Rate Control Algorithms For IEEE 802.11 Wireless Networks

This thesis evaluates software driven rate selection algorithms used in IEEE 802.11 wireless network interface cards. The motive of the bit-rate selection techniques is to optimize the throughput over the wireless network out of the many rates that are supported by the IEEE 802.11 link. The decision to switch from one rate to another with the changing link conditions to optimize the throughput is the primary focus of the bit-rate selection algorithms. Due to the uncertainty in the channel quality, it is a challenge to make the correct decision so as to minimize the wastage of network resources and achieve the highest throughput. This thesis also presents a novel learning bit-rate selection algorithm called the LeZiRate. LeZiRate uses the measurements of the quality of the signal received at the device to learn and predict the future quality of signal in near time. This thesis also presents a novel learning bit-rate selection algorithm called the LeZiRate. LeZiRate uses the measurements of the quality of the signal received at the device to learn and predict the future quality of signal in near time. Based on the prediction of channel condition it also selects the best bit-rate that would achieve the maximum throughput. LeZiRate does not monitor the network packets and therefore does not use any of the network resources. It monitors the signal quality received at the station and makes its decision based on that. It also makes the corrections to the prediction values by inducting the actual measurement of the signal quality on a real time basis so as to enable itself to make better predictions in the future. The LeZiRate algorithm monitors the signal quality and maps that to the received signal strength values, these values are quantized to map into a set of symbols. The frequency of occurrences of the string of these symbols is used to build a tree based on first order Markov model. It then selects the best bit-rate it believes would fetch the highest throughput. LeZiRate has a short setup and learning time to predict the first symbol. This thesis provides the simulation study of the LeZiRate algorithm and also presents simulation results of an existing bit-rate selection algorithm currently being used in the MADWiFi project for the multi-band Atheros wireless network interface cards. The simulation results show that LeZiRate is more sensitive to the changing link conditions of the wireless media although it does not use the network resources at all. The simulation study involved some measurements done through experimentation to collect realistic data and running the algorithms against that data.