A lightweight multi-parameter fusion link quality estimation method

A technology for link quality and link quality indication, applied in the field of lightweight multi-parameter fusion link quality estimation, it can solve problems such as imperfection, inability to take into account accuracy, agility and low overhead, and difficulty in accurately depicting the real quality of the link, etc. problem, to achieve the effect of anti-link transient fluctuation and low overhead

Active Publication Date: 2022-01-14
南京迈一勤电子科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] Existing link quality estimation methods usually use window averaging or standard Kalman filtering. When there are many fluctuations in the link itself, the effect is not ideal.
In addition, the existing methods either use only one physical layer parameter to estimate the link quality, which makes it difficult to accurately describe the real quality of the link, or the multi-parameter fusion algorithm used is too complex or the estimation effect is not ideal, which cannot give consideration to accuracy and agility. requirements such as sex and low overhead

Method used

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  • A lightweight multi-parameter fusion link quality estimation method
  • A lightweight multi-parameter fusion link quality estimation method
  • A lightweight multi-parameter fusion link quality estimation method

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Embodiment 1

[0091] This embodiment provides an off-line training method of a lightweight multi-parameter fusion link quality estimation method, such as figure 2 shown, including the following steps:

[0092]T100: By performing several simulation tests of different link qualities on wireless network nodes, obtain SNR sample sets, LQI sample sets, and NF sample sets of different link qualities respectively, calculate these sample sets to obtain their mean values ​​SNRM and LQIM, and count each Get the PRR set of all tests for the PRR of a test;

[0093] Among them, the PRR set is {PRR 1 , PRR 2 , …, PRR n},

[0094] No. i of tests

[0095] m 0 is the number of packets sent for each test, m i for the first i The number of data packets successfully received during the first test;

[0096] SNRM = {SNR m1 , SNR m2 , …, SNR mn},

[0097] Among them, the first i The average SNR of the tests , m i for the first i The number of packets received during a test, SNR mi for th...

Embodiment 2

[0129] This embodiment provides a lightweight multi-parameter fusion link quality estimation method on the basis of obtaining the best mapping relationship model between WED and PRR through training in Embodiment 1. Please combine figure 1 , image 3 As shown, the method specifically includes the following steps:

[0130] S100: The wireless network link receiving node makes statistics on SNR, LQI and NF every fixed time window, and obtains the SNR, LQI and NF actual measurement sample set of this time window;

[0131] S200: Perform exponentially weighted Kalman filtering on the LQI measured sample set and the SNR sample set respectively to obtain an estimated SNR value and an estimated LQI value;

[0132] Further, step S200 specifically includes the following steps:

[0133] S210: Perform Kalman filtering on the LQI measured samples and the SNR samples respectively,

[0134]

[0135] in, x K ( i ) for the i SNR estimates for time windows SNR K ( i ) or the i LQI...

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Abstract

The invention discloses a lightweight multi-parameter fusion link quality estimation method. The method performs data collection and offline training in advance, and establishes a logistic regression model for link quality estimation; Kalman filtering preprocesses the two physical layer parameters of SNR and link quality indication, which solves the problem of large fluctuations in physical layer parameters obtained by traditional processing methods, and makes the link quality estimation method not sensitive to instantaneous link fluctuations. sensitive. The method of weighted Euclidean distance is used to effectively fuse the two physical layer parameters with low overhead, which solves the problem that the traditional link quality estimation method uses a single physical layer parameter or uses a multi-parameter fusion algorithm that is too complex. On this basis, the fusion parameters are mapped to the packet reception rate through the logistic regression model established offline, and then the quantitative estimation of the link quality is realized.

Description

technical field [0001] The invention relates to the field of wireless communication and network, in particular to a lightweight multi-parameter fusion link quality estimation method in a low-power wireless network. Background technique [0002] Low-power wireless networks represented by wireless sensor networks have been successfully applied in many fields such as military investigation, environmental monitoring, industrial production and medical care. Such networks usually use low-power radio frequency transceivers, so that the wireless link between nodes is greatly affected by the environment, so the link stability is poor and there are more fluctuations. In order to improve the transmission efficiency of the network and minimize the packet retransmission overhead caused by using low-quality links, real-time and accurate estimation of the link quality is required to find the optimal end-to-end route. Therefore, the performance of link quality estimation methods is critica...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/309H04B17/336H04W24/06
CPCH04B17/309H04W24/06H04B17/336Y02D30/70
Inventor 许鸣黄婷
Owner 南京迈一勤电子科技有限公司
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