# Optimization of Coverage and Capacity Using Smart Antennae
## 1 Introduction  
Traditional base stations rely on wired backhaul network technologies, such as fiber optics, Digital Subscriber Line (xDSL), and Hybrid Fibre-coax, as the backend communication solutions. Among these options, fiber optic communication networks have become the primary choice for fixed-point base station backhaul transmission. However, the deployment of fiber optic networks entails lengthy pre-installation procedures, requiring approval from local authorities for road excavation or shared pipeline usage. Moreover, access to the interior of buildings necessitates consent from the property owners, adding to the cost and time required for fiber optic network implementation due to administrative constraints. As a result, the construction and scheduling of fiber optic networks often face challenges and complexities, impeding infrastructure development. To address these issues, wireless loop solutions have been increasingly discussed and proposed, leveraging the characteristics of wireless networks to reduce the need for civil engineering during network deployment. By doing so, not only can the costs of construction be lowered, but also the speed of base station equipment installation can be increased. In particular, for communication infrastructure requirements in the mountainous areas of Taiwan, a wireless backhaul network presents an excellent option, as it eliminates the need for physically laying cables, facilitating the provision of wireless internet services to remote regions while ensuring uninterrupted connectivity, even in the face of natural disasters. Additionally, the same infrastructure can serve as a vital relay point for activities such as landslide monitoring, forest fire detection, and environmental monitoring. In recent years, artificial intelligence methods have been applied in various fields to solve practical problems. In particular, issues such as network resource allocation and base station configuration, which belong to the realm of mathematical optimization theory, can be addressed using deep learning methods [1; 2]. The integration of Internet of Things (IoT) technologies plays a significant role in various applications, relying heavily on signal coverage rates and data transmission rates in network communications [3; 4; 5; 6]. Therefore, this paper aims to investigate the maximization of base station coverage in wireless backhaul networks. Signal interference remains a primary concern for base stations, encompassing two main scenarios: inter-cell interference from large-scale base stations [7] and co-channel interference between micro base stations [8; 9]. These interferences can lead to adverse effects on user network quality, causing delays, disconnections, and signal loss. To address the first scenario, the 3 rd Generation Partnership Project (3 GPP) introduced Enhanced Inter-Cell Interference Coordination (eICIC) to reduce interference between macro and small cells by implementing the Almost Blank Sub-frames (ABS) mechanism. However, the issue of mutual interference between base stations has not yet reached a unified standard approach. Currently, the most common method to control wireless signal interference between base stations is adjusting the transmission power of the base stations. Numerous studies have proposed algorithms to mitigate inter-cell interference, with the ICIC [10] and Coverage and Capacity Optimization (CCO) [11] solutions from 3 GPP release 8 being the most representative ones. Furthermore, due to the limited spectrum resources, some researchers have developed frequency reuse methods to allow the reuse of the same frequency bands. This is because when adjacent base stations transmit using the same frequency, it can lead to interference. In situations with limited bandwidth, allocating the optimal frequency bands to base stations helps mitigate interference among them [12]. However, traditional inter-cell interference coordination mainly focuses on coordinating interferences between macro base stations and resolving edge interference problems.