**4. Challenges and opportunities**

While MIMO systems offer many potential communication performance improvements and enable highly accurate localization models, several challenges still *Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental… DOI: http://dx.doi.org/10.5772/intechopen.112037*

need to be addressed. This section aims to introduce some of the main challenges in MIMO localization.

#### **4.1 Dynamic environments**

The majority of the models presented above assume a *static environment*, where the objects within the environment of interest are not moving or changing. In real world scenarios, we observe *dynamic environments* where objects are constantly moving through the environment changing the scattering in the environment quickly and thoroughly [53]. The static environment can be altered by any of the following dynamic changes:


Some efforts have been undertaken to mitigate the impact of dynamic changes. For example, through the analysis of the time sequence of fingerprints, it becomes possible to identify the moment when a dynamic change anomaly occurred. Models can then be developed to identify and remove the effect of the dynamic change anomaly from the fingerprint sample. However, countering the effects of the dynamic environment still poses a challenge in many proposed approaches.

#### **4.2 Dataset collection**

Data-driven localization techniques, specifically DL techniques, thus far have shown the best performance when it comes to accuracy. However, there is a major challenge with real world deployment of these models. In particular, data-driven methods necessitate extensive datasets for training the models, which are obtained through costly measurement campaigns that can be difficult to perform. Furthermore, as the environment changes, the dataset becomes invalid and a new measurement campaign needs to be deployed.

#### **4.3 Generalization**

Generalization in massive MIMO refers to the ability of a system to maintain good performance in a wide range of scenarios, including different channel conditions and new environments. This is important for practical deployment of massive MIMO systems, as it ensures that the system will work well in real-world environments where the conditions may vary.

Transfer Learning (TL) has been suggested as a potential approach to improve generalization in machine learning [73]. This technique involves reusing a pre-trained model to enhance the learning and generalization of a new model. In TL, the pretrained model is fine-tuned to the new environment using a small dataset representative of that environment. The goal is to leverage the knowledge gained from the prior environment to enhance the learning and generalization of the new environment.

Some studies have been exploring TL techniques to adapt their models to new environments [73, 100, 101]. However, TL does not solve the problem completely as it still requires some data collection in new environments. Generalization remains an open area of research in DL-based localization.
