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!
Wireless Research at UC Berkeley
Wireless
Radio
Architectures
SensorNet
Operating System
Tiny OS
Location Estimate
MEMS
RF Tags
EnergyControl
Pwr Scavenge
Distributed
Signal Processing
Sensors
Temp
Light
Heat
Chemicals
etc
+ Law,
Economics
Wireless Systems of Tomorrow
Yesterday
Tomorrow
One 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
New technologies require a new way of thinking about critical resources
Here be dragons!
Our weapons:
•
•
•
•
•
Information theory
Robust control and signal processing
Learning and distributed adaptation
Game theory and economics
And any other sharp enough blade …
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 1-3PM
Web-page: wifo.eecs.berkeley.edu
• Venkat Anantharam
• Michael Gastpar
• Kannan Ramchandran
• Anant Sahai
• David Tse
• Martin Wainwright
Focus 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: current frame
X: current frame
MPEG X-Y Lossless
channel
Encoder
Y: Reference frame
MPEG
Decoder
Y: Reference frame
PRISM: Distributed Source Coding (DSC) based video coding
(K. Ramchandran’s group)
X: current frame
X: current frame
DSC f(X) Lossy
channel
Encoder
DSC
Decoder
Y’: corrupted reference frame
Investigation of Error Floors of Structured Low-Density ParityCheck Codes by Hardware Emulation
(Zhengya Zhang, Lara Dolecek, B. Nikolic, V. Anantharam, and M. Wainwright)
...
10
10
FER/BER
10
10
10
10
uncoded BPSK
BER
FER
-2
Memory
M0
…...
...
...
...
LUT
LUT
LUT
Bank1
Bank2
Bank3
Bits
1-64
Bits
65-128
Bits
129192
Φ
Φ
Φ
…...
LUT
LUT
Bank31
Bank32
Bits
19211984
Bits
19852048
Φ
Φ
-4
…
-6
…
-8
-10
Processing
Unit 1
Region
unreachable
in software
Check
Node
-
-12
2
2.5
3
3.5
4
4.5
Eb/No (dB)
+
Memory
M1
10
...
Channel
output
0
5
5.5
-
-
+
+
Bank1
Bank2
Bank3
Bits
1-64
Bits
65-128
Bits
129192
6
Φ
Φ
Φ
incorrect bit
…
…...
-
+
+
Bank31
Bank32
Bits
19211984
Bits
19852048
…
Φ
Φ
Bit
Node
satisfied check
unsatisfied check
-
+
-
+
-
+
Hard Decision
-
+
-
+
Shannon meets Moore’s Law
• Shannon said that we
can get arbitrarily
low probability of
error with finite
transmit power
• Transistors are free,
but power is not.
• In short-range
communication, this is
not irrelevant.
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.
An abstracted model for technology
• Massively parallel ASIC implementation
• Nodes have local memory
– Might know a received sample
– Might be responsible for a bit
• Nodes have few neighbors
– (a+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
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
- 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 System
TV
Primary
Receiver
TV set
Minimal No Talk
Radius
If we use SNR as a proxy for distance …
- With worst case shadowing/multipath assumptions
- Detector sensitivity must be set as low as -116 dBm (-98 -> -116)
- Un-shadowed radios are also forced to shut up
LOS channel
Primary System
TV
Shadowing
Loss in Real estate
~ 100 km
Minimal No Talk
Radius
Detection Sensitivity
= -116dBm
Noise + interference uncertainty
Cabric et
al
Spurious tones, filter
shapes, temperature
changes – all impact our
knowledge of noise.
Calibration can reduce
uncertainty but not
eliminate it
Spectrum Sensing: Harder than it looks
How can we reclaim this lost real estate?
- Cooperation … can budget less for shadowing since
the chance that all radios are shadowed may be very low
Primary System
TV
No Talk radius
with cooperation
Min No Talk
Radius
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 interference
WPAR: 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?
- Then we could dynamically change our sensitivity …
and regain lost real estate
Detection Sensitivity
= -98dBm
Primary System
TV
Shadowing
Minimal No Talk
Radius
Detection Sensitivity
= -116dBm
Fundamental Sparsity
Sutro Tower
San Francisco
28 co-located
transmitters
GPS Satellites
Many in the sky
simultaneously
Fremont Peak
San Juan Battista
10 co-located
transmitters
Cooperation between multiband radios
PMO versus PHI for wideband radios cooperating using OR rule
1
Wideband, Uncorrelated Radios
Narrowband Uncorrelated Radios
Wideband, Correlated Radios
Narrowband Correlated Radios
Probability of Missed Opportunity (PMO)
0.9
Cooperati
on
between
multiband
radios
improves
both PHI
and PMO
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Probability of Harmful Interference (P HI)
0.18
0.2
Cooperation between multiband radios
PMO versus PHI for wideband radios cooperating using OR rule
Can start with
low PHI, large
PMO point for a
single radio.
1
Probability of Missed Opportunity (P MO)
0.9
0.8
0.7
0.6
Primary just
trusts that
shadowing is
correlated
between bands.
0.5
0.4
0.3
0.2
0.1
0
0
0.02
0.04
0.06
0.08
0.1
Probability of Harmful Interference (P HI)
0.12
0.14
0.16
Disneyland vs Yosemite
• Owner controls access to
preserve QoS for users
• “Band-managers” own
band and lease it out.
• Monopoly
• Public owns and sets
guidelines for use
• Unlicensed users are
on their own
• Competition
“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.