Dear Muhammad Karam,

I suggest yoou to see links and attached files in topic.

- Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier ...

https://www3.nd.edu/~mhaenggi/pubs/tut13.pdf

-Load-Aware Modelling in Cellular Networks with Repulsively... https://www.researchonline.mq.edu.au/vital/access/.../SOURCE1

- Intelligent On-demand Radio Resource Provisioning for Green Ultra ...

etheses.whiterose.ac.uk/.../PhD%20Thesis%20-%20Zhehan%20Li%20-%20Recheck.pdf

-Spectral and Energy Efficiency in 5G Wireless Networks - Tel Archives... https://tel.archives-ouvertes.fr/tel-01476080/document

Best regards

Thanks a lot, sir. @Mohamed-Mourad Lafifi

Here is the answer to my question. Use following MATLAB code to get points using Matern type-I hard core process.

% Copyright(c) 2019 Muhammad Karam Shehzad.

% All Rights Reserved.

% Kindly report any suggestions or corrections to:

% [email protected]

%---------------------------------------------------------------------------%

% output:

% 1. Distribution of points uing Matern Type-I hard core process %

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function RemainingNodes = RemoveNearNodesSBSs (Lamda,Area,MinDistance)

display('Total Points are')

N=Lamda*Area; %Avg Number of SBSs, Lamda is the intensity. e.g., 3e-6

n=poissrnd(N);

pproc=rand(n,2).*5000; %PPP

RemainingNodes=pproc;

size_PPP=size(RemainingNodes);

distances = pdist2(RemainingNodes, RemainingNodes);

closePoints = distances > MinDistance; %Find which points are greater than Min. Distance (e.g., 100m) from each other

indexToRemove(1,n+n)=0; %creating variable lets n+n(make bigger if it is not enough i.e n*n) preallocating with zeros

index=1; %index count

for row =1:n

y=find(~(closePoints(row,(row+1:end)))); % checking on upper triangle,"~" is for finding position where 0's are ,

[~,n]=size(y);

if(n~=0)

indexToRemove(1,(index:n+index-1))=y+row; % finding which points to remove "+row+ is due to upper triangle index shift , assigning those indexes as values 1 to avoid repeatition

index=index+n;

end

end

indexToRemove(:, all(~indexToRemove,1) )=[];

%now removing those indexes

RemainingNodes(indexToRemove,:)=[];

end

%%%%Now call this function%%%%

RemainingNodes = RemoveNearNodesSBSs (3e-6,25*((1e3))^2,100);

plot(RemainingNodes(:, 1), RemainingNodes(:, 2), 'o');

Thank you for sharing your answer. Your code reminding my work, I was doing this in another way. I check the point if it needs to be removed at the beginning. That is, I regenerate a new point and compared the new one with the former points.

Here is a blog that prof. H. Paul Keeler share his code also.

https://hpaulkeeler.com/?s=cluster

Both generation can have the same result. The definition is to thinning on the set such that any point is retained in hard-core PP if it has the lowest mark with radius r. Are the both approaches can be seen as the same?

Appreciation your work! It is helpful!

On that post I actually describe how to simulate a Matérn *cluster* point process, not a Matérn hard-core point process. (Matérn proposed at least 4 different point processes.) I've now had a remark to reduce confusion.

By chance, I actually wrote code that simulates Matérn *hard-core* point processes Type I and II for this paper:

Preprint Determinantal thinning of point processes with network learn...

The code is here:

https://github.com/hpaulkeeler/DetPoisson_MATLAB

See the file SubsetGenerate.m

The code is long, but most of it is not for simulating the hard-core point processes. The relevant section is (roughly) between lines 162 and 194.

Perhaps I'll write a short version of the code.

Incidentally, one has to simulate the underlying Poisson point process on an extended simulation window (to account for edge effects), as it's possible that a point is thinned (ie removed) due to a Poisson point *outside* the simulation window. I don't see that in the code by Muhammad Karam Shehzad.

I have written a short post outlining how to simulate (or sample) both the Type I and Type 2 Matérn hard-core point processes:

https://hpaulkeeler.com/simulating-matern-hard-core-point-processes/

As I wrote above and in the post, what is important is simulating (or sampling) the underlying Poisson point process on an extended window.

The code in MATLAB and Python is here:

https://github.com/hpaulkeeler/posts/tree/master/MaternHardcoreRectangle

Hi Dear

The attached files are the files that I used to simulate my results in the IEEE Access paper:

https://bit.ly/2vZRTxz

The files run_this.m file will generate two types of clusters:

1- Thomas cluster (Gauss).

2- Materns Cluster.

If you read the files line by line you will understand how it works.

So you can extend and modify them to reach your goal.

I wrote these files my self, so please any credit is for me.

Please do not hesitate to contact me or share the files to anyone.

Best wishes

Ali Hayajneh

Could you check my papers, reply to your question Muhammad Karam Shehzad