## 1 Introduction
The main goal is to find optimal solutions for relay selection, power allocation and subcarrier assignment that maximize the system throughput. Aiming to support and attain the green wireless LTE network, an energy-efficient resource allocation scheduler with QoS aware support for LTE network is proposed in [9]. The authors of [10] proposed a two-stage method to solve the inter-cell interference problem. In the first stage, the subcarrier allocation and time scheduling are jointly conducted with sequential users' selection and without considering the interference. The power control optimization is left to the second stage, using a geometric programming method. In [11], energy efficient resource block and power allocation optimal and low complexity suboptimal schemes are presented for OFDMA relay-assisted downlink. Authors use fractional programming to make the non-linear mixed integer problem to convex subtractive problem. In order to reduce the computational complexity of the optimal solution, they present two-stage RB allocation and transmission power control algorithms. The system model of this paper is similar to our model but they use relays (small eNB in that paper) with frequency reuse factor of one, and the users employ maximal ratio combining to maximize the received signal-to-noise ratio (SNR). In our case, we use multiplexing gain instead of the diversity gain at users' end by exploiting the knowledge of their location in relay selection and users association algorithm. We have compared our proposed schemeswith the low complexity energy-efficient resource block and power allocation (LERPA) algorithm 3 and 4 of [11]. Artificial intelligence techniques can be used in highly dynamic and stringent constraint Next-Generation networks. Since machine learning is a most promising technique of artificial intelligence, it can be directly/indirectly employed to achieve the goals of 5 G in cognitive radios, massive multiple-input multiple-output (MIMO), hybrid beamforming, femto/small cells, smart grid, wireless power transfer, device-to-device communications, non-orthogonal multiple access (NOMA) etc. [12]. This paper [12] gives an overview of the applications of machine learning in Next-Generation wireless networks. Specifically, supervised learning techniques are suitable for massive MIMO channel estimations and spectrum sensing, unsupervised learning could be helpful in users grouping and clustering; and reinforcement learning can be applied in resource allocation problems. A detailed review on existing techniques and methods have been provided in [13]. For example, in [14], a cooperative Q-learning approach was applied as an efficient approach to solve the resource allocation problem in a multi-agent network. The quality of service QoS for each user and fairness in the network are taken into account and more than a four-fold increase in the number of supported small cells. The authors in [15], proposed a machine learning framework for resource allocation to determine the optimal or near-optimal solutions based on the learning of the most similar historical scenario. In paper [16], the authors proposed an approximated solution to a wireless network capacity problem using flow allocation, link scheduling, and power control. The Support Vector Machine (SVM) was used to classify each link to be assigned maximal transmit power or be turned off, whereas, the deep belief networks (DBNs) computes an approximation of the optimal power allocation. Both learning approaches have been trained on offline computed optimal solutions. A novel resource allocation method using deep learning to squeeze the benefits of resource utilization was developed in [17]. It was reported that when the channel environment is changing fast, the deep learning method outperforms traditional resource optimization methods. The resource allocation is to be optimized by a convolutional neural network using channel information.