Paper Info
Title
Data and pilot power controls scheme based on convolutional neural network for massive cellular system with underlaid device-to-device communications
Abstract
Studying deep learning framework in massive cellular system to optimize its performance is a challenge task. In this paper, multi-cell massive cellular system with device-to-device (D2D) communication is studied, where D2D pairs and cellular users can share different orthogonal pilots to overcome interference at the cellular base station (BS). We propose data and pilot power controls scheme based on deep learning framework using convolutional neural network (CNN) for cellular and D2D users. The main focus is to obtain the optimal data and pilot powers (i.e., cellular pilot power, pCellular Userpilot$$ {p}_{\mathrm{Cellular}\ \mathrm{User}}<^>{\mathrm{pilot}} $$, cellular data power, pCellular User$$ {p}_{\mathrm{Cellular}\ \mathrm{User}} $$, D2D pilot power, pD2Dpilot$$ {p}_{\mathrm{D}2\mathrm{D}}<^>{\mathrm{pilot}} $$, and D2D data power, pD2D$$ {p}_{\mathrm{D}2\mathrm{D}} $$) for maximum spectral efficiency (SE). SE optimization problem is formulated with the goal of obtaining optimal transmit powers. Also, comparison between the proposed scheme and the iterative power control schemes (i.e., weighted minimum mean square error [MMSE] technique, bisection technique, and technique-based geometric mean per-cell max-min fairness) is provided. Results show that the proposed scheme can increase the sum SE of multi-cell massive cellular system with D2D communications.
Year
DOI
Venue
2022
10.1002/dac.5201
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
Keywords
DocType
Volume
cellular, CNN, D2D, deep learning, massive
Journal
35
Issue
ISSN
Citations 
11
1074-5351
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Osama S. Faragallah102.03
Hala S. El-sayed200.34
Mohamed G. El-Mashed300.68