Wearable networks: A new frontier for device-to-device communication Professor Robert W. Heath Jr. Wireless Networking and Communications Group Department of Electrical and Computer Engineering The University of Texas at Austin http://www.profheath.org Joint work with Kiran Venugopal (UT) and Ma9hew C. Valen< (West Virginia Univ.) Supported by the Intel 5G program and the Big-XII Faculty Fellowship program
Wearable networks Device to track dog s activity Connected pet Augmented reality glasses Fitness trackers Connected person Wireless headset Smart watch Smart phone u Multiple communicating devices in and around the body ª 5 or more devices per person based on market trends trend u D2D communication between nodes ª Uncoordinated with another person s wearable network Smart phone may be the hub of the D2D wearable network 2
Galaxy of wearables u Low-rate fitness monitors to high-rate infotainment devices u May lead to the high consumption that motivates 5G data rates Wearable networks will be very heterogeneous [1] http://www.arm.com/markets/wearables.php [2] http://iot-observer.blogspot.com/ 3
Wearables growth potential Gartner research data Smartphone: 18% Wearable : 372% u Significant interest at the Mobile World Congress ª Smart watches, augmented reality, and other devices u People may have one smart phone but many wearables ª New opportunities for semiconductors and software Wearables becomes a smart phone multiplier [1] Will wearables replace your smartphone? Tom s guide: Tech for real life http://www.tomsguide.com/us/smartphones-vs-wearables,review-2253.html [2] https://medium.com/@iguchijp/will-telepathy-one-be-able-to-change-the-world-be590c4840b0 4
Wearable networks vs. body area networks Narrowband communica.on UWB communica.on Frequency Bands (MHz) Supported data rates (kbps) Frequency Band (GHz) Max. data rate (Mbps) 402-405, 420-450, 863-870, 902-928, 950-956, 2360-2400, 2400-2483.5 57.5 971.4 3.2-4.7, 6.2-10.2 15.6 u BANs have been focused low-rate applications: IEEE 802.15.6 ª Health-care and fitness monitors including implants ª Man-to-machine communication in workplace u Sparse environment and less significant interference [1] IEEE 802.15.6 standard, 2012 [2] S. N. Ramli and R. Ahmad, Surveying the Wireless Body Area Network in the realm of wireless communication IAS conference 2012 5
D2D WEARABLE NETWORKS WITH MMWAVE 6
PHY / MAC challenges in D2D wearable networks u Provide a high quality and high bandwidth comm. link u Support heterogeneous devices u Co-exist with other networks in dense environments [1] http://www.bombardier.com/en/transportation/products-services/railvehicles/metros.html 7
MmWave as solution for wearable networks USA 1 Japan 2 Australia 3 Europe 4 Max transmit power : 500 mw Max EIRP : 43 dbm Max output power: 10 mw Max bandwidth: 2.5 GHz; Max antenna gain: 47 dbi Max output power: 10dBm Max EIRP: 51.8 dbi Max transmit power : 20 mw Max EIRP : 40 dbm 57 GHz 59 GHz 64 GHz 66 GHz Several GHz of spectrum available for worldwide operation 0 u High bandwidth and reasonable isolation u Compact antenna arrays to provide array gain and reduced interf. u Commercial products already available: IEEE 802.11ad, WirelessHD 1 47 CFR 15.255; 2 ARIB STD-T69, ARIB STD-T74; 3 Radiocommunications Class License 2000; 4 CEPT : Official journal of the EU; 8
PERFORMANCE ANALYSIS IN FINITE MMWAVE WEARABLE NETWORKS K. Venugopal, M. Valenti, and R. W. Heath, Jr., Interference in finite-sized highly dense millimeter wave networks, (invited) Proc. of the Information Theory and Applications, San Diego, California, February 1-6, 2015. Paper available at http://www.ita.ucsd.edu/workshop/15/files/paper/paper_430.pdf 9
What is different for mmwave wearable networks? Receiver Interferers Blocked 2D geometry u Finite number of interferers in a finite network region ª Realistic assumption for the indoor wearable setting w/ mmwave ª Fixed/random location of interferers (extended in journal version) u Blockages due to other human bodies (can be extended to pets) u Both interferer and blockage associated with a user 10
Related work on interference modeling u Stochastic geometry models for mmwave cellular networks [1]-[3] ª Infinite spatial extent and number of nodes ª Did not consider people as a source of blockage u Performance analysis for finite ad-hoc networks [4] ª Does not include directional antennas or blockage u Self-blockage model for mmwave [5] ª Considers a 5G cellular system ª User's own body blocks the signal, not other users [1] T. Bai and R. W. Heath Jr., Coverage and rate analysis for millimeter wave cellular networks, IEEE Trans. Wireless Comm., 2014. [2] S. Singh, M. N. Kulkarni, A. Ghosh, and J. G. Andrews, Tractable model for rate in self-backhauled millimeter wave cellular networks, online [3] T. Bai, A. Alkhateeb, and R. W. Heath Jr., Coverage and capacity of millimeter-wave cellular networks, IEEE Commun. Magazine, 2014. [4] D. Torrieri and M. C. Valenti, The outage probability of a finite ad hoc network in Nakagami fading, IEEE TCOM, 2012. [5] T. Bai and R. W. Heath Jr., Analysis of self-body blocking effects in millimeter wave cellular networks, in Proc. Asilomar 2014. 11
SYSTEM MODEL 12
Modeling antenna pattern using a sectored antenna Number of antenna elements Beamwidth θ Main- lobe gain G Side- lobe gain g u Use a 2D sectored antenna model to simplify the analysis ª Parameterize via a uniform planar square array w/ half-wavelength spacing u Incorporates omni- direc<onal antennas as a special case ª = 1 à omni-directional antenna, G = g = 1 ª Of interest for inexpensive wearable 13
Network topology R i X i φ i Reference Rx Reference Tx Interfering Tx Finite region u Finite sized network region, area =, users u One interferer per user transmits at a time u ª interferers + reference transmitter-receiver pair, location of transmitters relative to reference receiver 14
Modeling human body blockages X i Y i Reference Rx Reference Tx Interfering Tx u Associate diameter W circle with each user denoted Y i u Determine blocking cone for each Y i ª X i blocked if it falls in one of the blocking cones u Assume Y i does not block X i, i.e., no self-blocking 15
SINR MODEL 16
SINR and ergodic spectral efficiency Evaluate CCDF of SINR Derive ergodic spectral efficiency u SINR is Noise power normalized by P 0 17
Signal from reference transmitter Reference Rx Reference Tx R 0 u h 0 Nakagami fade gain from reference with parameter m 0 u Assume that there is always LOS communication u Reference Tx is within the main beam of the reference Rx 18
Signal from interfering transmitters Reference Rx Reference Tx Interfering Tx Blockage associated with interfering Tx NLOS link LOS link u h i - Nakagami fading with parameter m i from X i u Link is NLOS if blocked and LOS otherwise m i = m N m i = m L 19
Path-loss model and power gains Reference Rx θ r Rx gain G r R i X i Reference Tx Interfering Tx φ 0 φ i Rx gain g r u - path-loss exponent from X i : for LOS, for NLOS u Define normalized power gain from X i Tx power of X i Ref. receiver s main-lobe points towards X i Captures path loss and Rx orientation 20
Relative transmit power Gain G t w.p. (θ t /2π) θ t Transmit antenna at X i Gain g t w.p. (1 - θ t /2π) u X i transmits with probability p t (Aloha-like medium access) u X i points its main-lobe in a (uniform) random direction Probability that ref. receiver is within main-lobe of X i Captures p t and random Tx orientation 21
PERFORMANCE ANALYSIS 22
CCDF of SINR u SINR coverage probability for a given threshold K. Venugopal, M. C. Valenti and R. W. Heath Jr., Interference in Finite-Sized Highly Dense Millimeter Wave Networks, in Proc. of ITA 2015 23
CCDF of SINR u SINR coverage probability for a given threshold where 24
NUMERICAL RESULTS 25
Setting Receiver at center u 5 X 9 rectangular grid u Separation between nodes = 2R 0 u No reflection from boundaries Receiver at a corner Parameter s Value R 0 1 m L 4 m N 2 α L 2 α N 4 W 1 σ 2-20 db K 44 u All nodes transmit with same P i 26
Spectral efficiency for different antenna configurations Robert W. Heath Jr. (2015) p t = 0.1 N t =N r =16 Receiver at the center N t =N r =1 Significant benefits to beamforming 27
Effect of receive antenna orientation Receiver at the center Receiver at a corner Robert W. Heath Jr. (2015) p t = 0.7 N t = N r = 16 Orientation of RX is more important in the corner 28
Rate trends with N t and N r Assume 2.16 GHz BW of IEEE 802.11ad N t N r Ergodic spectral efficiency (bits/s/hz) Rate (Gb/s) Receiver at center Receiver at a corner Receiver at center Receiver at a corner 1 1 0.499 1.063 1.08 2.30 p t = 1 1 4 0.797 1.405 1.72 3.03 1 16 1.757 2.087 3.80 4.51 4 1 2.449 4.046 5.29 8.74 4 4 3.210 5.072 6.93 10.96 4 16 5.437 7.078 11.74 15.29 16 1 3.618 5.027 7.81 10.86 16 4 4.635 6.396 10.01 13.82 16 16 6.952 8.434 15.02 18.22 Gigabit throughputs are achieved even with a single transmit and receive antenna 29
Concluding remarks u Wearable networks are the next frontier for D2D u MmWave can provide Gbps data rates to wearables ª Substantial variation as a function of location u Many issues remain to be studied ª Protocols for wearables to support multi-band and het. devices ª Channel models including self-body blocking ª Performance analysis For more information, see: K. Venugopal, M. Valenti, and R. W. Heath, Jr., Interference in finite-sized highly dense millimeter wave networks, (invited) Proc. of the Information Theory and Applications, San Diego, California, February 1-6, 2015. Paper available at http://www.ita.ucsd.edu/workshop/15/files/paper/paper_430.pdf 30
QUESTIONS? 31