# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 3 Performance Evaluation  
In this section, we selected representative algorithms from each of the categories in order to evaluate their performance under mobility scenarios. Very few papers have dealt with mobility and interference impact on RAAs, according to our knowledge. The selected algorithms are: Ideal, Strale, MinstrelHT, and MutFed. The selected algorithms cover the different categories, MinstrelHT and Strale are both implicit feedback algorithms, MinstrelHT, which is a widely used algorithm, as it is implemented in the linux kernel [23], claims being able to select a sampling rate that results in the highest throughput and highest probability of successful delivery of the frames. The main difference is that Strale can be used as an extension algorithm, it can be placed on top of MinstrelHT to enhance its performance, and it is a promising algorithm that tries to adapt the number of MPDUs in aggregated frames without reducing the transmission rate. To our knowledge, it has not been tested in a dense environment. Not many hybrid RAAs are available in the literature, we selected the MutFed scheme that is the most generic algorithm in its category compared to the other hybrid algorithms proposed for specific scenarios. The idea behind Mutfed is promising due to its ability to distinguish the reason of packet loss and the fact that it relies on SINR. We believe that these algorithms give a comprehensive understanding of RAAs due to the fact that they rely on a variety of different metrics and approaches that are commonly used in the RAAs in the literature. We used the network simulator NS-3 to compare and evaluate the performance of these algorithms. Ideal and Minstrel-Ht rate adaptation algorithms are already implemented in the simulator. We modified the original implementation of the Ideal algorithm in the simulator in order to ensure a fair comparison between the selected algorithms. Indeed, we consider that all of the nodes have only one communication channel for data and control traffic exchange. Hence, for Ideal, we included the feedback in the reserved bits of the BlockAck, which is sent back to the transmitter on the same communication channel. We implemented the other two algorithms (Strale and MutFed). We chose the following metrics for the performance evaluation: (i) throughput, which is calculated at the physical layer, it gives a global view of the achieved performance of RAAs. (ii) Selected MCS, which indicates the MCS values selected by the nodes, it gives an idea about the different choices made by RAAs. Table 2 shows the different modulation and coding schemes of IEEE 802.11 ac. (iii) FLR based on MPDUs, it is calculated based on the BlockAck information, it gives an idea about the overall lost MPDUs. (iv) FLR based on A-MPDUs, it is calculated based on the number of times the transmitter had to retransmit its frame, it gives an idea about the efficiency of RAAs in selecting an MCS that avoids loosing frames. Indeed, choosing a high MCS would cause A-MPDU frames to be lost at the receiver. This means that high A-MPDU frame loss means that the RAA is making a wrong decision in increasing the MCS. All of these performance metrics are evaluated according to the number of nodes in the network. We increased the number of nodes while maintaining the same deployment surface. This means that increasing the number of nodes will increase the traffic load in the network and the interference level. This, in turn, will help us to evaluate the behavior of algorithms when faced with increased interference. All of the algorithms are evaluated in three different scenarios: (i) single link scenario, which is useful for highlighting the impact of link degradation due to mobility on RAAs. This allows us to evaluate the efficiency of RAAs is adapting the bit rate according to RSSI values only without interference. (ii) Infrastructure mode network, which represents the most widely used deployment mode.