# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 1 Introduction  
Improving the quality of service (QoS) of mobile broadband networks depends on quality control and network performance monitoring [1, 2]. For recent networks, such as 5G and 4G, network performance can only be monitored by critically analyzing relevant mobile broadband (MBB) metrics such as the reference signal received power (RSRP),reference signal received quality (RSRQ), received signal strength indicator (RSSI), and download throughput, among others. Equally important is a real-life test of how to target user equipment (UE) using the resource block (RB) allocated across different types of devices [3; 4]. Despite the development and implementation of the fifth-generation network (5G) in leading nations of the world by the fall of 2020 [5], developing countries such as Nigeria still linger behind in the infrastructure and hardware required to implement 5G technology. When other factors influencing the adoption and acceptance of technology in society, such as religion and governmental policies, are considered, developing countries in Africa, such as Nigeria, may not have the technological drive nor the capital required to implement 5G technology across the nation very soon. The ever-increasing number of mobile broadband (MBB) subscribers find a ray of hope for better connectivity through the optimization of existing 4G LTE networks widely available across the country through mobile network operators (MNOs) such as the Emirates Telecommunications Corporation (Etisalat) and the Mobile Telephone Network (MTN). As wireless technologies continue to advance and become widely adopted in daily and industrial applications, it is essential to note that this progress is not limited to cellular communications. Recent advancements in wireless technology have also paved the way for the deployment of autonomous vehicles, which can self-drive for several kilometers across different terrains by utilizing low latency and high data rates, particularly from the 4G/5G network, for the establishment of situational awareness [6]. Situational awareness, otherwise known as user localization, is critical to achieving reliable, full autonomy, especially in the establishment of vehicle-to-everything (V2X) communication [7]. As such, the metrics used to monitor MBB networks evaluate mobile communication and establish relevant documentation that can be used to enhance throughput values, especially in dense urban environments where vehicles need to maintain high levels of communication with pedestrians, other vehicles, and devices for safe navigation. Achieving the high throughput and low latency levels specified for 5G networks requires transmission bandwidths of high magnitude. However, the scarcity of available spectra, otherwise known as unlicensed spectra, makes it difficult to sustain these conditions. Millimeter waves (mmWaves) have been used for 5G network deployment, as they offer significantly higher bandwidths than lower frequencies. Unfortunately, the short wavelengths of mmWaves mean they are easily blocked by obstacles, especially in dense urban areas. In practice, massive multiple-input multiple-output (mMIMO) and beamforming techniques have been used to effect directional communications to sustain the operation of the network, especially in dense environments. The work [8] proposes a reduced-complexity algorithm based on a pseudo-maximum likelihood (ML) approach capable of predicting unknown multipath constraints using spatially smoothed adaptive beamforming to arrive at an appropriate likelihood function. This helps to address the problem of direct position estimation (DPE) for multi-antenna receivers, especially in environments where a dynamic multipath is considered.