## 3 Challenges and Related Recommendation
### Future Research Challenges
#### 3.2.5 Al-Enabled Networks  
Network AI is a crucial enabling technology in the 6G era for AI-enabled smart wireless telecommunication networks. It offers a whole AI ecosystem within the network itself, including AI facilities AI process logic, data and design solutions, etc. [128]. The field of network AI remains in its infancy, which means that there is a great deal of unanswered questions. This is despite the fact that AI innovations are advancing at a quick pace and that the previously described fresh approaches may make an impact. To begin, studies on artificial intelligence in wireless networks rely heavily on data. Consequently, the most pressing issue is figuring out methods to set up a shared data set for investigations as well as how to gather and utilize data in wireless networks. Paying close attention to the functioning of AI products is also essential when implementing AI to the network. Improved AI functionality may be obtained by carefully selecting AI algorithms and allocating network assets. An internet-based network design method that adjusts to network fluctuations and modelling the association across AI outcomes and network setup are the biggest obstacles [129]. It is critical to investigate ways to enhance the EE and decrease energy usage as well as expenses prior to the widespread implementation of AI services [130]. This is because more sophisticated and precise AI systems tend to use a lot of resources and have substantial negative environmental effects. If present trends continue, the increasing computing load of deep learning may soon limit its use in various fields, rendering the attainment of crucial benchmark goals unreachable. Deep learning becoming computationally limited would not be unprecedented in history. Productivity was constrained by the processing resources accessible even when [PERSON] created the initial neural networks. Thanks to acceleration from switching to specially designed equipment and a desire to dedicate additional assets to enhance productivity, many functional limitations have been loosened in the last decade. As the amount of data sets increases, the amount of deep learning parameters also has to expand, which can lead to overparameterization. Computational demands increase by a minimum the square of the data sets in the overparameterized case, while the expense of training a deep learning structure grows with a combination of the total amount of variables and the quantity of data sets. When it comes to deep learning systems, this exponential expansion is not nearly quick enough to boost ability. In order to see a linear improvement in achievement, the volume of data used for training typically needs to rise at a faster-than-linear rate. Linear regression provides a good example of a well-developed mathematical acquiring concept (and, by extension, a 1-layer by layer neural system with linear activations) that can be used to comprehend the connection among deep learning system variables, data, and computational demands. One possible approach to reducing the computational load on deep learning systems is enhancing data curation. More specifically, instead of using more data, models might be trained with improved data to decrease processing requirements [131]. Although higher-quality data would ideally reduce computing expenses, it may still have an unclear impact on overall prices due to the high expense of data curation (e.g., higher human annotator expenses). One alternative to deal with deep learning's increasing computing load is to switch to different forms of machine learning, some of which may be just as unexplored or undervalued. Even though \"expert\" approaches are often cheaper in terms of computing, their efficiency will eventually halt if the experts involved are unable to identify all of the elements that a model with greater flexibility could test.