## 3 Findings and Discussion
### Toward Sustainability: Net Zero and Edge Intelligence in High-Frequency Contexts  
The primary aim of net zero operations is to achieve a state of zero environmental impact and carbon footprint when initiating projects or introducing technological advancements [57; 58]. The alignment of net-zero goals with future higher-frequency network deployments underscores a commitment not only to technological advancement but also to ensuring user safety [59]. It is crucial to incorporate strict safety precautions, thoroughly study the consequences on cognitive health, and ensure that technological improvements align with net-zero principles. This ensures a sustainable technical landscape that eliminates environmental effects while ensuring human cognitive well-being in high-frequency network deployments. As communities strive towards sustainability, these infrastructures become vital to improved efficiency and inter-connectivity [60].  
Another fundamental aspect of high-frequency networks is their reliance on edge computing, a paradigm that revolutionizes data processing by enabling real-time analysis at the network periphery [61]. The integration of edge computing into high-frequency networks presents significant potential for innovation and efficiency, meeting the increasing demand for faster data processing across diverse electronic devices and networks situated closer to the user [62]. To guarantee that edge computing deployment complies with safety regulations, cooperation between technology development and cognitive health research is essential. This approach might help create a technologically sophisticated environment that is both effective and considerate of its influence on human intelligence, guaranteeing a smooth transition of edge computing into high-frequency networks.  
On the other hand, integrating artificial intelligence (AI) features at the network periphery to transform instantaneous decision-making, edge intelligence represents an evolutionary advancement inside high-frequency network operations [63]. Without depending entirely on centralized systems, this integration allows for quick data processing and well-informed actions in high-frequency signal circumstances where split-second responses are required [64]. Edge intelligence reduces latency by locally processing and filtering data, ensuring quick and precise responses that are essential for real-time communication networks applications [65]. Furthermore, through the selective transmission of critical information, this integration lowers the strain on network bandwidth and maximizes the efficiency of data processing [66]. Edge intelligence enhances network resilience through its decentralized decision-making process, which empowers autonomous decision-making even during network disruptions [67]. This method eliminates the vulnerability of sensitive data during transmission, which improves security measures and boosts operational efficiency [68; 69]. A major step forward, the integration of edge intelligence with high-frequency network operations improves the responsiveness, security, and agility of contemporary data-intensive environments [70; 71]. This revolutionary merger changes the game for high-frequency network operations by allowing for a more flexible and efficient network architecture.