## 2 Related Work
### Network Planning Algorithms  
To ensure the best network connectivity to users, wireless network planning is of utmost importance. Rational planning (i.e., minimizing the number of APs) and effective planning of the wireless network (i.e., maximizing the throughput) for indoor/outdoor operation usually requires the consecutive evaluation of many candidate solutions using optimization algorithms. Among the optimization algorithms commonly used in network planning, we can mention: e.g., heuristics [12; 22; 23; 24], GA [25; 26; 27; 28], or Particle Swarm Optimization (PSO) [29; 30; 31].  
In [12], a heuristic network planning approach is proposed, based on successively adding new access points until a user-defined throughput is achieved. Once the coverage requirement is met, it is investigated if any two closely spaced access points can be replaced by one. In [24], the authors designed an 802.11 ax-based dense Wi-Fi network to provide service to a large number of users minimizing the number of APs and fulfilling the users' throughput requirement. The authors designed a heuristic algorithm with polynomial time complexity and compared the performance to the random and Greedy solution. The simulation results show that the algorithm achieved an optimal performance for small areas. However, for a large area, despite the algorithm not being able to find the optimal solution, it reduced the number of APs by 32%, compared to random and Greedy solutions. However, finding the proposed solution takes much more time than the random and Greedy solutions, being the main constraint in the research.  
In [25], an NN was used to estimate the signal strength and avoid the architectural complexity of an indoor environment. In this research, the authors compared the NN performance with four different optimization algorithms: the PSO algorithm, GA, Powell's conjugate method, and the Simplex search method. The simulation results show that all four methods have similar performance in terms of the required number of base stations. However, in terms of computational time, the GA has slightly better performance compared to the other three models. In [26], a WLAN planning method, using a GA to optimize the placement and channel assignment of AP, was proposed. To evaluate the results, the authors analyzed three different conditions on the same floor/building and compared the results to a commercial planning tool. Simulation results show that the proposed solution has added extra APs. It happens because it takes into consideration the dense area (more users to support). In terms of time, the proposed solution is 1.5 times faster than the commercial planning tool used to evaluate the results. In [27], a combination of the Voronoi Diagram and a GA was proposed to maximize the coverage of a three-floor indoor environment. The proposal was compared in terms of coverage and computational time (e.g., time to distribute the population + execution time) with a centralized approach developed in [32]. The simulation results revealed that the combination Voronoi Diagram + GA provides only 94% (the same as the centralized approach) coverage after 1250 iterations and is 1.6 times faster than the centralized approach.  
A multi-objective algorithm to optimize the coverage and maximize the Signal-to-Interference Ratio (SIR), based on Binary Particle Swarm Optimization (BPSO), was proposed in [29]. The proposal was applied to the existing network, which consists of four APs. The coverage analysis of each AP was based on a ray-tracing method. Finally, the BPSO redistributes the APs increasing the RSS average by 10 dB and the SIR by 9 dB.