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Blind Detection Algorithm Based on Spectrum Sharing and Coexistence for Machine-to-Machine Communication
Yun ZHANG Bingrui LI Shujuan YU Meisheng ZHAO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2020/01/01
Online ISSN: 1745-1337
Type of Manuscript: PAPER
Category: Analog Signal Processing
machine-to-machine communication, 5G, spectrum sharing, Hopfield neural network, blind detection,
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In this paper, we propose a new scheme which uses blind detection algorithm for recovering the conventional user signal in a system which the sporadic machine-to-machine (M2M) communication share the same spectrum with the conventional user. Compressive sensing techniques are used to estimate the M2M devices signals. Based on the Hopfield neural network (HNN), the blind detection algorithm is used to recover the conventional user signal. The simulation results show that the conventional user signal can be effectively restored under an unknown channel. Compared with the existing methods, such as using the training sequence to estimate the channel in advance, the blind detection algorithm used in this paper with no need for identifying the channel, and can directly detect the transmitted signal blindly.