  Intent-based network automation is a promising tool to enable easier network
management however certain challenges need to be effectively addressed. These
are: 1) processing intents, i.e., identification of logic and necessary
parameters to fulfill an intent, 2) validating an intent to align it with
current network status, and 3) satisfying intents via network optimizing
functions like xApps and rApps in O-RAN. This paper addresses these points via
a three-fold strategy to introduce intent-based automation for O-RAN. First,
intents are processed via a lightweight Large Language Model (LLM). Secondly,
once an intent is processed, it is validated against future incoming traffic
volume profiles (high or low). Finally, a series of network optimization
applications (rApps and xApps) have been developed. With their machine
learning-based functionalities, they can improve certain key performance
indicators such as throughput, delay, and energy efficiency. In this final
stage, using an attention-based hierarchical reinforcement learning algorithm,
these applications are optimally initiated to satisfy the intent of an
operator. Our simulations show that the proposed method can achieve at least
12% increase in throughput, 17.1% increase in energy efficiency, and 26.5%
decrease in network delay compared to the baseline algorithms.
