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

• URL: http://arxiv.org/abs/2103.14548v1
• Date: Fri, 26 Mar 2021 16:07:02 GMT
• Title: Deep Unsupervised Learning for Generalized Assignment Problems: A Case-Study of User-Association in Wireless Networks
• Authors: Arjun Kaushik, Mehrazin Alizadeh, Omer Waqar, and Hina Tabassum
• Abstract summary: We propose a novel deep unsupervised learning (DUL) approach to solve the generalized assignment problems (GAP) in a time-efficient manner. In particular, we propose a new approach that facilitates to train a deep neural network (DNN) using a customized loss function. Numerical results demonstrate that the proposed DUL approach provides near-optimal results with significantly lower time-complexity.
• Score: 11.42707683459227
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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