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A method and system for lora splicing communication based on segmentation neural network decoding

A technology of neural network and communication method, which is applied in the field of LoRa splicing communication method and system based on segmentation neural network decoding, which can solve the problems of short actual life of LoRa network, achieve low power consumption, reduce power consumption, and improve performance

Active Publication Date: 2022-07-12
NORTHWEST UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

At the same time, through the analysis of the actual application situation, the actual life of the LoRa network is usually less than the theoretical expectation

Method used

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  • A method and system for lora splicing communication based on segmentation neural network decoding
  • A method and system for lora splicing communication based on segmentation neural network decoding
  • A method and system for lora splicing communication based on segmentation neural network decoding

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Embodiment 1

[0068] This embodiment proposes a LoRa splicing communication method based on segmented neural network decoding, including the following steps:

[0069] Step 1: The standard LoRa signal data packet contains the following parts: the pilot frequency used for signal detection, namely Preamble, the start frame delimiter (SFD) used for signal synchronization, and the payload data part that records the original transmission information Payload. The payload data part Payload consists of individual chirp signals. To ensure signal detection and synchronization, the standard headers in LoRa are reserved and do nothing. The first part of each chirp signal is cut out at an appropriate length according to the current signal-to-noise state of the communication, and the cut length is equal to the original length of the splicing rate sr*chirp signal. The optimal choice of splicing rate sr in different communication environments is shown in Table 1. Then the intercepted signal is re-spliced ...

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Abstract

The invention discloses a LoRa splicing communication method and system based on neural network decoding. Considering the problem that the LoRa communication throughput is too low and the actual lifespan of the node is lower than its official data, a sender splicing transmission is proposed, and the receiver sends through multiple Eigen neural network decoding for communication systems. Step 1: Signal transmission; with the chirp signal as the processing unit, the gateway calculates the signal-to-noise ratio of the original LoRa signal sent by the node to obtain the corresponding splicing rate, and cuts and splices the chirp signal according to the splicing rate to form a new Payload signal. The Payload signal and the Preamble signal are spliced ​​to form a new LoRa signal, and the node sends the new LoRa signal to the gateway; Step 2: Signal reception; Step 3: Model training; Step 4: Use the neural network training model obtained in Step 3 to perform the received LoRa signal decoding. The invention greatly reduces the transmission power consumption of LoRa and improves the throughput.

Description

technical field [0001] The invention belongs to the field of communication, and in particular relates to a LoRa splicing communication method and system based on segmentation neural network decoding. Background technique [0002] LoRa is based on linear spread spectrum modulation technology, and each signal it modulates, namely chirp, is a sin wave whose frequency linearly increases (upchirp) or decreases (downchirp). In the frequency band (-BW / 2, BW / 2) the frequency of the chirp starts from the initial frequency f 0 Starts a linear ramp up to bw / 2, then back to the lower bound of the band -bw / 2, sweeping the entire bandwidth. Different chirps correspond to different starting frequencies, with a total of 2^SF species. During demodulation, the original chirp is pulse-compressed to obtain the starting frequency. Since upchirp and downchirp have a conjugate relationship, we can directly use upchirp and standard downchirp to multiply here, and do Fourier transform to get it he...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W4/70H04W24/02G06N3/04G06N3/08
CPCH04W4/70H04W24/02G06N3/08G06N3/045Y02D30/70
Inventor 徐丹胡王倩任仲远孙雪陈少杰王薇房鼎益陈晓江
Owner NORTHWEST UNIV
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