摘要
A key challenge to the scalable deployment of the energy self-sustainability(ESS)Internet of Everything(IoE)for sixth-generation(6G)networks is juggling massive connectivity and high spectral efficiency(SE).Cell-free massive multiple-input multiple-output(CF mMIMO)is considered as a promising solution,where many wireless access points perform coherent signal processing to jointly serve the users.However,massive connectivity and high SE are difficult to obtain at the same time because of the limited pilot resource.To solve this problem,we propose a new framework for ESS IoE networks where the user activity detection(UAD)and channel estimation are decoupled.A UAD detector based on deep convolutional neural networks,an initial access scheme,and a scalable power control policy are proposed to enable the practical scalable CF mMIMO implementation.We derive novel and exact closed-form expressions of harvested energy and SE with maximum ratio(MR)processing.Using local partial minimum mean-square error and MR combining,simulation results prove that the proposed framework can serve more users,improve the SE performance,and achieve better user fairness for the considered ESS IoE networks.
