Wireless sensor network rolling horizon signal-to-noise ratio estimation method with packet loss

Abstract

The invention discloses a wireless sensor network rolling horizon signal-to-noise ratio estimation method with packet loss. The method comprises the following steps of: 1) obtaining a state space model of a wireless sensor network by use of a signal-to-noise ratio formula, a power control algorithm and a flow rate control algorithm; 2) modeling the packet loss into a random process of Bernoulli distribution according to the characteristics of packet loss to obtain a wireless sensor network model with packet loss; 3) transforming the rolling horizon signal-to-noise ratio estimation problem into an equivalent minimization problem according to the set rolling horizon window length N and the weight matrixes Pi, Q and R; and 4) solving the minimization problem based on a first-order optimization principle and a random method to obtain a wireless sensor network rolling horizon signal-to-noise ratio estimator, and obtaining the optimal estimated value and the predicted value of the next moment of the signal-to-noise ratio by the estimator. The method disclosed by the invention has a reasonable model and has functions of compensating packet loss and performing online calculation.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a method for estimating rolling time-domain signal-to-noise ratios of wireless sensor networks. Background technique [0002] The signal-to-noise ratio is a key parameter of communication signals and one of the indicators of communication quality. In many occasions in wireless communication, such as the identification of modulated signals, iterative decoding of Turbo Code, power control in mobile communication, adaptive modulation switching, adaptive handover and channel allocation, etc., it is necessary to know the signal-to-noise ratio. value for best performance. Therefore, SNR estimation is an important topic in wireless communication networks. [0003] In recent years, SNR estimation technology has made some progress. Researchers at home and abroad have proposed various SNR estimation algorithms for different channel environments and communication systems....

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to aim at the deficiency that the existing wireless sensor network signal-to-noise ratio estimation method cannot compensate for data packet loss, and the present invention proposes a rolling time domain signal-to-noise ratio estimation method suitable for wireless sensor networks with data loss

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