Leveraging Large Language Models for DRL-Based Anti-Jamming Strategies
  in Zero Touch Networks
Abubakar S. Ali Dimitrios Michael Manias Abdallah Shami Sami Muhaidat
As the dawn of sixth-generation (6G) networking approaches, it promises
unprecedented advancements in communication and automation. Among the leading
innovations of 6G is the concept of Zero Touch Networks (ZTNs), aiming to
achieve fully automated, self-optimizing networks with minimal human
intervention. Despite the advantages ZTNs offer in terms of efficiency and
scalability, challenges surrounding transparency, adaptability, and human trust
remain prevalent. Concurrently, the advent of Large Language Models (LLMs)
presents an opportunity to elevate the ZTN framework by bridging the gap
between automated processes and human-centric interfaces. This paper explores
the integration of LLMs into ZTNs, highlighting their potential to enhance
network transparency and improve user interactions. Through a comprehensive
case study on deep reinforcement learning (DRL)-based anti-jamming technique,
we demonstrate how LLMs can distill intricate network operations into
intuitive, human-readable reports. Additionally, we address the technical and
ethical intricacies of melding LLMs with ZTNs, with an emphasis on data
privacy, transparency, and bias reduction. Looking ahead, we identify emerging
research avenues at the nexus of LLMs and ZTNs, advocating for sustained
innovation and interdisciplinary synergy in the domain of automated networks.