Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks

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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.

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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.

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Main ideas

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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