MIMO-SCFDE (Multiple Input Multiple Output-Synchronized Frequency Division Multiplexing Element) self-adaptive transmission method based on model-driven deep learning

A MIMO-SCFDE, adaptive transmission technology, applied in the field of intelligent communication, can solve the problems of low throughput and reliability

Active Publication Date: 2021-01-15
QILU UNIV OF TECH
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0004] The present invention solves the problem of low throughput and reliability caused by the rule-based scheme in the adaptive transmission method of the existing multiple-input multiple-output single-carrier frequency domain equalizati

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  • MIMO-SCFDE (Multiple Input Multiple Output-Synchronized Frequency Division Multiplexing Element) self-adaptive transmission method based on model-driven deep learning
  • MIMO-SCFDE (Multiple Input Multiple Output-Synchronized Frequency Division Multiplexing Element) self-adaptive transmission method based on model-driven deep learning
  • MIMO-SCFDE (Multiple Input Multiple Output-Synchronized Frequency Division Multiplexing Element) self-adaptive transmission method based on model-driven deep learning

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Embodiment Construction

[0029] The present invention will be described in further detail below in conjunction with the examples, but the protection scope of the present invention is not limited thereto.

[0030] The invention relates to a MIMO-SCFDE adaptive transmission method based on model-driven deep learning, and the model-driven MIMO-SCFDE system model is as follows figure 1 As shown, the method includes the following steps.

[0031] Step 1: Generate the data set required for the depth model based on the MIMO-SCFDE wireless communication system framework. The feature information of the data set comes from the characteristics of the received signal extracted at the receiving end. The labels identified by adaptive modulation and adaptive modulation mode are the combination of different inter-antenna modulation modes and the four modulation modes of BPSK, QPSK, 16QAM and 64QAM, respectively.

[0032] In the step 1, the specific implementation process of the MIMO-SCFDE wireless communication syste...

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Abstract

The invention relates to an MIMO-SCFDE self-adaptive transmission scheme based on model-driven deep learning. According to the method, a self-adaptive transmission model is established based on an MIMO SCFDE system. AMNet and ADNet are adopted to replace a signal modulation part and a modulation identification part in a traditional system respectively. The AMNet adopts a combined network taking a2D CNN, an LSTM and an FCDNN as sub-networks to form an integrated neural network model, a modulation mode of a sending end is adjusted according to a channel condition of a receiving end, feature information extracted from a received signal is input into the plurality of sub-networks, and conversion between features and an optimal modulation scheme are achieved according to network parameters obtained by training. Meanwhile, the receiving power under different path delays is selected as an adaptive factor to achieve adaptive integration of each sub-network result. The ADNet completes adaptiveselection of a modulation identification scheme based on the complexity of a cyclic spectrum according to the advantage that the cyclic spectrum has accurate detection on the signal type under a lowsignal-to-noise ratio. The system is more suitable for performance requirements of a 5G communication system.

Description

technical field [0001] The invention relates to the field of intelligent communication, in particular to a MIMO-SCFDE adaptive transmission method based on model-driven deep learning. Background technique [0002] Adaptive transmission technology refers to the technology that the transmitter of the system uses channel state information (CSI) to adaptively adjust the transmission strategy, including changing the transmission power, adjusting the modulation mode or adjusting the channel coding scheme, thereby improving the information transmission rate or reliability. Most of the traditional adaptive transmission technologies use complex algorithms to improve the performance of communication systems. However, for 5G communications that require high efficiency and high density, the increase in computational complexity will inevitably reduce the effectiveness of communications. With the rise of artificial intelligence technology, deep learning as an advanced data processing alg...

Claims

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Application Information

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IPC IPC(8): H04B7/0413H04L27/00G06N3/08G06N3/04G06K9/62
CPCH04B7/0413H04L27/0012G06N3/049G06N3/08G06N3/045G06F18/2135
Inventor 李军尚李杨张志东于印长乔元健付文文韩永力
Owner QILU UNIV OF TECH
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