## 2 Literature Review
This synthetic data generation process enables the expansion of the dataset, introducing additional instances that capture the diversity of CSI patterns. By effectively mitigating data scarcity, GAN-based techniques contribute to the robustness and generalization capabilities of models trained on CSI datasets. To be more specific, the authors in [31] address the challenge of performance degradation in leave-one-subject-out validation for activity recognition based on CSI. They introduced a GAN-based framework called CsiGAN and conducted experiments on the following two distinct CSI datasets: SignFi data, which encompasses CSI traces related to sign language gestures, and FallDefi data, comprising CSI traces capturing various human activities like falling, walking, jumping, picking up, sitting down, and standing up. The authors propose a novel generator within the CsiGAN framework to generate synthetic samples, enhancing the discriminator's performance. They achieve this by increasing the number of probability outputs from \(K+1\) to \(2K+1\), where \(K\) denotes the number of classes, effectively correcting the decision boundary for each category. Additionally, they introduced manifold regularization to stabilize the learning process. It is noteworthy that their approach is rooted in CycleGAN [31], whereas our work utilized a conditional tabular GAN, representing a fundamental distinction. Our proposed methodology focuses on leveraging GANs to generate synthetic data, aiming to attain comparable accuracy with a reduced number of overall samples. This reduction in sample size leads to significant time and cost savings, distinguishing our approach from the cycle GAN-based method introduced by the authors [32]. Another limitation is the lack of diversity in activities. Most of the existing works focus on a limited set of activities such as walking, running, and sitting, which limits the scope of the model for real-world applications where a diverse range of activities can be performed. Additionally, the accuracy of the model heavily depends on the environment in which it was trained, which can result in reduced performance when deployed in a different environment due to variations in the signal characteristics. Furthermore, some existing models have high computational complexity, making them unsuitable for real-time applications. Finally, some models are designed to work only in specific scenarios, such as indoor environments or with specific types of devices, limiting their applicability in other scenarios. Moreover, Ref. [33] presents a WSN-based fingerprinting localization system for indoor environments, leveraging a low-cost sensor architecture and four tailored machine learning models. The system was validated through real experiments in complex indoor settings, demonstrating an average localization accuracy of 1.4 m. Additionally, the study analyzes the impact of reference point density on localization accuracy, highlighting the effectiveness of machine learning in enhancing WSN-based localization. Moreover, the study in [34], presents a SLAM-based localization and navigation system for social robots, specifically using the Pepper robot platform, to enhance autonomous indoor navigation in dynamic environments. The system is designed to enable efficient path planning while interacting with humans in complex indoor spaces. Validated using the robot operating system (ROS) in two different settings, the system demonstrated an average localization error of 0.51 m and an 86.1% user acceptability rate, highlighting its effectiveness in real-world applications. On the other hand, to overcome the previously mentioned challenges. We proposed a solution to overcome the constraints of existing Wi-Fi CSI-based sensing for simultaneous human activity recognition and outdoor localization by generating synthetic data and using a variety of CSI features. Moreover, we proposed a hybrid neural network to develop a more efficient model.