###### 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. deep learning; transmit power control; spectral efficiency; index of fairness +
Footnote †: journal:  
0000-0001-6000-0000-0000-0000-00  
that the signaling overhead, throughput, and delay of the deep learning system are considerably improved. In [3], a convolutional neural network (CNN)-based learning algorithm has been used for network traffic control, and it has attained significantly low average delay and packet loss rate. In [4], the auto-encoder architecture of deep neural network (DNN)-based learning has been proposed for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing systems, and it has been also shown that the DNN-based learning scheme improves both the PAPR and bit error rate. In [5], DNN-based sparse code multiple access (SCMA) has been proposed for the mapping of data to codebook and decoding of the received signal, where DNN is learned to minimize the bit error rate. It has been also noted that the decoder that is based on DNN requires lower computational complexity than the conventional SCMA decoder. Device-to-device (D2D) communication has attracted great attention as one of the techniques to fulfill the requirements for the 5G communication system in terms of data rate, latency, and spectrum utilization. D2D communication enables devices that are in close proximity to communicate while using a direct link without main infrastructure. Thus, D2D communication techniques allow internet of things, public safety, and data offloading services. 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.