Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11543
Title: Deep learning based adaptive bit allocation for heterogeneous interference channels
Authors: Aycan, Esra
Özbek, Berna
Le Ruyet, Didier
Keywords: Bit allocation
Deep neural networks
Heterogeneous networks
Channel state information
Issue Date: 2021
Publisher: Elsevier
Abstract: This paper proposes an adaptive bit allocation scheme by using a fully connected (FC) deep neural network (DNN) considering imperfect channel state information (CSI) for heterogeneous networks. Achieving an accurate CSI has a crucial role on the system performance of the heterogeneous networks. Different quantization techniques have been employed to reduce the feedback overhead. However, the system performance cannot increase linearly with the number of bits increasing exponentially. Since optimizing the total number of bits is too complex for the entire network, an initial step is performed to distribute the bits to each cell in the conventional method. Then, the distributed bits are further allocated to each channel optimally. In order to enable direct allocation for the entire network, a FC-DNN based method is presented in this study. The optimized number of bits can be directly obtained for a different number of bits and scenarios by the proposed approach. The simulations are performed by using various scenarios with different allocation schemes. The performance results show that the DNN based method achieves a closer performance to the conventional approach. (C) 2021 Elsevier B.V. All rights reserved.
URI: https://doi.org/10.1016/j.phycom.2021.101364
https://hdl.handle.net/11147/11543
ISSN: 1874-4907
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
1-s2.0-S1874490721001014-main.pdf1.15 MBAdobe PDFView/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

2
checked on Mar 1, 2024

WEB OF SCIENCETM
Citations

2
checked on Feb 10, 2024

Page view(s)

11,074
checked on Mar 4, 2024

Download(s)

4
checked on Mar 4, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.