neural network isn't training properly. It shows "minimum gradient reached" just after few epochs.

I am trying to write a code for "channel estimation in OFDM using neural networks" but the results of neural network aren't satisfactory. Though I manually changed the gradient to greater value, but stil no improvement. Could anyone please tell me the reason behind it. It is very urgent.

The code is given below:

clc clear all

nCP = 16; %round(Tcp/Ts); nFFT = 64; NT = nFFT + nCP; F = dftmtx(nFFT)/sqrt(nFFT); MC = 2; kk=32; EsNodB = 0:5:31; snr = 10.^(EsNodB/10); beta = 17/9; M = 16; modObj = modem.qammod(M); demodObj = modem.qamdemod(M); L = 5; %ChEstLS = zeros(1,length(EsNodB)); %ChEstMMSE = zeros(1,length(EsNodB)); tic; E=[] % Random channel taps g = randn(L,1)+1i*randn(L,1); g = g/norm(g); H = fft(g,nFFT);

for ii = 1:length(EsNodB) disp('EsN0dB is :'); disp(EsNodB(ii)); ChMSE_LS = 0; ChMSE_LMMSE=0; error=0; error1=zeros(1,32); %for mc = 1:MC

e=[]

     % generation of symbol
        X = randi([0 M-1],nFFT,1);  %BPSK symbols
       % x1 = de2bi(X,4); 
        XD = modulate(modObj,X); % normalizing symbol power
        x = F'*XD;                    %loading
        xout = [x(nFFT-nCP+1:nFFT);x];

% channel convolution and AWGN y = conv(xout,g); nt =randn(nFFT+nCP+L-1,1) + 1i*randn(nFFT+nCP+L-1,1); No = 10^(-EsNodB(ii)/10); y = y + sqrt(No/2)*nt; % Receiver processing y = y(nCP+1:NT); Y = F*y; R = demodulate(demodObj,Y); % b = de2bi(R,4)

for k=1:kk
R1=R';
X1=X';
%net=newff(minmax(R1), [30,20], {'logsig','purelin', 'trainlm'});

 net = feedforwardnet([50 30]);
net=init(net);
net.trainparam.epochs=100; 
%net.trainparam.goal=1e-5;
net.trainparam.lr=0.1;
%net.trainparam.min_grad=1e-10;%No. of iterations
%net.trainFcn='trainrp';
net.divideParam.trainRatio = 0.7; % training set [%]
net.divideParam.valRatio = 0.1; % validation set [%]
net.divideParam.testRatio = 0.2; % test set [%]
[net,tr]=train(net,X1,R1);
%x_net = net(y2);
%error=(x2-x_net);
  dbstop if error
  a1=sim(net,R1);
  a=round(a1);
  error=(a-X1).^2;
  e=[e; error];
end
eav=(mean(mean(e),2))/2;
E=[E;eav];

[n, r(ii)]=biterr(R1,X1); [n1, r1(ii)]=biterr(abs(a),X1); end

toc % Channel estimation figure, semilogy(EsNodB,abs(E/2),'--m','LineWidth',2); hold on legend('MSE'); figure, semilogy(EsNodB,r,'b','LineWidth',2); hold on semilogy(EsNodB,r1,'--r','LineWidth',2); hold on grid on title('Bit Error Rate .VS. Signal To Noise Ratio'); ylabel('BER'); xlabel('SNR [dB]'); legend('withoutNN','withNN');