# Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network

arXiv: https://arxiv.org/abs/2405.17436 · HTML: https://mlsanity.com/paper/2405.17436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 2 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks](https://mlsanity.com/paper/2003.13606/) (2020)
- [D2RL: Deep Dense Architectures in Reinforcement Learning](https://mlsanity.com/paper/2010.09163/) (2020)
- [Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.06146/) (2018)
- [Learning Decentralized Wireless Resource Allocations with Graph Neural Networks](https://mlsanity.com/paper/2107.01489/) (2021)
- [Recurrent Reinforcement Learning: A Hybrid Approach](https://mlsanity.com/paper/1509.03044/) (2015)
- [Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning](https://mlsanity.com/paper/1901.04292/) (2018)
- [Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching](https://mlsanity.com/paper/2110.10349/) (2021)
- [Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service](https://mlsanity.com/paper/2104.01036/) (2021)
- [Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks](https://mlsanity.com/paper/2207.00755/) (2022)
- [DECT-2020 New Radio: The Next Step Towards 5G Massive Machine-Type Communications](https://mlsanity.com/paper/2101.07158/) (2021)

## Abstract (arXiv)

In this paper, we aim to maximize the SSR for heterogeneous service demands in the cooperative MEC-assisted RAN slicing system by jointly considering the multi-node computing resources cooperation and allocation, the transmission resource blocks (RBs) allocation, and the time-varying dynamicity of the system. To this end, we abstract the system into a weighted undirected topology graph and, then propose a recurrent graph reinforcement learning (RGRL) algorithm to intelligently learn the optimal hybrid RA policy. Therein, the graph neural network (GCN) and the deep deterministic policy gradient (DDPG) is combined to effectively extract spatial features from the equivalent topology graph. Furthermore, a novel time recurrent reinforcement learning framework is designed in the proposed RGRL algorithm by incorporating the action output of the policy network at the previous moment into the state input of the policy network at the subsequent moment, so as to cope with the time-varying and contextual network environment. In addition, we explore two use case scenarios to discuss the universal superiority of the proposed RGRL algorithm. Simulation results demonstrate the superiority of the proposed algorithm in terms of the average SSR, the performance stability, and the network complexity.