a small mismatch in the phase domain of CSI. To handle this issue, a linear fitting method and phase calibration technique have been employed [26]. We applied a simple LSTM with one hidden layer and 128 hidden units for the CSI amplitude feature with a 52-dimensional vector. The proposed technique extracts features automatically without preprocessing, and then it can hold the temporal state information of the activity. Thus, it enhances the performance of the proposed approach. Other deep learning architectures, which include deep adversarial networks, deep reinforcement learning, and transfer learning, are also frequently used for CSI-based human activity recognition and localization. In addition to the above CNN- and RNN-based works in the literature [27], deep adversarial networks were used for cross-scenario human activity recognition in Ref. [28]. To accomplish this, the researchers designed a maximum-minimum adversarial method to correlate source features and target features. In addition, a center alignment approach was developed to further enhance the source domain's feature quality, which is essential for performance improvement. For the same purpose, Ref. [29] presented deep reinforcement learning-based human activity recognition. To do this, two issues, namely, the feature patterns of the received signals and the translation of the received data to the detection results, need to be jointly addressed. As a result, the authors combined two problems into one optimization problem and developed a deep reinforcement learning-based strategy to find the best solution by reducing the cross-entropy loss of the recognition results, thus enhancing recognition accuracy. However, most human activity recognition schemes are highly location-dependent and susceptible to environmental factors. To alleviate this issue, further research was conducted. For instance, Ref. [30] developed a human activity recognition scheme by utilizing transfer learning and a CNN to accomplish location-independent human activity recognition. The data distribution of the received signals and their properties were examined for feature extraction. Then, by transferring features from the source environments to the testing location, transfer learning and CNN were implemented to successfully learn discriminatory features. As a result, the proposed schemes achieved a preferable sensing performance. All the above deep learning schemes are mainly employed in human activity recognition. On the other hand, the above deep learning networks have also been also used in CSI-based localization. Generally, the major limitation of existing works on simultaneous deep learning-based human activity recognition and localization using Wi-Fi signals is the limited dataset. Many of these works have been conducted on small and limited datasets, which may not represent real-world scenarios. As a result, overfitting the model and reduced generalization capabilities may be the result. However, there are several research studies that endeavored to address the challenges posed by the limited dataset of CSI through innovative techniques, with a notable emphasis on generative adversarial networks (GAN)-based approaches. These methodologies aim to overcome the scarcity of real-world CSI data by generating synthetic samples that closely resemble the underlying patterns and dynamics of authentic CSI observations. GAN-based strategies consist of a generator and a discriminator. The generator generates synthetic CSI samples, attempting to mimic the characteristics of the original data, while the discriminator evaluates the authenticity of these synthetic samples. Through an adversarial training process, the generator refines its output to be increasingly indistinguishable from real CSI data.