# Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model

arXiv: https://arxiv.org/abs/2412.20820 · HTML: https://mlsanity.com/paper/2412.20820/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Evaluating Retrieval Quality in Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.13781/) (2024)
- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [Large Language Model Enhanced Multi-Agent Systems for 6G Communications](https://mlsanity.com/paper/2312.07850/) (2023)
- [Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing](https://mlsanity.com/paper/1910.07990/) (2019)
- [Distilling Knowledge for Fast Retrieval-based Chat-bots](https://mlsanity.com/paper/2004.11045/) (2020)
- [Retrieval Augmented Generation using Engineering Design Knowledge](https://mlsanity.com/paper/2307.06985/) (2023)
- [wChain: A Fast Fault-Tolerant Blockchain Protocol for Multihop Wireless Networks](https://mlsanity.com/paper/2102.01333/) (2021)
- [Interference Aware Cooperative Routing for Edge Computing-enabled 5G Networks](https://mlsanity.com/paper/2201.01520/) (2022)
- [Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning](https://mlsanity.com/paper/2408.02047/) (2024)

## Built on by (most cited first, 3 of 3)

- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [DIRC-RAG: Accelerating Edge RAG with Robust High-Density and High-Loading-Bandwidth Digital In-ReRAM Computation](https://mlsanity.com/paper/2510.25278/) (2025)

## Abstract (arXiv)

The rapid evolution of mobile edge computing (MEC) has introduced significant challenges in optimizing resource allocation in highly dynamic wireless communication systems, in which task offloading decisions should be made in real-time. However, existing resource allocation strategies cannot well adapt to the dynamic and heterogeneous characteristics of MEC systems, since they are short of scalability, context-awareness, and interpretability. To address these issues, this paper proposes a novel retrieval-augmented generation (RAG) method to improve the performance of MEC systems. Specifically, a latency minimization problem is first proposed to jointly optimize the data offloading ratio, transmit power allocation, and computing resource allocation. Then, an LLM-enabled information-retrieval mechanism is proposed to solve the problem efficiently. Extensive experiments across multi-user, multi-task, and highly dynamic offloading scenarios show that the proposed method consistently reduces latency compared to several DL-based approaches, achieving 57% improvement under varying user computing ability, 86% with different servers, 30% under distinct transmit powers, and 42% for varying data volumes. These results show the effectiveness of LLM-driven solutions to solve the resource allocation problems in MEC systems.