wearable networks: a new frontier for device-to-device...

© Robert W. Heath Jr. (2015)

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

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

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Augmented reality glasses

Wireless headset

Smart watch

Fitness trackers

Device to track dog’s activity

Connected person Connected pet

Smart phone

Smart phone may be the hub of the D2D wearable network


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

3 [1] http://www.arm.com/markets/wearables.php [2] http://iot-observer.blogspot.com/

Wearable networks will be very heterogeneous


Wearables growth potential

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

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Smartphone: 18% Wearable : 372%

Gartner research data

[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

Wearables becomes a smart phone multiplier


Wearable networks vs. body area networks

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

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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

[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


D2D WEARABLE NETWORKS WITH MMWAVE

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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

7 [1] http://www.bombardier.com/en/transportation/products-services/railvehicles/metros.html


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;

MmWave as solution for wearable networks

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Max transmit power : 500 mW Max EIRP : 43 dBm

Max output power: 10 mW Max bandwidth: 2.5 GHz; Max antenna gain: 47 dBi

57 GHz 64 GHz

Max output power: 10dBm Max EIRP: 51.8 dBi

59 GHz

USA1

Japan2

Australia3

66 GHz

Max transmit power : 20 mW Max EIRP : 40 dBm Europe4

Several GHz of spectrum available for worldwide operation 0


PERFORMANCE ANALYSIS IN FINITE MMWAVE WEARABLE NETWORKS

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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


What is different for mmWave wearable networks?

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

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Receiver Interferers

Blocked

2D geometry


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

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[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.


SYSTEM MODEL

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Modeling antenna pattern using a sectored antenna

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

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Number of antenna elements

Beamwidth θ

Main-‐lobe gain G

Side-‐lobe gain g


Network topology

u  Finite sized network region , area = , users u  One interferer per user transmits at a time

ª  interferers + reference transmitter-receiver pair

u  , location of transmitters relative to reference receiver

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Reference Rx

Reference Tx

Interfering Tx φi

Ri

Xi

Finite region


Modeling human body blockages

u  Associate diameter W circle with each user – denoted Yi u  Determine blocking cone for each Yi

ª Xi blocked if it falls in one of the blocking cones

u  Assume Yi does not block Xi, i.e., no self-blocking

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Reference Rx

Reference Tx

Interfering Tx

Xi Yi


SINR MODEL

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SINR and ergodic spectral efficiency

u  SINR is

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Noise power normalized by P0

Evaluate CCDF of SINR

Derive ergodic spectral efficiency


Signal from reference transmitter

u  h0 – Nakagami fade gain from reference with parameter m0 u  Assume that there is always LOS communication u  Reference Tx is within the main beam of the reference Rx

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Reference Rx

Reference Tx

R0


Signal from interfering transmitters

u  hi - Nakagami fading with parameter mi from Xi

u  Link is NLOS if blocked and LOS otherwise

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Reference Rx

Reference Tx

Interfering Tx

Blockage associated with interfering Tx

NLOS link LOS link

mi = mN mi = mL


φ0

u  - path-loss exponent from Xi: for LOS, for NLOS

u  Define normalized power gain from Xi

Path-loss model and power gains

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φi

Xi Ri

Reference Rx

Reference Tx

Interfering Tx θr

Rx gain Gr

Rx gain gr

Ref. receiver’s main-lobe points towards Xi

Tx power of Xi

Captures path loss and Rx orientation


Relative transmit power

u  Xi transmits with probability pt (Aloha-like medium access) u  Xi points its main-lobe in a (uniform) random direction

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Gain gt w.p. (1 -‐ θt/2π)

Gain Gt w.p. (θt/2π)

θt

Probability that ref. receiver is within main-lobe of Xi

Captures pt and random Tx orientation

Transmit antenna at Xi


PERFORMANCE ANALYSIS

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CCDF of SINR

u  SINR coverage probability for a given

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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


CCDF of SINR

u  SINR coverage probability for a given

where

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threshold


NUMERICAL RESULTS

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Setting

u  5 X 9 rectangular grid u  Separation between nodes = 2R0

u  No reflection from boundaries u  All nodes transmit with same Pi

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Parameters Value

R0 1

mL 4

mN 2

αL 2

αN 4

W 1

σ2 -‐20 dB

K 44

Receiver at center Receiver at a corner


Spectral efficiency for different antenna configurations

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pt = 0.1

Receiver at the center

Nt=Nr=1

Nt=Nr=16

Significant benefits to beamforming


Effect of receive antenna orientation

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Receiver at the center Receiver at a corner

pt = 0.7

Nt = Nr = 16

Orientation of RX is more important in the corner


Rate trends with Nt and Nr

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Nt Nr

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

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

Assume 2.16 GHz BW of IEEE 802.11ad

Gigabit throughputs are achieved even with a single transmit and receive antenna

pt = 1


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

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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


QUESTIONS?

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