How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN
  Slicing?
Ahmad M. Nagib Hatem Abou-Zeid Hossam S. Hassanein
The success of immersive applications such as virtual reality (VR) gaming and
metaverse services depends on low latency and reliable connectivity. To provide
seamless user experiences, the open radio access network (O-RAN) architecture
and 6G networks are expected to play a crucial role. RAN slicing, a critical
component of the O-RAN paradigm, enables network resources to be allocated
based on the needs of immersive services, creating multiple virtual networks on
a single physical infrastructure. In the O-RAN literature, deep reinforcement
learning (DRL) algorithms are commonly used to optimize resource allocation.
However, the practical adoption of DRL in live deployments has been sluggish.
This is primarily due to the slow convergence and performance instabilities
suffered by the DRL agents both upon initial deployment and when there are
significant changes in network conditions. In this paper, we investigate the
impact of time series forecasting of traffic demands on the convergence of the
DRL-based slicing agents. For that, we conduct an exhaustive experiment that
supports multiple services including real VR gaming traffic. We then propose a
novel forecasting-aided DRL approach and its respective O-RAN practical
deployment workflow to enhance DRL convergence. Our approach shows up to 22.8%,
86.3%, and 300% improvements in the average initial reward value, convergence
rate, and number of converged scenarios respectively, enhancing the
generalizability of the DRL agents compared with the implemented baselines. The
results also indicate that our approach is robust against forecasting errors
and that forecasting models do not have to be ideal.