Sampleless Wi-Fi: Bringing Low Power to Wi-Fi Communications

The high sampling rate in Wi-Fi is set to support bandwidth-hungry applications. It becomes energy inefficient in the post-PC era in which the emerging low-end smart devices increase the disparity in workloads. Recent advances scale down the receiver’s sampling rates by leveraging the redundancy in the physical layer, which, however, requires packet modifications or very high signal-to-noise ratio. To overcome these limitations, we propose Sampleless Wi-Fi, a standard compatible solution that allows energy-constrained devices to scale down their sampling rates regardless of channel conditions. Inspired by rateless codes, Sampleless Wi-Fi recovers under-sampled packets by accumulating redundancy in packet retransmissions. To harvest the diversity gain as rateless codes without modifying legacy packets, Sampleless Wi-Fi creates new constellation diversity by exploiting the time shift effect at receivers. Our evaluation using GNURadio/USRP platform and real Wi-Fi traces has demonstrated that Sampleless Wi-Fi significantly outperforms the state-of-the-art downclocking technique in both decoding performance and energy efficiency.

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

[2]  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..

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

[4]  Mo Li,et al.  From Rateless to Distanceless: Enabling Sparse Sensor Network Deployment in Large Areas , 2016, IEEE/ACM Transactions on Networking.

[5]  Dapeng Oliver Wu,et al.  From Rateless to Hopless , 2015, IEEE/ACM Transactions on Networking.

[6]  Feng Lu,et al.  Enfold: downclocking OFDM in WiFi , 2014, MobiCom.

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

[8]  Harish Viswanathan,et al.  Retransmission ≠ repeat: simple retransmission permutation can resolve overlapping channel collisions , 2010, HotNets.

[9]  Feng Lu,et al.  SloMo: Downclocking WiFi Communication , 2013, NSDI.

[10]  Tian He,et al.  FreeBee: Cross-technology Communication via Free Side-channel , 2015, MobiCom.

[11]  Dina Katabi,et al.  Frequency-aware rate adaptation and MAC protocols , 2009, MobiCom '09.

[12]  Dina Katabi,et al.  BigBand: GHz-Wide Sensing and Decoding on Commodity Radios , 2013 .

[13]  Devavrat Shah,et al.  Spinal codes , 2012, CCRV.

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

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

[16]  Omid Salehi-Abari,et al.  GHz-wide sensing and decoding using the sparse Fourier transform , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[17]  Michael Luby,et al.  LT codes , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[18]  Victor Y. Chen,et al.  Less Transmissions, More Throughput: Bringing Carpool to Public WLANs , 2015, IEEE Transactions on Mobile Computing.

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

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

[21]  Leo Monteban,et al.  WaveLAN®-II: A high-performance wireless LAN for the unlicensed band , 1997, Bell Labs Technical Journal.

[22]  Richard G. Lyons,et al.  Understanding Digital Signal Processing , 1996 .

[23]  Jihoon Kim,et al.  WiZizz: Energy efficient bandwidth management in IEEE 802.11ac wireless networks , 2015, 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[24]  Sachin Katti,et al.  Strider: automatic rate adaptation and collision handling , 2011, SIGCOMM.

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

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

[27]  David Wetherall,et al.  Demystifying 802 . 11 n Power Consumption , 2010 .

[28]  Piotr Indyk,et al.  Nearly optimal sparse fourier transform , 2012, STOC '12.

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

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

[31]  Jr. G. Forney,et al.  Viterbi Algorithm , 1973, Encyclopedia of Machine Learning.

[32]  Qian Zhang,et al.  Wideband Spectrum Adaptation Without Coordination , 2017, IEEE Transactions on Mobile Computing.

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

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

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

[36]  Keith E. Nolan,et al.  Compressive sensing for dynamic spectrum access networks: Techniques and tradeoffs , 2011, 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[37]  Yin Zhang,et al.  Robust network compressive sensing , 2014, MobiCom.