# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 1 Introduction
Although this paper seeks to extensively address the communication lapses common in MBB networks in the areas considered, it is noteworthy to state that, as in the case of autonomous vehicles, recent networks such as the 5G network find application in a wide variety of fields, including virtual reality, augmented reality, remote surgery, and very high-quality video streaming [9]. There are three main categories of 5G use cases: enhanced mobile broadband (eMBB), ultra-reliable low latency (URLLC), and massive machine-type communication (mMTC). While eMBB focuses on providing faster download and upload speeds and improving network capacity to support high-bandwidth applications such as streaming video, online gaming, and virtual reality, URLLC focuses on supporting low-latency and high-reliability applications such as autonomous vehicles, industrial automation, and remote surgery. Finally, mMTC supports large-scale deployments of connected devices and sensors, such as smart cities and the internet of things (IoT). An abundance of literature exists that seeks to address the problems common in wired and wireless communication technologies. For instance, [PERSON] and [PERSON] [10] opinedthat issues involving the difficulty of transferring information between the sender and receiver using wireless communication systems could be addressed using minimization optimization techniques. The application of particle swarm intelligence and optimization (PSI and PSO) was seen when they focused on finding the best conditions for the signal-to-noise ratio (SNR) using a dynamic particle swarm optimization (DPOS) algorithm for a 5G network. Thus, PSO is used in our search for optimal KPIs for the 4G LTE network. As the development of the cellular service market continues to advance radically, spurring high levels of competition between MNOs, sophisticated methods that foster better service provision, better network coverage, and reductions in cell reselection, handover time, and requirements have been highly encouraged and adopted by MNOs [11]. Several works show the relevance of establishing different relationships between network parameters and the QoS provided via an optimization process [3; 12]. The multidimensionality of the processes and requirements characterizing an eventual improvement in the service provided and the experience quality of an MBB network user involve changes in the qualitative methods adopted in both the design and the operation of the network in question. In some cases, major re-routing and replanning of the hardware positions within the target areas become indispensable if substantial levels of improvements are to be achieved [13; 14; 15; 16]. These changes are costly but, nonetheless, not as expensive as the implementation of 5G. Moreover, with increasing subscribers and network service prices, MNOs have the corresponding income to help them sustain their quest to ensure better QOE for end users. In [17], [PERSON] et al. extensively measured selected MBB KPIs in Victoria Island and Ikoyi in Lagos, Nigeria. Their work detailed the behavioral dynamics of the 4G LTE network in the two areas, followed by an extensive error analysis comparing the behavioral patterns formed with respect to missing data from the measurements. However, their work did not pay attention to other densely populated areas in Lagos, such as UNILAG. Similarly, Ref. [18] treated the characteristics of MBB networks with a focus on 5G-related operational codes and architecture using simulations. While adopting varying research approaches, none proposed practical network optimization methods.