Have I done the modeling correctly. Getting Infeasible solution

This post corresponds to https://or.stackexchange.com/questions/5271/how-can-i-convexify-allowed-some-approximation-the-objective-function

I want to maximize

\sum_{u,b} D_{u,b} H_{u,b} T_u

subject to

\sum_b H_{u,b} T_u - \sum_b D_{u,b} H_{u,b} T_u = 1 for each u.

Introducing Y_{u,b} = T_u\cdot D_{u,b} to linearize both objective and constraint:

we have

\text{maximize} \sum_{u,b} H_{u,b} Y_{u,b}

subject to

\sum_b H_{u,b} T_u - \sum_b H_{u,b} Y_{u,b}=1}
Finally, we linearize the relationship between Y and D.

I am having the following script

            variable T(U)
            variable D(U,B) binary
            variable Y(U,B)

I model objective function as

           maximize  sum(sum(H.*Y))

And the constraints as

  for u=1:U
      sum(H(u,:).*T(u))-sum(Y(u,:).*H(u,:))==1;
  end
  
  for b=1:B
      
      for u=1:U
          
          Y(u,b)<=D(u,b).*M;
          Y(u,b)>=0;
          Y(u,b)<=T(u);
          Y(u,b)>=T(u)-(1-D(u,b))*M;
          
      end
      
  end

But I am getting infeasible solution. Have I modeled the constraints correctly?