## 5 Opportunities and Challenges
### Variability in Maritime Channel Measurements Affected by Climate Change  
The channel measurements such as RTT or PLR are used to select RANs among multiple RANs simultaneously utilized in the context of ATSSS features despite having no impact on RAN aspects. In the realm of communication systems, channel measurement metrics such as RTT serve as standard criteria for assessing the suitability of one method over another. Moreover, statistical channel models often stem from these fundamental channel measurements. In the context of 6G communication within the maritime sector, several studies explored the development of 6G channel models, considering the distinctive characteristics that arise from the maritime communication environment, which differs significantly from terrestrial communication environments [3; 98; 99; 100]. When examining the data in [101] (Figures 16 and 18), it becomes apparent that wireless channel conditions in the maritime communication environment tend to stay relatively stable on sunny and calm days. In contrast, they show significant variability on windy days, primarily attributed to the presence of high waves, even in coastal regions. This variability implies that channel models may not sustain consistent effectiveness, even within the same maritime region geographically. The outcome of channel measurements is intrinsically dynamic, influenced by specific conditions like weather or the increasingly unpredictable temperature patterns resulting from the significant effects of climate change currently experienced by the global community. This highlights a disconnect between the current communication environment and what lies ahead in the maritime sector. As a result, channel models developed based on channel measurement during the research phase may not be optimally suited for real-world deployment scenarios of the maritime communication environment, which usually come to be implemented in at least five years to a decade after the research phase.  
In recent times, there has been a remarkable surge in interest surrounding the utilization of Artificial Intelligence (AI) technologies for channel modeling within the academic research community [102; 103; 104]. Additionally, the 3 GPP standards body has undertaken the exploration of AI and Machine Learning (ML) technologies to enhance the overall functionalities of 5G and beyond systems. This exploration encompasses AI/ML-based network energy conservation, data-traffic load balancing, and mobility optimization within 3 GPP systems, while considering the dynamic nature of air interfaces [105; 106].  
Emphasizing the critical nature of acquiring a substantial volume of actual channel-measurement data for AI/ML-based approaches is imperative. In the maritime sector,autonomous ships can play a pivotal role in collecting this data, spanning diverse weather conditions, including severe weather situations when conventional navigation is typically constrained. Furthermore, it is worth noting that the maritime communication environment varies significantly across different regions, such as polar sea regions, earthquake-prone areas, open-ocean expanses, and coastal regions. This variability underscores the challenges of creating a single universal channel model of a 6G communication system capable of providing ubiquitous connectivity in all locations, either terrestrial or non-terrestrial areas. The availability of a comprehensive global dataset of real channel measurements collected in the maritime communication environment becomes increasingly crucial. It will enable the development of optimized channel models integrated with AI/ML technologies for the maritime sector within the context of the 6G framework. These models can be considered when global standards bodies like 3 GPP work towards establishing comprehensive 6G channel model frameworks, catering to scenarios encompassing space, air, and sea as well as terrestrial environments.