## 2 Related Works  
In this section, we briefly discuss related works in the literature. In [37], [PERSON] et al. presented an algorithm for AP deployment based on physical distance and channel isolation (DPDCI) in urban environments, which can effectively reduce interference and improve the system's overall throughput. The algorithm optimizes the placement of APs and allocates the channel to APs by adopting a genetic algorithm. In [38], [PERSON] et al. proposed a new optimal AP deployment (OAD) algorithm based on fuzzy C-clustering, which provides good performance in the maximum number of WLAN users and balanced the traffic load among the APs. In [39], [PERSON] et al. proposed a genetic algorithm Cramer-Rao lower bound (GA-CRLB) method for finding optimal AP positioning that maximizes localization accuracy and coverage simultaneously. This method adopted CRLB as an evaluation methodology for evaluating the localization accuracy of the deployment plan, and then the genetic algorithm was used to rapidly find the optimal AP deployment strategy. In [40], [PERSON] et al. jointly presented a user association and bandwidth allocation algorithm called the bandwidth-constrained association (BCA) to maximize the total network throughput while satisfying user device bandwidth requirements. They adopted an efficient polynomial time method for producing the best user association. In [41], [PERSON] et al. presented a joint AP association and bandwidth-allocation scheme in order to optimize network throughput and utilization taking into account access points' (APs) capacity and user demand. They adopted the rounding and bandwidth allocation algorithm (RBAA) for this model. In [42, 43], [PERSON] et al. and [PERSON] et al. proposed novel AP deployment methods by using a genetic algorithm (GA) and fruit fly optimization algorithm (FOA), respectively. In both proposals, the AP locations and the transmit powers of all the activated APs are optimized while ensuring complete coverage of the desired service area. In [44], [PERSON] et al. proposed analytical expressions to measure the throughput performance of WLAN while considering the inter-network interference. They adopted the airtime concept to express the simultaneous transmission time and the carrier-sensing duration to understand the carrier-sensing duration for this proposal. In [45], [PERSON] et al. proposed a method for optimizing AP deployment based on the multi-purpose particle swarm optimisation algorithm (MOPSO). First, the performance of a single AP was evaluated using the random geometry theory to estimate the number of APs that should be deployed in the WLAN, based on the user's service needs. Then, the proposed scheme uses the MOPSO algorithm to obtain the optimal position and optimal transmit power of the APs. Finally, a greedy algorithm is used in order to remove the redundant APs. In [46], [PERSON] et al. presented a joint optimization method for the placement of access points, power assignment, channel assignment, and resource unit assignment in dense IEEE 802.11 ax WLAN. The objective of the proposal is to reduce the cost regarding the number of access points (APs) while meeting the requirements of specified failure tolerance and a specified two-tier flow. The authors designed a heuristic algorithm based on polynomial time complexity in order to find high-quality solutions and compared the throughput results to the greedy and random solution. In [47], [PERSON] et al. analyzed the ability of IEEE 802.11n dual-band APs to support heterogeneous client adapters and applications. By testbed experiments, they concluded that dual-band APs can effectively reduce the negative effects of heterogeneity on clients' network adapters. In [48], [PERSON] et al. proposed a QoS differentiation scheme called Slickfi for high-speed WLANs. It aims to enhance the real-time application performance while maintaining the high network throughput.