# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 2 Theoretical Background
### Optimization  
We will now discuss the theoretical framework for optimizing the experience of a 4G LTE network user applying particle swarm optimization (PSO). PSO is an optimization algorithm that searches for solutions by simulating the behavior of a swarm of particles considered a population. It searches for the optimal solution in a high-dimensional search space while information is shared among swarm members. The algorithm works by iteratively updating the position and velocity of the particles based on their current best performance and the best performance of the entire swarm. PSO has been successfully applied in various fields, including wireless communications, and has proven timeless in optimizing wireless networks.  
Closely related to PSO is harmony search optimization (HSO), a dynamic version of the algorithm that combines PSO with other optimization techniques, such as genetic algorithms (GA), artificial neural networks (ANN), and fuzzy logic, to create a hybrid optimization algorithm. In HSO, the aim is to leverage the strengths of multiple optimization techniques and combine them into a single hybrid algorithm. Although the results are considered more efficient, PSO remains an effective optimization process that can handle complex and highly constrained optimization problems.  
4G network architecture plays a role in any process, ensuring successful optimization. Generally, 4G network architecture consists of various components, such as the base station, the mobile terminal, and the core network. These components work together to provide the required QoS to the end users. For example, the base station provides radio coverage and manages data transmission between the mobile terminal (user equipment) and the core network. While the user equipment affords the user access to the network, the core network manages and routes the data to its destination.  
To optimize the QoS of 4G networks, various metrics, such as signal strength, data rate, and packet loss rate, must be considered and optimized. This approach has been used in different works in the past for effective optimization, and is summarized in the following steps:  
1. Modeling the 4G network: First, a network model is developed taking into account the physical characteristics of the network, operational base stations, active users, the frequency spectrum available, and the user's proximity to a base station.
2. Defining the objective function: A mathematical representation of the QoS metric to be optimized is developed within a predetermined boundary. For example, in the case of 4G networks, common objective functions may include maximizing the data rate, minimizing the latency, or maximizing the energy efficiency of the network.
3. Selecting the optimization algorithm: Depending on the objective function and the characteristics of the 4G network, an informed choice can be made between applying PSO or HSO as the optimization algorithm. For example, if the objective function is complex and the network is large, HSO may be a better choice. On the other hand, if the objective function is simple and the network is small, PSO may be a more efficient choice.
4. Implementing the algorithm in MATLAB: Once the optimization algorithm has been selected, a thorough implementation in MATLAB is required. This will involve writing code that implements the algorithm, defining the parameters for the optimization, and setting up the objective function.
5. Running the optimization: Once the algorithm has been implemented in MATLAB, the optimization can then be run, specifying the initial conditions and the number of iterations. The algorithm will then iteratively update the parameters of the network until the objective function is optimized.
6. Analyzing the results: Finally, the optimization results can be further examined to determine the optimal configuration of the 4G network. This may involve plotting the results, comparing the results to the baseline, or determining the impact of the optimization on the QoS metric.