Deep Unsupervised Learning for Generalized Assignment Problems: A Case-Study of User-Association in Wireless Networks

TL;DR

This paper introduces a deep unsupervised learning method to efficiently solve generalized assignment problems, specifically applied to user-association in wireless networks, achieving near-optimal solutions with reduced computational time.

Contribution

The paper presents a novel deep unsupervised learning framework with a customized loss function and tensor splitting to solve GAP efficiently, especially in wireless network user-association.

Findings

Achieves near-optimal solutions for GAP in wireless networks.

Reduces computational complexity compared to traditional methods.

Demonstrates effectiveness through numerical experiments.

Abstract

There exists many resource allocation problems in the field of wireless communications which can be formulated as the generalized assignment problems (GAP). GAP is a generic form of linear sum assignment problem (LSAP) and is more challenging to solve owing to the presence of both equality and inequality constraints. We propose a novel deep unsupervised learning (DUL) approach to solve GAP in a time-efficient manner. More specifically, we propose a new approach that facilitates to train a deep neural network (DNN) using a customized loss function. This customized loss function constitutes the objective function and penalty terms corresponding to both equality and inequality constraints. Furthermore, we propose to employ a Softmax activation function at the output of DNN along with tensor splitting which simplifies the customized loss function and guarantees to meet the equality constraint. As a case-study, we consider a typical user-association problem in a wireless network, formulate it as GAP, and consequently solve it using our proposed DUL approach. Numerical results demonstrate that the proposed DUL approach provides near-optimal results with significantly lower time-complexity.