# Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2408.02047 · HTML: https://mlsanity.com/paper/2408.02047/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 4 Aug 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, 7 of 7)

- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services](https://mlsanity.com/paper/2303.16129/) (2023)
- [A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks](https://mlsanity.com/paper/2309.01426/) (2023)
- [Deep Reinforcement Learning for Backscatter-Aided Data Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2001.10183/) (2020)
- [Accelerating Mobile Edge Generation (MEG) by Constrained Learning](https://mlsanity.com/paper/2407.07245/) (2024)
- [Mobile Edge Generation: A New Era to 6G](https://mlsanity.com/paper/2401.08662/) (2023)
- [Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research Opportunities](https://mlsanity.com/paper/2407.02804/) (2024)

## Built on by (most cited first, 1 of 1)

- [Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model](https://mlsanity.com/paper/2412.20820/) (2024)

## Abstract (arXiv)

Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently. Numerical results demonstrate that the proposed algorithm can achieve lower latency than two baseline algorithms.