###### Abstract
In D2D WCSs, radio resources, such as transmit power, frequency bandwidth, and time slot, can be properly managed in order to improve the data rate and the coverage. 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]. For network-assisted D2D WCSs, a deep learning-based transmit power control scheme has been proposed in [13; 14], where it has been also shown that the deep learning-based scheme provides better spectral efficiency than the weighted minimum mean square error (WMMSE) scheme. The WMMSE scheme has been proposed to maximize distributed sum-utility for a multiple-input multiple-output (MIMO) interfering broadcast channel [15], where transmit weights are iteratively updated for the sum-utility maximization. Such an iterative algorithm to update the weights can be similar to a deep learning algorithm, in that deep learning is a method for optimizing a loss function by updating the weights' values inside the neural network. However, the deep learning scheme needs lower computational complexity than WMMSE, because it uses the neural network that is trained by previous channel realizations. The power control scheme in [13; 14] can limit the spectral efficiency due to a local optimum problem of deep learning. Hence, in this paper, we present CNN while using the batch normalization (BN) scheme [16] to resolve the problem, which achieves better spectral efficiency than the conventional scheme. In addition, the deep learning-based power control schemes presented in [13; 14] have focused on the improvement in spectral efficiency and energy efficiency. Thus, their data-rate fairness is quite poor, as seen in Section 4 of this paper. Therefore, we propose a fairness-based deep learning algorithm while using CNN with the BN scheme. It is noted that the data-rate fairness between devices may be a key performance metric in D2D communications, since sensors or devices, which are located in a certain area and use the same WCS, may anticipate a fair quality of service. In addition, to the best of our knowledge, a deep learning-based transmit power control technique for D2D WCSs considering fairness performance has not yet been investigated. The contributions of this paper can be summarized, as follows:  
1. 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. 2. We define a loss function for the deep-learning optimization in order to maximize both spectral efficiency and data-rate fairness, where the priority between spectral efficiency and data-rate fairness are adjusted by the loss function parameter. 3. We present a pertinent value of the loss function parameter that maximizes the spectral efficiency while achieving the perfect data-rate fairness. 4. We provide simulation results of spectral efficiency, fairness, and computational time in the presence of co-channel interference from devices and cellular users assuming independent wireless channels with path loss and Nakagami-\(m\) fading effects. 5. From the simulation results, we verify that the proposed scheme is superior to the conventional deep-learning scheme and the WMMSE scheme in terms of the spectral efficiency as well as fairness. The remainder of this paper is organized, as follows. Section 2 describes the system model and problem formulation. Section 3 proposes the CNN structure and deep learning method.