Joint rate and channel width adaptation for 802.11 MIMO wireless networks

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.

[1]  Sung-Ju Lee,et al.  CSI-SF: Estimating wireless channel state using CSI sampling & fusion , 2012, 2012 Proceedings IEEE INFOCOM.

[2]  Kien T. Truong,et al.  An Experimental Evaluation of Rate Adaptation for Multi-Antenna Systems , 2009, IEEE INFOCOM 2009.

[3]  Robert Tappan Morris,et al.  Link-level measurements from an 802.11b mesh network , 2004, SIGCOMM '04.

[4]  Kevin C. Almeroth,et al.  The impact of channel bonding on 802.11n network management , 2011, CoNEXT '11.

[5]  Edward W. Knightly,et al.  Modulation Rate Adaptation in Urban and Vehicular Environments: Cross-Layer Implementation and Experimental Evaluation , 2008, IEEE/ACM Transactions on Networking.

[6]  Ratul Mahajan,et al.  Measurement-based models of delivery and interference in static wireless networks , 2006, SIGCOMM.

[7]  David Wetherall,et al.  Predictable 802.11 packet delivery from wireless channel measurements , 2010, SIGCOMM '10.

[8]  Songwu Lu,et al.  MIMO rate adaptation in 802.11n wireless networks , 2010, MobiCom.

[9]  Vaduvur Bharghavan,et al.  Robust rate adaptation for 802.11 wireless networks , 2006, MobiCom '06.

[10]  Hari Balakrishnan,et al.  Cross-layer wireless bit rate adaptation , 2009, SIGCOMM '09.

[11]  Michael Barton,et al.  Link Adaptation Algorithm for the IEEE 802.11n MIMO System , 2008, Networking.

[12]  Joseph M. Kahn,et al.  Fading correlation and its effect on the capacity of multielement antenna systems , 2000, IEEE Trans. Commun..

[13]  J. J. Garcia-Luna-Aceves,et al.  A practical approach to rate adaptation for multi-antenna systems , 2011, 2011 19th IEEE International Conference on Network Protocols.

[14]  Srikanth V. Krishnamurthy,et al.  Auto-configuration of 802.11n WLANs , 2010, CoNEXT.

[15]  Hongwei Yang A road to future broadband wireless access: MIMO-OFDM-Based air interface , 2005, IEEE Communications Magazine.

[16]  Xinbing Wang,et al.  Energy-based rate adaptation for 802.11n , 2012, Mobicom '12.

[17]  Fei Peng,et al.  Adaptive Modulation and Coding for IEEE 802.11n , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[18]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[19]  Suman Banerjee,et al.  802.11n under the microscope , 2008, IMC '08.