## 5 Related Works and Discussion  
In a 5G based on the dense deployment of small-cell C-RAN architecture, a huge number of low-latency fiber-based front-haul connections (e.g., dark fiber [10]) are required to connect small-cell RRUs residing in different locations to the BBU pool or cloud. This increases the deployment cost of small cell in C-RAN architecture. Front-haul transport network sharing is a potential method to address C-RAN deployment cost issues in the 5G era. A variety of front-haul transport solutions that support network sharing have been proposed in the literature. The main proposed solutions are: Passive wavelength-division multiplexing (WDM) [27], WDM/optical transport network (OTN) [28], WDM/PON [8], and Ethernet [29]. This paper considers low-cost shared front-haul transport solutions based on TDM-PONs, which are classified as a type of WDM/PON solution. In particular, the paper focuses on optimizing XGS-PON networks to meet the latency requirement of C-RAN while maintaining a high degree of bandwidth efficiency. The main contribution of this paper is introducing a traffic prediction bandwidth allocation solution to overcome the latency and bandwidth efficiency problems of mobile front-haul in C-RAN. This solution takes advantage of the nature of the scheduling process in an LTE network to formulate the traffic prediction bandwidth allocation problem; then, it applies a machine leaning tool to solve this problem.  
Machine learning tools have been widely used for traffic prediction in passive optical network and other networking contexts. For example, in [30] a traffic prediction method employing a least mean square (LMS) algorithm was utilized to reduce the burst delay at the edge nodes in optical packet switching networks. In [31] a nonlinear auto-regressive (NAR) neural network is implemented for forecasting video bandwidth requirements (i.e., grant requests) to improve the efficiency of the DBA algorithm in Ethernet passive optical networks (EPONs) to support high-definition video transmission. In [32] several traffic estimation methods such as Gaussian approximation, Courcoubetis approximation and leaky bucket (LB) algorithms have been proposed to optimize PON networks to transport mobile base stations' backhaul traffic with a high degree of bandwidth utilization and a QoS guarantee. In [3] a traffic estimation method utilizing simple statistical traffic analysis function is used as a dynamic bandwidth allocation method to accommodate the bursty nature of the front-haul traffic in TDM-PON-enabled C-RAN architecture. To the best of our knowledge, this is the first time a neural network-based traffic prediction approach has been employed to deal with the front-haul latency issue in C-RAN. Fortunately, our simulation results show that the neural network-based traffic prediction approach not only satisfies the delay requirement of mobile front-haul for C-RAN architecture but also improves other network performance metrics such as upstream utilization, jitter, and packet losses. Although in our current proposal we consider the XGS-PON system (i.e., a single-wavelength PON), in our future work we will consider more complex PON networks (i.e., multi-wavelength networks such as WDM-PON and TWDM-PON), taking into consideration the physical non-linearity of the system described in [33; 34].