Continual Adaptation and Dynamic Number of Devices Management for Resource Provisioning in NextG O-RAN
Mrityunjoy Gain, Avi Deb Raha, Apurba Adhikary, Han Zhu, Choong Seon Hong · 2025
The Open Radio Access Network (O-RAN) paradigm offers a compelling solution to the constraints of traditional RAN by establishing an open framework that enables data-driven optimization at the individual user level, which is essential for the evolution of the next-generation (NextG) cellular networks. Accurate predictions of CPU demand for each user's equipment (UE) in the O-Cloud at the next step will enable more efficient CPU resource optimization. While AI is promising to optimize CPU utilization, it encounters two significant challenges. First, the varying number of UEs leads to shifts in feature dimensions, rendering the model unable to accept these inputs since the input dimension of the AI model remains fixed. Second, the ongoing introduction of new types of UEs over time with distinct CPU demands and dynamic combinations of various active devices adds further variability, thereby complicating predictive accuracy. To address the first challenge, in this research, we propose a novel dynamic number of devices management (DNDM) framework that effectively accommodates a dynamic number of devices in O-RAN, addressing the challenges associated with variable UE demands in future NextG O-RAN. We formulate an optimization problem for the second challenge, enabling the model to learn new demand scenarios while preserving knowledge from previously encountered configurations. To solve the optimization, we propose an exemplar replaybased continual adaptation (CA) framework designed to operate within the near real-time RAN Intelligence Controller (RT-RIC). The CA-DNDM actively prevents catastrophic forgetting and delivers continuous adaptability, seamlessly handling evolving UE types and quantities. Through extensive experimental results, we demonstrate that the proposed CA-DNDM framework effectively handles scenarios with varying UE counts and reliably predicts CPU demand for new situations while preserving the knowledge gained from prior scenarios.