2 research outputs found
Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework
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.Comment: accepted in IEEE Transactions on Communication
Deep Unsupervised Learning for Network Resource Allocation Problems with Convex and Non-Convex Constraints
Deep neural networks (DNNs) are currently 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 or base station quota, guaranteeing constraint satisfaction becomes a fundamental challenge. In this thesis, I propose a novel unsupervised learning framework to solve the classical power control and user assignment problem in a multi-user interference channel, where the objective is to maximize the network sum-rate with QoS, power budget, and base station quota constraints. The proposed method utilizes a differentiable projection function, defined both implicitly and explicitly, to project the output of the DNN to the feasible set of the problem. 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, and also outperform the optimization-based benchmarks in computation time