Communications, Networking, and Signal Processing

Anant Sahai

March 10, 2007

Visit Day

Open house this afternoon (2nd Floor Cory Hall)See space, meet students, see posters!

RF TagsEnergyControlPwr Scavenge

Wireless Research at UC Berkeley

WirelessRadio

Architectures

TunedRF

MEMS

SensorNetOperating System

Tiny OSLocation Estimate

SensorsTempLightHeat

Chemicalsetc

DistributedSignal Processing

Resonators

RF Relays

MEMS/CMOS

Process

MEMS

+ Law,Economics

Wireless Systems of Tomorrow

New technologies require a new way of thinking about critical resources

Yesterday TomorrowOne radio per application

Multiple radios per application & Multiple apps per radio

Long range & point to point

All ranges & comm. patterns

Frequency specific

Frequency agile

A-priori limits on power

Adaptive limits on acceptable interference

Static robustness guarantees

Must guarantee robustness dynamically

Here be dragons!

• Information theory• Robust control and signal processing• Learning and distributed adaptation• Game theory and economics• And any other sharp enough blade …

Our weapons:

Where do you fit in?

• Grad school is not “Undergrad Part II”– Not just about learning new skills and

practicing their application.– Great research is not a supercharged class

project.

• Come to Berkeley if you want to learn how to ask the right questions.– Research “taste” is what we aim to foster– One-on-one with your advisor– Ad-hoc collaborations with your fellow

students– Seminars and student-led reading groups

• High-risk/high-reward research

Samples of COM/NET/DSP Research

• New approaches to 3D modeling and Video• New understanding of error correcting

codes• New perspectives on spectrum sharing

Much more information at open houses 1-3pm…– Video Lab: 307 Cory– WiFo + Connectivity Lab: 264 Cory

Video and Image Processing Lab

• Theories, algorithms and applications of signals; image, video, and 3D data processing;

• Director: Prof. Zakhor; founded in 1988• Open house posters: 307 Cory: 1 – 3 pm• Web page: www-video.eecs.berkeley.edu• Current areas of activities:

• Fast, automated, 3D modeling, visualization and rendering of large scale environments: indoor and outdoor

• Wireless multimedia communication• Applications of image processing to IC processing: maskless

lithography; optical proximity correction

Figure 1: An example of a residential area in downtown Berkeley which has been texture mapped with 8 airborne pictures on top of 3D geometry obtained via 1/2 meter resolution airborne lidar data

Open House: 264 Cory 1-3PMWeb-page: wifo.eecs.berkeley.edu• Venkat Anantharam• Michael Gastpar• Kannan Ramchandran• Anant Sahai• David Tse• Martin WainwrightFocus on signal processing, information theory, and

fundamental limits. Interface to economics and policy.

PRISM: Distributed Source Coding (DSC) based video

coding (K. Ramchandran’s group)

X-Y

X: current frame

Y: Reference frame

MPEGDecoder

Y: Reference frame

X: current frame

Losslesschannel

MPEGEncoder

PRISM: Distributed Source Coding (DSC) based video coding (K. Ramchandran’s group)

f(X)DSCEncoder

DSCDecoder

Y’: corrupted reference frame

X: current frame X: current frame

Lossychannel

Investigation of Error Floors of Structured Low-Density Parity-Check Codes by Hardware Emulation(Zhengya Zhang, Lara Dolecek, B. Nikolic, V. Anantharam, and M. Wainwright)

satisfied check

unsatisfied check

incorrect bit

Bank1

Bits1-64

Bank2

Bits65-128

Bank3

Bits129-192

…...

Bank31

Bits1921-1984

Bank32

Bits1985-2048

Φ …

…

Bank1

Bits1-64

Bank2

Bits65-128

Bank3

Bits129-192

…...

Bank31

Bits1921-1984

Bank32

Bits1985-2048

…

Check Node

Φ Φ Φ Φ

+ + + + +- - - - -

-+

LUT LUT LUT LUT LUT

+-

+-

+-

+-

Φ …Φ Φ Φ Φ

Bit Node

... ... ... …... ... ...

Hard Decision

Channel output

MemoryM0

MemoryM1

Processing Unit 1

2 2.5 3 3.5 4 4.5 5 5.5 610

-12

10-10

10-8

10-6

10-4

10-2

100

Eb/No (dB)

FE

R/B

ER

uncoded BPSKBERFER

Region unreachable in software

Shannon meets Moore’s Law

• Transistors are free, but power is not.

• In short-range communication, this is not irrelevant.

• Shannon said that we can get arbitrarily low probability of error with finite transmit power

What is the analogy to the waterfall curve that includes decoding?

The need for guidance

• Practical question: “What should we deploy in 2010, 2015, or 2020?”– Semiconductor side: roadmap + scaling– Gives an ability to plan and coordinate work

across different levels.

• No such connection on the comm. side. – Capacity calculations do not say anything

about complexity and power.– Left to either guess, stick to tried/true

approaches, or to invest a lot of engineering effort to even understand plausibility.

• Need a path to connect to the roadmap.

• Massively parallel ASIC implementation

• Nodes have local memory– Might know a received sample– Might be responsible for a bit

• Nodes have few neighbors– (+1) maximum one-step away– Can send/get messages– Can relay for others

• Nodes consume energy– e.g. 1 pJ per iteration

• Nodes operate causally

An abstracted model for technology

Key idea: decoding neighborhoods

• Treat like a sensor network or distributed control problem.

• After a finite number of iterations, the node has only heard from a finite collection of neighbors.

• Allow any possible set of messages and computations within nodes

• Allow any possible code.

“Waterslide” curves bound total power

Assuming 1pJ, a range of around 10-40 meters, ideal kT receiver noise, and 1/r2 path loss attenuation.

Spectrum: The Looming Future

• Many heterogeneous wireless systems share the entire spectrum in a flexible and on-demand basis.

• How to get from here to there?

A new hope: breaking the interference barrier(David Tse, Ayfer Ozgur and Olivier Leveque)

Spatial Spectrum-Sharing (Gastpar)

• Each system must make sure it lives within a certain spatial interference footprint. (Requires spectrum sensing…)

• Example: To the right of the boundary, the REDs must collectively satisfy a maximum interference constraint.

• Leads to new capacity results (identify capacity “mirages”) and coding schemes

Spectrum: Where we are today

• Most of the spectrum is allocated for specific uses and users.

• But measurements show the allocated spectrum is vastly underutilized.

Semi-ideal case: perfect location information

Minimal No TalkRadius

Primary System TV

- Locations of TV transmitter and Cognitive radios are known. - Location of TV receivers is unknown Non-interference constraint translates into “Minimal No-talk” radius

Primary Receiver TV set

If we use SNR as a proxy for distance …

Minimal No TalkRadius

LOS channel

Primary System TV

- With worst case shadowing/multipath assumptions - Detector sensitivity must be set as low as -116 dBm (-98 -> -116)

Shadowing

Detection Sensitivity = -116dBm

- Un-shadowed radios are also forced to shut up

Loss in Real estate~ 100 km

Noise + interference uncertainty

Spurious tones, filter shapes, temperature changes – all impact our knowledge of noise.Calibration can reduce uncertainty but not eliminate it

Cabric et al

Spectrum Sensing: Harder than it looks

How can we reclaim this lost real estate?

Min No TalkRadius

Primary System TV

- Cooperation … can budget less for shadowing since the chance that all radios are shadowed may be very low

No Talk radiuswith cooperation

Detection Sensitivity = -116 -> -104 dBm

What if independence assumptions are not true?

Need right metrics for safety and performance

• Safety: no harmful interference to primary

• Performance: recovered area for the secondary.

• Fundamental incentive incompatibility in models– Secondary is tempted to

be optimistic in optimizing performance.

– The primary will always be more skeptical of the model.

FHI and WPAR: the right simple metrics

FHI: worst-case prob of interferenceWPAR: normalized area recovered

– Area closer to edge of primary likely to have more customers

– Area far from edge likely to have another primary.

Cooperative Safety Is Fragile!

Why should the primary trust our independence assumptions?

What if we knew the shadowing?

Minimal No TalkRadius

Primary System TV

- Then we could dynamically change our sensitivity … and regain lost real estate

Detection Sensitivity = -98dBm

Detection Sensitivity = -116dBm

Shadowing

Fremont PeakSan Juan Battista

10 co-locatedtransmitters

Sutro TowerSan Francisco28 co-locatedtransmitters

Fundamental Sparsity

GPS SatellitesMany in the sky simultaneously

Cooperation between multiband radios

Cooperation between multiband radios improves both PHI and PMO

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

PMO

versus PHI

for wideband radios cooperating using OR rule

Pro

bab

ility

of

Mis

sed

Op

po

rtu

nit

y (P M

O)

Probability of Harmful Interference (PHI

)

Wideband, Uncorrelated RadiosNarrowband Uncorrelated RadiosWideband, Correlated RadiosNarrowband Correlated Radios

Cooperation between multiband radios

Can start with low PHI, large PMO point for a single radio.

Primary just trusts that shadowing is correlated between bands.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.160

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

PMO

versus PHI

for wideband radios cooperating using OR rule

Prob

abili

ty o

f Mis

sed

Opp

ortu

nity

(PM

O)

Probability of Harmful Interference (PHI

)

Disneyland vs Yosemite

• Public owns and sets guidelines for use

• Unlicensed users are on their own

• Competition

• Owner controls access to preserve QoS for users

• “Band-managers” own band and lease it out.

• Monopoly

“Spectrum tour guide” can coordinate users without band ownership

Potential Policy Alternatives

Cognitive Radio: Opportunistic Use of Spectrum

• Reclaim underutilized spectrum– How complex must

the radios be?– Can systems operate

individually?

• “If a radio system transmits in a band that nobody is listening to, does it cause interference?”

Theory’s role: help refine architecture

• Start by studying idealized systems with perfect models.– Look for key bottlenecks for system-level

performance and ways around them in the appropriate asymptotic limits.

• Continue by modeling the impact of model uncertainty on the architecture.– If the stars have to align for the

architecture to work, it is not worth implementing.

– Identify the new bottlenecks introduced by uncertainty.

– Shape the architecture so performance and safety depend only on solid assumptions.