## 1 Introduction
These are called the environmental effects of surroundings on radio waves [28]. Static objects produce persistent impacts that remain consistent unless the sensing environment changes, whereas the impact of moving objects creates a unique pattern associated with a particular activity. These systematic fluctuations establish a solid foundation for training a deep model to learn a distinctive mapping of an activity for its classification via channel state information (CSI). The CSI defines wireless channel propagation characteristics that include the consequences of fading and scattering on the system [29]. It is a complex representation of a unique pattern of static and dynamic vectors' movement in a 3D space which can be analyzed by vector magnitude and phase in a complex polar form [30]. Since radio signals are heavily dependent upon environmental specifications, the performance of such trained deep models is relevant to these conditions. There are limitations and challenges which motivate novel active research in similar domains. The recorded CSI data are assumed to be generated for a particular environment with certain characteristics, such as the position of surrounding objects, which are mainly responsible for constructing static vectors. If their positions are changed from training to testing data, network performance suffers from degradation [31; 32]. Interference caused by other moving objects apart from the target movement in the sensed area can lead to changes in the recorded CSI from training to the testing data because it may produce additional phase shifts or alter the magnitude of the dynamic vector and affect the system overall accuracy [30]. Hardware used in the transceiver antennas produces noises such as sampling frequency offset (SFO) which may differ by the variation of hardware from different vendors. Thus, hardware noises can modify the results and ultimately the accuracy of the system [33]. Interference generated by other Wi-Fi devices and microwave appliances nearby is another cause of desensitization of the recorded CSI data [34]. The CSI measurements are also sensitive to atmospheric conditions and can show dissimilarities in recorded data for an activity collected at different times or days of the week [35]. Since multiple factors can restrict a system's performance in a controlled environment, every time a system undergoes any change, it needs to be retrained with new data samples for mapping the CSI variations according to the recent modifications. Collecting new data samples is impractical, as it takes heavy data storage, maintenance, human labor, and time. Retraining a model from scratch is also computationally expensive. We therefore seek a concrete solution that can help to generalize this field for more practical applications. In this paper, we attempt for the first time to apply multi-source domain adaptation to an unsupervised model using Wi-Fi-based CSI data for cross-domain applications. Given the fact that domain shifting has the extra burden of generating, migrating, and annotating sufficient data, we take advantage of multiple scarce source domains to map their feature spaces to a target domain. Most of the public CSI datasets are collected in such a way that they cannot be used for cross-domain applications due to limited data samples per domain (particular user/environment or a specific day of the week). However, the proposed model overcomes the problem by successfully leveraging the data from diverse domains and aligning the distributions related to each specific source domain with a target domain. In addition, we train domain-specific classifiers by using domain-invariant representations acquired from diverse source domains. When these trained classifiers are used to predict target classes, they might classify them differently due to distribution mismatch near the decision boundaries. Ultimately, we align the decision boundaries of each classifier for the target domain.