Learning based E2E Energy Efficient in Joint Radio and NFV Resource Allocation for 5G and Beyond Networks

TL;DR

This paper introduces a deep reinforcement learning framework for joint radio and NFV resource allocation in 5G networks, optimizing energy efficiency while maintaining QoS, and demonstrates significant energy savings over separate optimization methods.

Contribution

It presents a novel joint optimization framework using SAC-DRL for energy-efficient resource allocation in 5G networks, integrating radio and core network management.

Findings

Significant reduction in energy consumption with joint optimization.

Effective use of SAC-DRL for dynamic resource management.

Improved end-to-end QoS in 5G networks.

Abstract

In this paper, we propose a joint radio and core resource allocation framework for NFV-enabled networks. In the proposed system model, the goal is to maximize energy efficiency (EE), by guaranteeing end-to-end (E2E) quality of service (QoS) for different service types. To this end, we formulate an optimization problem in which power and spectrum resources are allocated in the radio part. In the core part, the chaining, placement, and scheduling of functions are performed to ensure the QoS of all users. This joint optimization problem is modeled as a Markov decision process (MDP), considering time-varying characteristics of the available resources and wireless channels. A soft actor-critic deep reinforcement learning (SAC-DRL) algorithm based on the maximum entropy framework is subsequently utilized to solve the above MDP. Numerical results reveal that the proposed joint approach based on the SAC-DRL algorithm could significantly reduce energy consumption compared to the case in which R-RA and NFV-RA problems are optimized separately.