  In wireless network, the optimization problems generally have complex
constraints, and are usually solved via utilizing the traditional optimization
methods that have high computational complexity and need to be executed
repeatedly with the change of network environments. In this paper, to overcome
these shortcomings, an unsupervised deep unrolling framework based on
projection gradient descent, i.e., unrolled PGD network (UPGDNet), is designed
to solve a family of constrained optimization problems. The set of constraints
is divided into two categories according to the coupling relations among
optimization variables and the convexity of constraints. One category of
constraints includes convex constraints with decoupling among optimization
variables, and the other category of constraints includes non-convex or convex
constraints with coupling among optimization variables. Then, the first
category of constraints is directly projected onto the feasible region, while
the second category of constraints is projected onto the feasible region using
neural network. Finally, an unrolled sum rate maximization network (USRMNet) is
designed based on UPGDNet to solve the weighted SR maximization problem for the
multiuser ultra-reliable low latency communication system. Numerical results
show that USRMNet has a comparable performance with low computational
complexity and an acceptable generalization ability in terms of the user
distribution.
