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Physical Layer Comm Topics in Academia David J. Love

Professor

School of Electrical and Computer Engineering

Purdue University

djlove@purdue.edu

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Mobility Driving Research and

Economy • Mobile data and

applications driving global

technology

• Crosses all age and

economic barriers

• Challenging problems:

– Communications &

Networking

– Applications

– EM compliance

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Requirements on 5G

Nokia white paper: http://networks.nokia.com/file/28771/5g-white-paper

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Research Overview

• Massive MIMO

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How Many Transmit Antennas?

• Easier to put multiple transmit antennas at the base station

– 3G WCMDA (iPhone 4 and before) – at most 2

– 4G LTE (iPhone 5, 5s) – 2 or 4 antennas at base station

– 4G LTE-Advanced (iPhone 6) – Up to 8 antennas at base station

– Multiple receive antennas can be easily leveraged

• Harder to put multiple transmit antennas on portable device

– 3G WCMDA – Single antenna

– 4G LTE – Single antenna

– 4G LTE-Advanced – Up to 4 antennas (not commercially available)

How many antennas will systems have in 5G+?

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Massive MIMO • Growing interest in employing a very large number of antennas at

base station

– Many names: Massive MIMO, FD-MIMO, Hyper-MIMO, Large-Scale MIMO

– Deploy 32+ antennas at base station

– Likely will leverage planar arrays at base

station

• Many benefits

– Increased network throughput,

– Power efficiency, robustness, etc…

16x8 planar antenna

array [1] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up MIMO: opportunities and challenges with very large arrays,” IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 40–60, Jan. 2013

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Why Is There a Benefit?

• Channel matrix will become really “fat”

Spatial Data Pipe

• A “fat” matrix has many good spatial channels to share

among users

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Sample of Research

• Channel information at transmitter is critical

• Advanced sounding schemes to provide CSI tracking

– Very difficult to sound a large number of antennas

– Rely on adaptive techniques

• Advanced quantization techniques to provide channel

feedback

– LTE/LTE-Advanced ideas no longer extend

– Must figure out how to accurately represent a large

dimensional channel matrix

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Research Overview

• Millimeter wave

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Millimeter Wave Systems • Many dire predictions for throughput

demand

Solutions:

1) Higher frequency reuse (Cooper’s law)

– Move users physically closer to a high rate link

2) Use non-traditional frequencies

Potential Licensed Bands for 5G

Millimeter wave = 28-100GHz

Nokia white paper: http://networks.nokia.com/file/28771/5g-white-paper

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Possible Architecture

• Networks of small cells (pico) connected by millimeter wave

backhaul

• User could access with conventional (e.g., LTE-like or mmwave)

• Likely Requirements

1) Must be easy to install

2) At least one node per network sees macro (beamforming) or

can collaborate (distributed beamforming)

3) Nodes could use self-organizing topology

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Beam Alignment

• Millimeter wave suffers from many problems

• Propagation – high path loss, oxygen absorption

• Implementation – advanced analog, analog-to-digital

constraints

• Beamforming at Tx and Rx is critical using MANY antennas

• Lower frequency beamforming algorithms don’t work due to

implementation constraints

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Research Overview

• Minimizing Electromagnetic Exposure

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• Portable devices are regulated on the amount of user exposure

• Measurement: Specific absorption rate (SAR)

• SAR Units = Watts/Kg

• What if SAR is not satisfied?

– Reduce transmit power

• 5G handsets

– Power constrained

– SAR constrained

User Exposure in Wireless Systems

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SAR Model • SAR is a function of:

– Device orientation

– Polarization

• SAR has historically been difficult to simulate, but there have

been recent advances

• Multiple transmit antenna handsets

Interactions between antennas! • Working with EM researchers to

design signal processing models

for SAR

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• Objective: Low SAR values and high achievable rates

• Transmit signal design in SAR constrained channel

– SAR codes

– Low SAR Space-time codes

– Capacity Analysis

• Beamforming/precoding in SAR constrained channel

– Beamforming/precoding optimization

– SAR matrix estimation

SAR Aware Transmission

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Research Overview

• Software Radio

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Spectrum Sensing and Cognitive Radio

• Proliferation of wireless technology causing and

increasingly congested and contested spectrum

environment

• Critical points:

– What other RF sources are out there?

– What is the intent of these transmitters?

– What bands are available?

Interference

Available

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Software Radio Work

• Universities increasingly involved

in experimental work using

Universal Software Radio

Peripherals (USRPs)

• DARPA held Spectrum Challenge

– Competitive tournament

– Cooperative tournament

• Excellent training environment for graduate students (and

undergraduates)

Purdue #1 qualified

finalist

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Research Overview

• Distributed MIMO

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Traditional MIMO Thinking

• Multiple paths interfere constructively and destructively at

different antennas

• Creates multiple effective spatial data paths of varying

reliability

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Distributed MIMO Thinking

• Multiple paths interfere constructively and destructively at

different antennas

• Creates multiple effective spatial data paths of varying

reliability

• What if antennas are distributed?

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Distributed MIMO Thinking

• Multiple paths interfere constructively and destructively at different antennas

• Creates multiple effective spatial data paths of varying reliability

• What if antennas are distributed?

• Distributed nodes would be connected through some form of (very low-rate) network

?

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Quantized Distributed Detection

• Through LAN/backhaul, nodes communicate with fusion center

• Each node connected to detector must be a very low rate link (e.g., per packet control bits within LAN/backhaul)

• Fuse all of the low-rate data streams together

Received signal compression

Detector

Data

Lower-rate data

(e.g., 10Mbps)

High-rate data

(e.g., 1Gbps)

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Thanks!