Auto-configuration of 802.11n WLANs

Arslan, Mustafa Y.; Pelechrinis, Konstantinos; Broustis, Ioannis; Krishnamurthy, Srikanth V.; Addepalli, Sateesh; Papagiannaki, Konstantina

doi:10.1145/1921168.1921204

Cited by 41 publications

(37 citation statements)

References 17 publications

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“…Wider transmissions are more likely to suffer from frequency selective fading, which causes SNR variations across the OFDM subcarriers, and PER is dominated by the lower SNR carriers. A 40MHz channel, therefore, not only requires a stronger transmission power to achieve the same SNR [14] but also a higher SNR to provide the same PER. Fig.…”

Section: A the Limitation Of Rssimentioning

confidence: 99%

Joint rate and channel width adaptation for 802.11 MIMO wireless networks

Deek

García-Villegas

Belding

et al. 2013

2013 IEEE International Conference on Sensing, Communications and Networking (SECON)

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Abstract-The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems. This leap, however, adds complexity to selecting the right transmission rate. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth. ARAMIS adapts transmission rates on a per-packet basis; we believe it is the first 802.11n RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11n environments with up to a 10 fold increase in throughput.

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Section: A the Limitation Of Rssimentioning

confidence: 99%

Joint rate and channel width adaptation for 802.11 MIMO wireless networks

Deek

García-Villegas

Belding

et al. 2013

2013 IEEE International Conference on Sensing, Communications and Networking (SECON)

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show abstract

“…These studies rely on the assumption that increasing the channel bandwidth should theoretically increase the data transmission rate, since more data is being transmitted over a wider bandwidth. Recent studies, however, have shown that the benefits of channel bonding in 802.11n are influenced by network factors, such as interference and loss [6,25,27]. Therefore, it is clear that channel management solutions in 802.11n WLANs must first understand the behavior of channel bonding in order to make intelligent decisions as to how to assign bandwidth in the network.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Experimental Studies of 802.11n: Experimental studies on 802.11n have primarily focused on providing insight into 802.11n features [6,25,27]. Although much has been understood, research still falls short on accurately analyzing channel bonding in real-world WLAN settings.…”

Section: Background and Related Workmentioning

confidence: 99%

“…We vary the separation between the non-overlapping transmission channels from being adjacent (adj), shown in Figure 4(a), to being separated by a 20MHz channel (sep), shown in Figure 4(b). 6 We also include the case where transmission channels are far enough apart (40MHz or more) to be considered interferencefree (intf-free).…”

Section: What Is the Impact Of Channel Leakage?mentioning

confidence: 99%

“…Previous experimental studies on 802.11n have focused on providing insight into 802.11n features, namely frame aggregation, channel bonding, and MIMO [6,25,27,22]. Although much has been understood from related work, existing research still falls short in effectively characterizing channel bonding in real-world network settings.…”

Section: Introductionmentioning

confidence: 99%

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The impact of channel bonding on 802.11n network management

Deek

García-Villegas

Belding

et al. 2011

Proceedings of the Seventh COnference on Emerging Networking EXperiments and Technologies

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The IEEE 802.11n standard allows wireless devices to operate on 40MHz-width channels by doubling their channel width from standard 20MHz channels, a concept called channel bonding. Increasing channel width should increase bandwidth, but it comes at the cost of decreased transmission range and greater susceptibility to interference. However, with the incorporation of MIMO (Multiple-Input MultipleOutput) technology in 802.11n, devices can now exploit the increased transmission rates from wider channels at a reduced sacrifice to signal quality and range. The goal of our work is to understand the characteristics of channel bonding in 802.11n networks and the factors that influence that behavior to ultimately be able to predict behavior so that network performance is maximized. We discuss the impact of channel bonding choices as well as the effects of both cochannel and adjacent channel interference on network performance. We discover that intelligent channel bonding decisions rely not only on a link's signal quality, but also on the strength of neighboring links and their physical rates.

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A practical framework for 802.11 MIMO rate adaptation

et al. 2015

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The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems.This leap, however, adds complexity to optimizing transmission. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, our contributions in this work are two-fold. First, we propose a practical link metric that accurately captures channel conditions in MIMO 802.11n environments, and we call this metric diffSNR. Using diffSNR captured from real testbed environments, we build performance models that accuractely predict link quality in 95.5% of test cases. Practicality and deployability are guaranteed with diffSNR as it can be measured on all off-the-shelf MIMO WiFi chipsets. Second, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth, and we utilize the diffSNR-based 802.11n performance models within ARAMIS's framework. ARAMIS adapts transmission rates on a perpacket basis; we believe it is the first closed-loop, 802.11 RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS with diffSNR on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show Preprint submitted to Elsevier February 9, 2015 that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11nenvironments with up to a 10 fold increase in throughput.

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Auto-configuration of 802.11n WLANs

Cited by 41 publications

References 17 publications

Joint rate and channel width adaptation for 802.11 MIMO wireless networks

Joint rate and channel width adaptation for 802.11 MIMO wireless networks

The impact of channel bonding on 802.11n network management

A practical framework for 802.11 MIMO rate adaptation

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