Channel associated shadow fading construction method based on stationary random process

Abstract

The invention provides a channel associated shadow fading construction method based on a stationary random process, relating to the field of wireless communication. A time varying channel associated shadow fading model construction method base on a stationary random process comprises a step 101 of segmenting a time-varying channel into a plurality of stationary intervals according to an actual transmission environment, a step 102 of determining the mean value and root-mean-square of the shadow fading in each stationary interval, and a step 103 of simulating the shadow fading with correlation in the stationary interval according to the stationary random process time varying channel associated shadow fading model.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a method for constructing channel-related shadow fading based on a stationary random process. Background technique [0002] With the development of wireless communication technology, there is an urgent need for wireless operators to provide people with effective, reliable and safe wireless communication services "at any time" and "at any place". In the past, wireless communication access services were mainly aimed at accessing fixed or slow moving scenarios, such as indoor wireless coverage and wireless coverage in dense urban areas. Higher hopes are placed on broadband access services on rapid transportation vehicles such as subways (or urban rails) and expressways. [0003] Compared with fixed wireless access, the biggest difference in wireless communication access under high-speed mobile conditions is due to the Doppler brought by the movement (including the re...

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Problems solved by technology

[0012] However, there is a problem when the traditional autocorrelation model is used: within the scope of observation, the shadow fading correlation values ​​between observation points are different, this is because the correlation coefficient model of (1) is a negative distribution, each The correlation coefficients between the points are different

However, when the autocorrelation shadow model is actually needed for simulation, the initial point x' during simulation will be found 0 often not equal to x 0 , often x' 0 > x 0 , therefore, if you still use (1), when you need to simulate |vt-x' 0 When the point of |, there will be a problem with the normalization of the initial point, it is impossible to determine whether it is x 0 = 0 or for x' 0 normalize

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