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Multi-node access detection and channel estimation method of MMTC system based on deep learning

A technology of deep learning and channel estimation, which is applied in the field of channel estimation, can solve the problem of reconstruction time limited by the number of iterations, and achieve the effects of fast model speed, stable algorithm, and improved speed and accuracy

Active Publication Date: 2018-02-27
BEIJING JIAOTONG UNIV
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Problems solved by technology

These algorithms basically use cyclic iterative optimization to achieve signal reconstruction. The reconstruction time is generally longer due to the limitation of the number of iterations, and the reconstruction accuracy is limited by the nature of the measurement matrix.

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  • Multi-node access detection and channel estimation method of MMTC system based on deep learning
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  • Multi-node access detection and channel estimation method of MMTC system based on deep learning

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Embodiment

[0078] An embodiment of the present invention provides a channel estimation method in an MMTC system based on deep learning, figure 2 Schematic diagram of the sparse system MMTC, such as figure 2 As shown, there are a total of K user nodes in the sparse MMTC communication system. At the same time, there are at most n users in the system that need to send data to the base station, that is, the user's maximum active probability P a =n / K<<1. Active users send their respective pilots, and the base station performs multi-user access detection and joint channel estimation through a compressed sensing algorithm. Then the base station uses the estimated channel state information to estimate the data subsequently transmitted by the user.

[0079] The embodiment of the present invention provides a multi-user sparse access detection and channel estimation method in an MMTC communication system based on deep learning, which includes the following steps:

[0080] Step 1: Determine the initial...

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Abstract

The invention provides a multi-node access detection and channel estimation method of an MMTC system based on deep learning. The method comprises the following steps: determining a pilot frequency sequence of each node according to a modulation scheme adopted by the MMTC, and determining the channel impulse response of each node; generating input data according to certain node activity, further generating a training set and a verification set for training a depth neural network and a test set for testing model performance, designing DNN and BRNN models for detecting active users, performing simulation verification, and performing channel estimation by solving a linear system of equations via a least square method according to a user activity detection result of the model. By adoption of the channel estimation method provided by the invention, under different pilot frequency lengths and different numbers of active users, the accuracy of user access detection is higher than that of the traditional method, and the time of the node access detection is greatly shortened.

Description

Technical field [0001] The present invention relates to the technical field of channel estimation in communication systems, in particular to a method for multi-node access detection and channel estimation of an MMTC system based on deep learning. Background technique [0002] MMTC (Massive Machine-type Communication, large-scale inter-machine communication) is a hot research issue of IoTs (Internets of things). The main feature is that a large number of access nodes only sporadically transmit small data packets at low data rates. The cellular system used in traditional voice communication is mainly designed for high data rate and large data packets. The node and the receiving end follow the rules of access reservation when communicating. The communication characteristics of MMTC determine that this kind of communication rule will lead to control of packet header information The occupied overhead is relatively large compared to the packet information that really needs to be sent, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/02H04W4/70
CPCH04L25/0204H04L25/0254
Inventor 陈为白艳娜
Owner BEIJING JIAOTONG UNIV
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