Power Control with QoS Guarantees: A Differentiable Projection-based
  Unsupervised Learning Framework
Mehrazin Alizadeh Hina Tabassum
Deep neural networks (DNNs) are emerging as a potential solution to solve
NP-hard wireless resource allocation problems. However, in the presence of
intricate constraints, e.g., users' quality-of-service (QoS) constraints,
guaranteeing constraint satisfaction becomes a fundamental challenge. In this
paper, we propose a novel unsupervised learning framework to solve the
classical power control problem in a multi-user interference channel, where the
objective is to maximize the network sumrate under users' minimum data rate or
QoS requirements and power budget constraints. Utilizing a differentiable
projection function, two novel deep learning (DL) solutions are pursued. The
first is called Deep Implicit Projection Network (DIPNet), and the second is
called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a
differentiable convex optimization layer to implicitly define a projection
function. On the other hand, DEPNet uses an explicitly-defined projection
function, which has an iterative nature and relies on a differentiable
correction process. DIPNet requires convex constraints; whereas, the DEPNet
does not require convexity and has a reduced computational complexity. To
enhance the sum-rate performance of the proposed models even further,
Frank-Wolfe algorithm (FW) has been applied to the output of the proposed
models. Extensive simulations depict that the proposed DNN solutions not only
improve the achievable data rate but also achieve zero constraint violation
probability, compared to the existing DNNs. The proposed solutions outperform
the classic optimization methods in terms of computation time complexity.