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

arXiv: https://arxiv.org/abs/2107.05991 · HTML: https://mlsanity.com/paper/2107.05991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Network Function Virtualization: State-of-the-art and Research Challenges](https://mlsanity.com/paper/1509.07675/) (2015)
- [A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access](https://mlsanity.com/paper/1908.08401/) (2019)
- [Network Slicing for 5G with SDN/NFV: Concepts, Architectures and Challenges](https://mlsanity.com/paper/1703.04676/) (2017)
- [Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications](https://mlsanity.com/paper/1912.09302/) (2019)
- [Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining](https://mlsanity.com/paper/1710.02611/) (2017)
- [Service Function Chaining Resource Allocation: A Survey](https://mlsanity.com/paper/1608.00095/) (2016)
- [CPVNF:Cost-efficient Proactive VNF Placement and Chaining for Value-Added Services in Content Delivery Networks](https://mlsanity.com/paper/1803.06025/) (2018)
- [VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks](https://mlsanity.com/paper/1812.11365/) (2018)
- [5G Cellular: Key Enabling Technologies and Research Challenges](https://mlsanity.com/paper/1503.00674/) (2015)
- [Towards Service-oriented 5G: Virtualizing the Networks for Everything-as-a-Service](https://mlsanity.com/paper/1604.01739/) (2016)
- [Impact of Processing-Resource Sharing on the Placement of Chained Virtual Network Functions](https://mlsanity.com/paper/1710.08262/) (2017)
- [Optimized Energy Aware 5G Network Function Virtualization](https://mlsanity.com/paper/1812.09416/) (2018)
- [Joint Resource and Admission Management for Slice-enabled Networks](https://mlsanity.com/paper/1912.00192/) (2019)

## Abstract (arXiv)

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.