Wireless Local Area Networks (WLANs) have become increasingly popular due to the recent availability of affordable devices providing multiple and high rate capabilities. Optimizing the performance of WLANs for emerging new Internet applications that demand High Throughput (HT) is an important and a highly challenging issue. In this book, we present a new practical Rate Adaptation (RA) algorithm, termed L3S, which extends legacy schemes with new MIMO features suitable for the forthcoming 802.11n high-speed MIMO-based WLAN products. We implement our rate adaptation algorithm in real hardware devices and evaluate its performance and compare it to the existing rate control mechanisms. Our experiments demonstrate that our scheme allows the current 802.11n devices to have a greater adaptability to a variety of wireless channel conditions and performs better than state-of-the-art rate adaptation algorithms.

The first generation 802.11 wireless market, once struggling to expand, has spread from largely vertical applications such as healthcare, point of sale, and inventory management to become much more broad as a general networking technology being deployed in offices, schools, hotel guest rooms, airport departure areas, airplane cabins, entertainment venues, coffee shops, restaurants, and homes. This has led to the tremendous growth of new sources of IEEE 802.11 devices. IEEE 802.11 equipment is now moving into its second stage, where the wireless LAN is being treated as a large wireless communication system. As a system, there is more to consider than simply the communication over the air between a single access point and the associated mobile devices. This has lead to innovative changes in the equipment that makes up a wireless LAN. The IEEE 802.11 Handbook: A Designer’s Companion, Second Edition is for the system network architects, hardware engineers and software engineers at the heart of this second stage in the evolution of 802.11 wireless LANs and for those designers that will take 802.11 to the next stage.

Recent years have seen a staggering increase in the deployment and utilization of wireless networks. More and more devices are being equipped with Wireless LAN (WLAN) cards to take advantage of the omnipresence of WLAN networks. Therefore, it has become necessary that the protocols used by WLANs are efficient and provide good performance. Rate Adaptation protocols are an important mechanism employed by WLANs to improve network performance. This dissertation develops three complementary techniques, which use rate adaptation to optimize and improve performance by i) performing rate adaptation to optimize energy consumption, ii) developing a more accurate technique to predict the frame delivery ratio that is used by rate adaptation protocols, and iii) jointly optimizing rate adaptation with data retransmission to maximize throughput. More specifically, in i), we use extensive measurements to develop a simple yet accurate energy consumption model for 802.11n wireless cards. We use the model to drive the design of an energy aware rate adaptation scheme. A major benefit of a model-based rate adaptation is that applying a model allows us to eliminate frequent probes required in many existing rate adaptation schemes. In ii), we find that the accuracy of existing delivery ratio calculation techniques is still limited due to bursty errors inherent to the wireless channel. We develop a new method for computing packet delivery rate that captures the burstiness of errors. Furthermore, we propose a new data interleaving technique, which leverages our framework to reduce the burstiness of errors, thereby improving frame delivery ratio. Finally, in iii), we address the susceptibility of wireless networks to transmission failures due to dynamic channel conditions and unpredictable interference. To efficiently recover from failures, we propose a retransmission scheme where the receiver combines information received from multiple failed transmissions associated with the same frame. The protocol has two distinguishing features. First, it simultaneously supports partial retransmission and combines bits with low confidence. Second, it jointly optimizes the data rate of the retransmission and the information to be retransmitted to maximize throughput.