SloMo: Downclocking WiFi Communication

As manufacturers continue to improve the energy efficiency of battery-powered wireless devices, WiFi has become one of—if not the—most significant power draws. Hence, modern devices fastidiously manage their radios, shifting into low-power listening or sleep states whenever possible. The fundamental limitation with this approach, however, is that the radio is incapable of transmitting or receiving unless it is fully powered. Unfortunately, applications found on today’s wireless devices often require frequent access to the channel. We observe, however, that many of these same applications have relatively low bandwidth requirements. Leveraging the inherent sparsity in Direct Sequence Spread Spectrum (DSSS) modulation, we propose a transceiver design based on compressive sensing that allows WiFi devices to operate their radios at lower clock rates when receiving and transmitting at low bit rates, thus consuming less power. We have implemented our 802.11b-based design in a software radio platform, and show that it seamlessly interacts with existing WiFi deployments. Our prototype remains fully functional when the clock rate is reduced by a factor of five, potentially reducing power consumption by over 30%.

[1]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[2]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[3]  Scott Shenker,et al.  Scheduling for reduced CPU energy , 1994, OSDI '94.

[4]  Thomas A. DeMassa,et al.  Digital Integrated Circuits , 1985, 1985 IEEE GaAs IC Symposium Technical Digest.

[5]  Robert Tappan Morris,et al.  Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks , 2001, MobiCom '01.

[6]  Hari Balakrishnan,et al.  Minimizing Energy for Wireless Web Access with Bounded Slowdown , 2002, MobiCom '02.

[7]  Paramvir Bahl,et al.  Wake on wireless: an event driven energy saving strategy for battery operated devices , 2002, MobiCom '02.

[8]  Jason Flinn,et al.  Self-Tuning Wireless Network Power Management , 2003, MobiCom '03.

[9]  Li Shang,et al.  Dynamic voltage scaling with links for power optimization of interconnection networks , 2003, The Ninth International Symposium on High-Performance Computer Architecture, 2003. HPCA-9 2003. Proceedings..

[10]  William R. Dieter,et al.  Power reduction by varying sampling rate , 2005, ISLPED '05. Proceedings of the 2005 International Symposium on Low Power Electronics and Design, 2005..

[11]  Alec Wolman,et al.  Wireless wakeups revisited: energy management for voip over wi-fi smartphones , 2007, MobiSys '07.

[12]  Stefan Savage,et al.  Automating cross-layer diagnosis of enterprise wireless networks , 2007, SIGCOMM '07.

[13]  Ahmad Rahmati,et al.  Context-for-wireless: context-sensitive energy-efficient wireless data transfer , 2007, MobiSys '07.

[14]  Dave Levin,et al.  On the Fidelity of 802.11 Packet Traces , 2008, PAM.

[15]  Paramvir Bahl,et al.  A case for adapting channel width in wireless networks , 2008, SIGCOMM '08.

[16]  Brian D. Noble,et al.  BreadCrumbs: forecasting mobile connectivity , 2008, MobiCom '08.

[17]  Lin Zhong,et al.  Micro power management of active 802.11 interfaces , 2008, MobiSys '08.

[18]  Ion Stoica,et al.  Blue-Fi: enhancing Wi-Fi performance using bluetooth signals , 2009, MobiSys '09.

[19]  Rajesh Gupta,et al.  Softspeak: Making VoIP Play Well in Existing 802.11 Deployments , 2009, NSDI.

[20]  Haitao Wu,et al.  Sora: High Performance Software Radio Using General Purpose Multi-core Processors , 2009, NSDI.

[21]  Ilenia Tinnirello,et al.  On the Effects of Transmit Power Control on the Energy Consumption of WiFi Network Cards , 2009, QSHINE.

[22]  Richard G. Baraniuk,et al.  Signal Processing With Compressive Measurements , 2010, IEEE Journal of Selected Topics in Signal Processing.

[23]  Konstantina Papagiannaki,et al.  Catnap: exploiting high bandwidth wireless interfaces to save energy for mobile devices , 2010, MobiSys '10.

[24]  Ramachandran Ramjee,et al.  NAPman: network-assisted power management for wifi devices , 2010, MobiSys '10.

[25]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[26]  David Wetherall,et al.  Demystifying 802.11n power consumption , 2010 .

[27]  Xue Liu,et al.  SiFi: exploiting VoIP silence for WiFi energy savings insmart phones , 2011, UbiComp '11.

[28]  Justin Manweiler,et al.  Avoiding the Rush Hours: WiFi Energy Management via Traffic Isolation , 2011, IEEE Transactions on Mobile Computing.

[29]  Kang G. Shin,et al.  E-MiLi: Energy-Minimizing Idle Listening in Wireless Networks , 2011, IEEE Transactions on Mobile Computing.

[30]  Ranveer Chandra,et al.  Empowering developers to estimate app energy consumption , 2012, Mobicom '12.

[31]  Ming Zhang,et al.  Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof , 2012, EuroSys '12.