How to use CVX solving optimization problem with coordinate desent algorithm?

when I use cvx to solving a easy optimization problem, the correct solution can be obtained. However, using cvx with coordinate desent algorithm, I can’t get a solution.
I have two optimization variables, and after fixing one variable to find the optimal solution, I fix the other variable to find the optimal solution. However, after optimizing once, the following variables will not change, and the optimal solution will not change. The code I wrote in matlab is roughly as follows:

        ed = 1e-3;
        eh = 20;
        pe = 6;
        M = 25;
        N = 20;
        iterations = 100;
        HbPre = rand(M, N, "like",1i);
        HbMeasured = rand(M, N, "like",1i);
        Hd = rand(M, N, "like",1i);
        pdB = rand(M, 1, "like",1i);
        for i = 1:iterations 
            cvx_begin quiet
                variable wCur(N) complex
                minimize(norm(HbPre * wCur - pdB))
                subject to
                    norm(Hd * wCur) <= sqrt(ed);
                    norm(wCur) <= sqrt(eh);
            cvx_begin quiet
                variable HbCur(M, N) complex
                minimize(norm(HbCur * wCur - pdB))
                subject to
                    norm(reshape(HbCur - HbMeasured, M * N, 1)) <= pe;
            HbPre = HbCur;

That is, no matter how many iterations, HbCur will never change, so am I writing the coordinate descent algorithm wrong?

Remove quiet and look at the CVX and solver output so you will see better what is going on.

Even if you implemented it 'correctly", which I haven’t really checked, see