The newly ratified IEEE 802.11n standard brings a number of new possibilities in the quest for attaining wire-like transmission speeds. The number of subchannels, the option for channel bonding, and the use of multiple antennas and MIMO technologies result in a vast variety of potential transmission strategies. Because of the number of variables, selecting the 'right combination' given the offered load, interference patterns and environmental noise is a complex undertaking. A naïve channel assignment strategy will result in sub-optimal performance due to unnecessary channel contention. This work addresses a number of open questions in the quest to develop a channel assignment strategy for 802.11n that maximizes spatial reuse. In particular, the work seeks to answer to the following questions: (i) how should MIMO transmission schemes, channel bonding, and DFS considerations best be integrated into a channel management solution to maximize performance?; (ii) how can the gains achieved by MIMO systems best be utilized to influence the channel management system?; and (iii) how should metrics that influence these decisions be obtained and redefined for 802.11n systems?
The project will be facilitated through a partnership between UC Santa Barbara and HP Laboratories. Outcomes of the work will include a set of solutions for dynamically monitoring current channel state, and an integrated channel management solution that utilizes monitoring output to achieve high-speed, long transmission range communication in 802.11n systems.
Our work on channel bonding and rate adaptation in IEEE 802.11n networks has made fundamental contributions to the understanding of 802.11n MIMO operation. 802.11n is currently one of the most popular wireless channel access protocols; however, little is known about how to optimize its performance, particularly in the presence of interference from other 802.11n transmitters. Through empirical experiments, we have studied channel bonding in 802.11n (i.e., increasing channel width from 20MHz to 40MHz) to understand the impact of neighboring transmitters, particularly with respect to signal strength of the interferer and the transmitter at the receiver; the modulation and coding scheme (MCS) in use by the transmitter; the strength of the interfering transmission; and the physical rate of links in carrier sensing range. Based on our experimentation, we are able to make recommendations about whether a sender should increase the channel width of their transmission in order to perform intelligent channel bonding. Based on our findings from our study of channel width, we developed a joint rate and channel width rate adaptation algorithm, called ARAMIS, for 802.11n networks. ARAMIS leverages a new link metric, called diffSNR, that accurately characterizes the performance of MIMO links and reflects current channel conditions. Previous rate adaptation solutions based rate selection on SNR (signal-to-noise ratio). However, SNR does not reflect the extent of spatial selective fading that occurs in MIMO environments. Because per-packet SNR is the linear sum of all per-antenna measurements, the reported SNR bay be high even though a link could be lossy if only a subset of the antennas experience fading. Instead of SNR, diffSNR assesses the difference between the best and the worst SNR at any of the receiver’s antennas. A high diffSNR value indicates that some antennas are suffering from fading; SNR for the affected antennas can degrade to such an extent that the signal received can no longer be decoded. These deep fades degrade link performance and reduce the potential capacity of the MIMO system. The figure shows the real-time evolution of SNR and diffSNR for a given link. In the design of ARAMIS, we use diffSNR to build an 802.11n link predictor that correctly estimates link quality in more than 96% of test cases. With the use of diffSNR, ARAMIS is able to adapt transmission rate on a per-packet basis in order to achieve higher packet delivery rates and throughput in 802.11n networks. We implemented ARAMIS on a 15-node testbed and showed that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead, outperforming existing rate adaptation algorithms with up to a 10-fold increase in throughput. In the included figure, we demonstrate the performance of ARAMIS, when compared with three alternative solutions, in seven representative topologies. The figures demonstrate the improvement in performance ARAMIS achieves in each topology. Because 802.11n is such a widely used technology, and because next-generation 802.11ac networks are an evolution of 802.11n, our work has the potential to have broad impact on research, industry, society and education through the development of fundamental understanding of channel management in MIMO systems. Through our partnership with HP, our work has the potential to influence the next generation of commercially available 802.11 wireless products.
