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

arXiv: https://arxiv.org/abs/2103.14548 · HTML: https://mlsanity.com/paper/2103.14548/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Towards Optimal Power Control via Ensembling Deep Neural Networks](https://mlsanity.com/paper/1807.10025/) (2018)
- [Intelligent User Association for Symbiotic Radio Networks using Deep Reinforcement Learning](https://mlsanity.com/paper/1905.04041/) (2019)
- [Optimal User-Cell Association for Massive MIMO Wireless Networks](https://mlsanity.com/paper/1407.6731/) (2014)
- [Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks](https://mlsanity.com/paper/1608.08313/) (2016)
- [Interference and Coverage Analysis in Coexisting RF and Dense TeraHertz Wireless Networks](https://mlsanity.com/paper/2006.08778/) (2020)
- [Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model](https://mlsanity.com/paper/1601.07795/) (2016)
- [Joint User Association and Resource Allocation in the Uplink of Heterogeneous Networks](https://mlsanity.com/paper/1911.12697/) (2019)
- [Channel Assignment in Uplink Wireless Communication using Machine Learning Approach](https://mlsanity.com/paper/2001.03952/) (2020)
- [User Association and Load Balancing for Massive MIMO through Deep Learning](https://mlsanity.com/paper/1812.06905/) (2018)

## Built on by (most cited first, 4 of 4)

- [Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies](https://mlsanity.com/paper/2208.02249/) (2022)
- [Decentralized Channel Management in WLANs with Graph Neural Networks](https://mlsanity.com/paper/2210.16949/) (2022)
- [Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework](https://mlsanity.com/paper/2306.01787/) (2023)
- [CVaR-Based Variational Quantum Optimization for User Association in Handoff-Aware Vehicular Networks](https://mlsanity.com/paper/2501.08418/) (2025)

## Abstract (arXiv)

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.