Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks

最新推荐文章于 2025-07-21 19:09:28 发布

chenduoQi

最新推荐文章于 2025-07-21 19:09:28 发布

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分类专栏：学术分享文章标签：深度学习

本文链接：https://blog.youkuaiyun.com/the__future/article/details/110940963

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本文构建了一个实现边缘计算的框架，特别关注AI算法在通信系统中的应用。研究重点在于从用户设备到基站的数据传输，而非AI算法的具体实现。文章提出了针对移动用户发射功率、基站波束成形向量及RIS相移矩阵的优化算法。

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I will share the papers in the filed of CIM (computing in memory) based on emerging NVM (nonvolatile memory), especially RRAM.
The ideal state is that I will update the series blogs every day. However, I can not guarantee this because I am BUSY now. You know, homeworks, class projects and my own research projects.

Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks

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Abstract
Main ideas
- 1.
Inspirations

Abstract

This article built a framework to implement the edge computing, and the computing here is specifically AI algorithm. This work focuses on the data transmission from users devices to base station but not the AI algorithm implementation. Because the raw data sampled by sensors cannot be used directly by AI model. The problem is more of a communication problem. The highlight of this paper is the proposed algorithms to get the optimal solutions of the three variables, transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS under the loss function. Unfortunately, I don't focus on these algorithms very much, therefore, I am not going to go into detail on them.

Main ideas

1.

在这里插入图片描述

joint optimization problem

Inspirations

I want to find more applications scenarios of NN algorithm in communication system. The phase-shift matrix of the RIS has been demonstrated that it can be implemented using CNN. How about the other two variables in this articles? They are all the nonconvex problems with high complexity. If the algorithms of the two problems can be replaced by NN, maybe the NVM can be used.

the url of this paper