# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 4 Knowledge-Driven Applications in Telecommunications
### Limitations, Challenges, and Future Research  
Knowledge-based applications in wireless networks offer several advantages and prove themselves invaluable in addressing the complexity of telecommunications use cases. Despite the advantages, knowledge-based applications are limited in many aspects as they introduce new demands to be met. The following list, which is by no means exhaustive, summarizes some of the limitations of knowledge-based applications in wireless communications. It should be also noted that the magnitude probability of occurrence and ease of mitigation of what is listed below is different in each category of KRR. * _Data quality_--Knowledge-based applications heavily rely on historical and real-time data and data quality. If data is inaccurate in case of data poisoning and harmful content or is incomplete or outdated, it can lead to incorrect decisions and predictions. * _Initial training_--Training certain knowledge-based models such as LLMs initially requires a substantial amount of data and computational resources. This can be a barrier to adoption, especially for smaller networks. * _Data privacy_--The collection and analysis of user data or exposure/training on a company's proprietary data for knowledge-based applications raise privacy concerns. Striking a balance between data-driven insights and user privacy is a significant challenge in the case of private information exfiltration. There is a chance that the data being used for training is exposed in the output in the case of Generative AI type of use cases; therefore, data source and proper curating of data is very important. * _Security_--Protecting the knowledge base from intervention threats and ensuring the security of data used for training and decision-making is a significant challenge. * _Trustworthiness_--resilience, fairness/biases, and hallucinations are important challenges in Generative AI-based formalisms, where the research in this area is still fairly new. * _Scalability_--As wireless networks grow in size and complexity, managing and analyzing vast amounts of data can become challenging in wireless communication, and optimizing spectrum allocation and management in real-time is complex. Knowledge-based systems must adapt to changing spectrum conditions and demands. Complexity and scalability issues may arise in knowledge-based systems. * _Resource consumption_--Knowledge-based applications can be computationally intensive and costly, requiring significant processing power and memory. This can strain network resources, especially in resource-constrained environments. * _Interoperability_--Integrating knowledge-based systems with existing network infrastructure can be complex. Modern wireless networks encompass various technologies (e.g., 5G, IoT, and mobile edge computing) and require handling diverse types of data. Ensuring compatibility and smooth integration is a challenge. * _Real-time decision making_--Wireless networks are inherently dynamic, and adapting knowledge-based models to rapidly changing conditions can be difficult. Ensuring that knowledge-based systems can make timely decisions in dynamic environments is challenging. The limitations listed above have a major impact, mainly on the technology aspects of knowledge-based applications. Further, other partly non-technical challenges governing the usage of advanced AI technologies in telecommunications also comprise  
* _Ethical considerations_--The use of user data and AI algorithms in wireless networks raises ethical concerns, including bias in decision-making and the responsible use of AI technology. * _Regulatory compliance_--Compliance with data protection and privacy regulations, e.g., General Data Protection Regulation (GDPR), poses challenges for knowledge-based applications that involve user data. * _Human-machine interaction_--Ensuring that knowledge-based systems can effectively interact with human operators and users is a challenge, particularly in critical applications.