DL-CNN (deep leaning-convolutional neutral network) demodulator for super-Nyquist rate communication

A demodulator and rate technology, applied in the field of communication, can solve the problems of low spectrum utilization and energy utilization, and achieve the effect of improving energy utilization, improving spectrum utilization, and high receiving signal-to-noise ratio

Active Publication Date: 2015-07-29
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0021] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a high-performance demodulator capable of directly demod

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  • DL-CNN (deep leaning-convolutional neutral network) demodulator for super-Nyquist rate communication
  • DL-CNN (deep leaning-convolutional neutral network) demodulator for super-Nyquist rate communication
  • DL-CNN (deep leaning-convolutional neutral network) demodulator for super-Nyquist rate communication

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Embodiment Construction

[0069] The present invention will be further described below in conjunction with the accompanying drawings.

[0070] 1. System description

[0071] A specific embodiment of the present invention is for the DL-CNN anti-intersymbol interference demodulation of the simplified band-limited EBPSK modulation signal under the additive white Gaussian noise (AWGN) channel, and its system block diagram can be found in image 3 , the left side of the channel is a super-Nyquist rate modulator, and the right side of the channel is a DL-CNN demodulator that resists intersymbol interference.

[0072] Here the super-Nyquist rate modulator is structurally composed of a modulator 1 and a superband-limited filter 2, where the modulator 1 can be any system (such as phase shift keying, frequency shift keying, quadrature amplitude modulation, etc.) , base (binary, multi-ary) and rate conventional modulator, and the super-band-limit filter 2 can also be any form of band-pass filter, but whether the...

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Abstract

The invention discloses a DL-CNN (deep learning-convolutional neutral network) demodulator for super-Nyquist rate communication. For digitally modulated signals via a strict band-limited channel and even more than Nyquist rate bandwidth limitation, the demodulator directly extracts signal features including intersymbol interference from received signal samples subjected to filtering by a shock filter or an ordinary bandpass filter at the receiving end by the aid of a CNN, the CNN is trained by the aid of DL, and associated decision is made by adopting a single symbol element or multiple symbols, so that the signal samples can be classified by the DL-CNN under the higher intersymbol interference environment, intersymbol interference demodulation of the super-Nyquist rate modulation signals is realized, and good modulation performance and higher adaption capability are achieved as compared with a conventional amplitude integral judgment modulator.

Description

technical field [0001] The invention relates to the field of communication, in particular to the problem of decision demodulation of inter-symbol interference modulation signals when the communication symbol rate exceeds the Nyquist rate. Background technique [0002] With the continuous development of information technology, broadband mobile communication and smart phones, not only the radio spectrum has become a scarce resource, but also the communication base stations all over the country consume more than tens of billions of kWh every year. Therefore, the modern information society urgently needs to have high spectrum utilization. An information transmission system with high efficiency and high energy utilization. [0003] 1. Super Nyquist rate [0004] In the field of digital communication, the baud rate is the modulation rate, which refers to the rate at which the effective data signal modulates the carrier, which can be understood as the number of symbols (also calle...

Claims

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Application Information

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IPC IPC(8): H04L1/00
CPCH04L1/0059
Inventor 吴乐南欧阳星辰
Owner SOUTHEAST UNIV
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