Optimization of matrix operation of matrix with repeating elements

I want to get exp() of a large matrix (A) with values that repeat at different indices. To speed-up the exp() operation I only perform it on the unique values of A and then reassemble the matrix. However the reassembly of the matrix is quite slow. The following code provides a working example:

% defintion of a grid
gridSp = 5:5:35*5;

X = repmat(gridSp,35,1);
Z = repmat(gridSp',1,35);

% calculation of the distances
locMat = [X(:) Z(:)];
dist=sqrt(bsxfun(@minus,locMat(:,1),locMat(:,1)').^2 +...
bsxfun(@minus,locMat(:,2),locMat(:,2)').^2); 

sizeDist = size(dist);
uniqueDist = unique(dist,'stable');
[~, Locb] = ismember(dist,uniqueDist);

nn_A = exp(1i*2*pi*rand(sizeDist(1),100));
H_A = zeros(size(nn_A));

freq = linspace(10^-3,10,100);

psdA = 4096*length(freq).*10.*4.*22.6./((1 + 6.*freq*22.6).^(5/3));

for jj=1:100 
    b = exp(-8.8*uniqueDist*sqrt((freq(jj)/15).^2 + 10^-7));
    b = b.*psdA(jj);
    A = b(Locb);
    droptol = max(A(:))*10^-10;
    if min(A(:))<droptol
        A = sparse(A);
        HH_A = ichol(A,struct('type','ict','shape','lower','droptol',droptol));
    else
        HH_A = chol(A,'lower');
    end
    H_A(:,jj) = HH_A*nn_A(:,jj);
end

Especially the reassembly of the matrix

A = b(Locb);

and the conversion of the matrix to sparse

A = sparse(A);

in the last for loop take up a lot of time. Is there a quicker way to do this? I have to perfom these operations far more often than the 100 iterations in the example.