Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

Donghyeon Kim,In-Ho Lee

doi:10.3390/electronics9101606

Abstract

The proximity-based device-to-device (D2D) communication allows for internet of things, public safety, and data offloading services. Because of these advantages, D2D communication has been applied to wireless communication networks. In wireless networks using D2D communication, there are challenging problems of the data rate shortage and coverage limitation due to co-channel interference in the proximity communication. To resolve the problems, transmit power control schemes that are based on deep learning have been presented in network-assisted D2D communication systems. The power control schemes have focused on enhancing spectral efficiency and energy efficiency in the presence of interference. However, the data-rate fairness performance may be a key performance metric in D2D communications, because devices in proximity can expect fair quality of service in the system. Hence, in this paper, a transmit power control scheme using a deep-learning algorithm based on convolutional neural network (CNN) is proposed to consider the data-rate fairness performance in network-assisted D2D communication systems, where the wireless channels are modelled by path loss and Nakagami fading. In the proposed scheme, the batch normalization (BN) scheme is introduced in order to further enhance the spectral efficiency of the conventional deep-learning transmit power control scheme. In addition, a loss function for the deep-learning optimization is defined in order to consider both the data-rate fairness and spectral efficiency. Through simulation, we show that the proposed scheme can achieve extremely high fairness performance while improving the spectral efficiency of the conventional schemes. It is also shown that the improvement in the fairness and spectral efficiency is achieved for different Nakagami fading conditions and sizes of area containing the devices.

Highlights

Deep learning techniques have been widely integrated in wireless communication systems (WCSs) in order to resolve radio resource management problems and improve system performances with low complexity
We propose a fairness-based transmit power control scheme using convolutional neural network (CNN) with the batch normalization (BN) process for network-assisted D2D WCSs, where the BN scheme is applied to the CNN structure to reduce the learning time and enhance the spectral efficiency
We proposed the CNN-based transmit power control scheme for network-assisted

Summary

Introduction

Deep learning techniques have been widely integrated in wireless communication systems (WCSs) in order to resolve radio resource management problems and improve system performances with low complexity. For network-assisted D2D WCSs, a deep learning-based transmit power control scheme has been proposed in [13,14], where it has been shown that the deep learning-based scheme provides better spectral efficiency than the weighted minimum mean square error (WMMSE) scheme. The deep learning-based power control schemes presented in [13,14] have focused on the improvement in spectral efficiency and energy efficiency Their data-rate fairness is quite poor, as seen in Section 4 of this paper. We propose a fairness-based transmit power control scheme using CNN with the BN process for network-assisted D2D WCSs, where the BN scheme is applied to the CNN structure to reduce the learning time and enhance the spectral efficiency.

System Model and Problem Formulation

Proposed Convolutional Neural Network Structure and Learning Process

Performance Evaluation

Computational Complexity of the Proposed DPC Scheme

Conclusions