## 3 Challenges and Related Recommendation
### Fundamental Theories Challenges  
Few aspects are included in this section including channel research, information theory of the electromagnetic, and baseband processing including the unified, iterative, hardware generation, and architecture. When discussing Unique Network Research, there are typically four stages to conventional channel research: measuring the channel, estimating its parameters, analyzing its characteristics, and finally, modelling it. Several restrictions are imposed by this passive method of channel recognition. It takes an extensive amount of time, assets, and effort to perform the channel assessment. Also, channel assessments will never be able to capture every possible frequency range or circumstance in the real world. Channel parameter evaluation is complicated and plagued by massive amounts of data. The channel characteristics can only be examined at predetermined frequencies and in predetermined circumstances; thus, the complex link among novel qualities and frequencies/scenarios cannot be thoroughly explored. Last but not least, conventional channel modelling cannot foretell the future channel properties of frequency ranges or scenarios for which no data are now available. Various channel models, such as 6G established pervasive channel demonstration [56], AI-based forecasting 6G channel demonstrating [57; 58], scenario flexible channel modelling, and RIS medium modelling, will need to switch from passively acknowledging channels to proactively controlling them for 6G [59]. Various methods for optimizing antenna-channel interaction are shown in Figure 8. Two methodologies have been employed for channel interaction, distinguished by their level of sophistication. The primary research avenues for AI-based strategies in optimizing antenna and channel compounds involve either the straightforward modification of certain high-level station attributes, such as antenna/beam/tilt choice, or the additional immediate alteration of antenna arrangements according to channel information. AI is anticipated to be especially beneficial for the ideal pattern creation technique illustrated in Figure 8, since a flexible construction of the radiating architecture entails a substantial improvement search space. When it comes to ML-based channel description and forecasting, there are two main areas where the techniques shine. In the first, it is commonly used to find the locating function among the surroundings and the channel characteristics or to immediately mimic the assessment data (Figure 9a,b), respectively. In the second, it is more common to acquire knowledge about the past behaviour of channels and use that information to forecast or extrapolate future channels (Figure 9c). One might come across it in the most recent research: most ML-based channel forecasting techniques are built on top of artificial neural networks (ANNs), which have varied network topologies but both offline training and online executions with minimal computing overhead. As a result, creating effective channel prediction algorithms presents several  
Figure 8: Various methods for optimizing antenna–channel interaction. The grey arrows represent the complexity direction (increasing), while the orange arrow represents a two-way direction between the antenna and the channel. Figure 9: Fundamental concepts of ML-based channel (**a**) steps included in a simulation, (**b**) modelling of prediction using ML, and (**c**) forecasting. formidable obstacles related to the type of the ANN, and the input/output type that should be used for training. Conversely, the conceptual underpinnings of wireless communications rest on the theory of information and electromagnetic (EM) theory. The (EM theory) describes how electromagnetic waves are created and how they travel through space. When dealing with constrained resources like power and bandwidth, the information theory lays out a framework for precise and effective data transmission.