Learning-Based Resource Allocation with Dynamic Data Rate Constraints
Pourya Behmandpoor, Panagiotis Patrinos, Marc Moonen
Abstract
In this paper, we address the problem of resource allocation (RA) in wireless communication networks, where each user has a dynamic data rate constraint. The objective of RA is to maximize the sum rate (SR) of the users while satisfying the data rate constraints in expectation. For a given set of data rate constraints, a suitable probability distribution for the activation of users is found iteratively with a stochastic gradient descent (SGD) approach to satisfy the data rate constraints in expectation. At each time instant, RA amongst the randomly activated users is performed noniteratively by a centralized deep neural network (DNN). Simulations show that the proposed approach is convergent and not only can consider dynamic data rate constraints accurately, but also that it achieves a SR higher than that of the conventional geometric programming (GP) method. The proposed approach can open up a direction of research for cross-layer RA in the current deep learning-based RA context.
BibTeX
@inproceedings{icassp2022_learningbasedres,
title = {Learning-Based Resource Allocation with Dynamic Data Rate Constraints},
author = {Pourya Behmandpoor and Panagiotis Patrinos and Marc Moonen},
booktitle = {ICASSP 2022},
year = {2022}
}