Why Is My Massive MIMO Partial LSE Curve Not Smooth?

[thestrong]

I am not seeing why my curve is not smooth. I normalized the data so it is not due to that. The partial LSE just assumes all other channels are part of the noise term (i.e. it performs worse than the full LSE model).

clear all;
close all;
clc;

%Parameters
M = 5; %base station antennas
K = 8; %number of users
tau = 4; %pilot length
SNR = 1:30; %range of SNR
TRIALS = 10000;
MSE = 0;

%pilots
% s_i = randn(tau, K);
% s_i = ones(tau, K); %all pilot sequences are non ortho
% s_i = 2*(randi(2, tau, K)-1) -1;
% s_i = s_i/tau;
% s_i = randi(2, tau, K) -1;
s_i = [1, 1, 1, 1, 1, 1, 1, 1;
  1, 1, 1, 1, -1, -1, -1, -1;
  1, 1, -1, -1, 1, 1, -1, -1;
  1, -1, 1, -1, 1, -1, 1, -1;]

% s_i = triu(s_i)
% for j = 1:K
%  v = s_i(:, j);
%  for i = 1:j-1
%  R(i, j) = Q(:,i)'* s_i(:,j);
%  v = v - R(i, j)*Q(:,i);
%  end
%  R(j,j) = norm(v);
%  Q(:, j) = v/R(j,j);
%  
% end
for i = 1:K
  S_i(:, : , i) = kron(s_i(:,i), eye(M));
end

for n = 1:size(SNR, 2);
  
  for t = 1:TRIALS

  %channel vector (not needed based on model)
  h_i = randn(M, K);

  %noise vector
  z_i = 1/(sqrt(SNR(n)*2))*(rand(tau*M, 1) + 1i * rand(tau*M, 1));
  
  %MSE
  p_wbe = (K - tau)/((K-1)*tau); %using WBE, (roe_ij)^2
  sum_hj = sum(h_i(:,2:end),2);
  MSE_t = p_wbe*sum_hj + S_i(:, :, 1).'*z_i;
  MSE_t = norm(MSE_t)^2;
  MSE(TRIALS, n) = MSE_t;
  end %end Trialsend %end SNR

MSE = sum(MSE,1)/TRIALS
MSE_dB = 10*log10(MSE)

figure, plot(MSE)
figure, plot(MSE_dB)

 

[Baluncore]

Welcome to PF.
I am sure that all here thank you for your confidence in our ability to guess what you are doing but;
What is a massive MIMO ?
What is a partial LSE ?
In what language did you write your code ?

 

[donpacino]

Looks like matlab.
I'm guessing MIMO is multi input multi output?

thestrong, can you tell us more about the application and what you expect to see and why?
also not everyone has MATLAB so it might be useful to post the graphs