Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)

Abstract

Recently, the concept of open radio access network (O-RAN) has been proposed, which aims to adopt intelligence and openness in the next generation radio access networks (RAN). It provides standardized interfaces and the ability to host network applications from third-party vendors by xapplications (xAPPs), which enables higher flexibility for network management. However, this may lead to conflicts in network function implementations, especially when these functions are implemented by different vendors. In this paper, we aim to mitigate the conflicts between xAPPs for near-real-time (near-RT) radio intelligent controller (RIC) of O-RAN. In particular, we propose a team learning algorithm to enhance the performance of the network by increasing cooperation between xAPPs. We compare the team learning approach with independent deep Q-learning where network functions individually optimize resources. Our simulations show that team learning has better network performance under various user mobility and traffic loads. With 6 Mbps traffic load and 20 m/s user movement speed, team learning achieves 8% higher throughput and 64.8% lower PDR.

Deep Reinforcement Learning-based Radio Resource Allocation and Beam Management under Location Uncertainty in 5G mmWave Networks

“…Specifically, reinforcement learning (RL) is designed to exploit the current environment without any prior knowledge, which avoids the complexity of building a dedicated optimization model [6]. Moreover, combined with a deep neural network, deep reinforcement learning (DRL) overcomes the limitations of tabular-based RL, which has been generally applied for wireless network optimizations [7].…”