# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 2 Related Work  
Since the conventional RSSI-based AP association scheme may lead to imbalanced load traffic among APs and unfairness bandwidth allocation among clients, many literatures concentrate on conceiving AP association strategies as the alternative of the default Strong Signal First (SSF) scheme in legacy IEEE802.11.  
There have been several AP association schemes for load balance among APs and various metrics are first studied to determine the AP load in some literatures. In [8], energy consumption and throughput are both proposed as the indication of AP load. In [9], the author uses the file download time for web browsing to estimate the AP load. In [10], the traffic intensity of clients and the effect of hidden terminals are considered as the main factors of AP load. Simultaneously, many load balancing strategies for AP association are also investigated. In [11], [PERSON] et al., propose a load balancing strategy for AP association based on the game theory with local information. In [12], a multi-constraint load balancing scheme based on cell breathing is utilized for the tradeoff between load balancing among APs and data power loss of clients. However, they are infeasible in many realistic environments due to their high complexity for clients. [PERSON] et al. [13] proposes a distributed adaptive load balancing algorithm for multi-rate WLANs, which uses some innovative load metrics such as throughput and transmission rates. However, it ignores the negative influence of newly arriving clients on the throughput of the existing clients.  
In [14], a comprehensive study about the correlation between load balancing and channel fairness is presented, which indicates that they cannot be easily achieved simultaneously. In [15], [PERSON] et al., propose a time-based fair AP algorithm, which jointly considers power control and AP association for aggregated throughput and proportional fairness. However, in this algorithm, the AP association is formulated as a NP-hard problem, which can lead the clients to switch among APs frequently due to its multiple iteration process often triggering the power adjustment. In [16], [PERSON] and [PERSON] present an AP association algorithm, which estimates the throughput of clients from downlink and uplink by a bi-dimensional Markov model and can achieve proportional fairness among clients. However, this algorithm has a high time complexity because some information from all nearby APs must be obtained timely. In [17], they propose another lower complexity on-line AP association algorithm, namely Categorized algorithm (Categorized), which achieves the proportional fairness by classifying APs based on types of clients associating with them. Although the performance anomaly problem is eliminated, load imbalance may emerge.  
In this paper, we propose a CAA-BD algorithm, which utilizes a new metric of AP load, the allocated transmission time of the AP, determined by all clients associating with it, as well as the newly arriving one. Meanwhile, we present an FBA-BP algorithm, which can gather the aggregated transmission time demand according to the clients' business and their priority. Simultaneously, the APs are categorized by the types of their associating clients. Therewith, the clients will give preference to associating the AP with the same type and the least allocated transmission time. This algorithm not only improves load balance among APs, but also takes channel fairness into consideration, which effectively minimizes the impact of performance anomaly.