Interference Mitigation by Intelligent Channel Selection for Device-to-Device Communications

Abdelhafez, Anas M.; ElAttar, Hussein M.; Aboul-Dahab, Mohamed A.

doi:10.1109/uemcon47517.2019.8993057

Cited by 1 publication

(2 citation statements)

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“…Because of such advantages, D2D communication has been introduced into wireless sensor networks in order to realize various applications, such as smart cities, smart home, smart grids, healthcare, military, and internet of things applications [6]. However, applying D2D communication into the wireless sensor networks induces technical problems, such as device discovery, mode selection, data security, and interference mitigation [7]. In particular, there are challenging issues on the data rate shortage and coverage limitation due to co-channel interference in the proximity communication.…”

Section: Introductionmentioning

confidence: 99%

“…The importance of radio resource management becomes more significant when devices are dense in WCS, because co-channel interference can be severer. In D2D WCSs, most of the researches have focused on transmit power control for radio resource management since devices typically share the frequency and time resources in D2D communication [6][7][8][9]. In addition, network-assisted D2D WCSs have been considered for further performance enhancement, where a base station or control center obtains information, such as D2D communication channels and manages radio resources for D2D communications [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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

Kim

2020

Electronics

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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.

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Section: Introductionmentioning

confidence: 99%