Hi,
Yes, it is possible to modify the 'phased.FreeSpace' function in MATLAB to accommodate the lognormal path loss and shadowing models, and use it to estimate the path loss exponent and distance in a short-range wireless network. Here are the steps that you can follow:
- Create a custom function in MATLAB that takes the input parameters required for the lognormal path loss and shadowing models, such as the frequency, antenna heights, and shadowing standard deviation, and returns the path loss exponent and distance between the transmitter and receiver.
- Use the 'phased.FreeSpace' function as a template for your custom function. Create a copy of the 'phased.FreeSpace' function and rename it to a descriptive name, such as 'pathLossEstimator'.
- Modify the copied function to include the lognormal path loss and shadowing models. You can add additional input parameters and processing steps as needed to implement these models. For example, you could add an input parameter for shadowing standard deviation and use it to compute the received power with a lognormal distribution.
- Test your custom function with simulated or measured data to verify its accuracy and compare it with the 'phased.FreeSpace' model. You can also use different statistical methods to improve the accuracy of your model.
Here is the sample code implementation:
function [PL_exp, d] = pathLossEstimator(pt, freq, ht, hr, shadowingStdDev, Pr)
c = physconst('LightSpeed');
a = ((4*pi*ht*hr)/lambda)^2;
Pr_ref = pt*getFreeSpaceLoss(freq, ht, hr);
PL_exp = log10(Pr_ref/Pr)/(10*log10(d_ref/d));
Pr_lognormal = Pr_ref/(d^PL_exp);
Pr_shadowing = Pr_lognormal + shadowingStdDev*randn();
Pr_real = abs(Pr_shadowing);
d = sqrt((pt*Pr_real*a)/(4*pi*Pr_ref));
Note that this is just an example implementation and that there might be various factors to consider while optimizing the models based on different scenarios.
Hope it helps!
