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Communications, Networking, and Signal Processing
Wireless Foundations Faculty
May 20, 2008
Grand Challenges
• Capacity of wireless networks– Abstraction of physical resources– Scalability– Architecture
• Communication, Computation and Control– Communicate to compute– Compute to communicate– Control/Sense/Estimate
• Active social networks: towards Web 4.0– Human free will and actions in the world– Incentives and semantics
• Venkat Anantharam• Michael Gastpar• Kannan Ramchandran• Anant Sahai• David Tse• Martin WainwrightLong term research: focus on signal processing,
information theory, and fundamental limits. Interface to economics and policy.
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:
Holy Grail: Capacity of Wireless Networks
• Point-to-point communication: Information theory provides a clear answer:
• Wireless networks Open problem for 30 years.
C
broadcast
interference
cooperation
Two Key Questions
• Is there a simple abstraction of the physical layer?
• Are there big gains to be had under optimal cooperation?
Deterministic Model: An Abstraction
)(rank)(cutwhere cGc
Tx
Rx1
Rx2
n1
n2
mod 2 addition
Tx1
Tx2
Rx+
+
A1
DS
A2
B1
B2
c
)(cutminflowmax c
Point-to-Point:
Theorem:
Broadcast Interference
Networks
(wireless version of Ford-Fulkerson)
Bridging the Gap
PHY Layer Higher Layers
deterministic model
The Power of Cooperation
• Baseline: no cooperation. Separate point-to-point links.• Adding terminals degrades user capacity
Node density
Cap
acity
Total system capacityPer-user capacity
Cooperation is essential for better spectrum utilization Links individually are interference-limited. Working together leads to better capacity.
1n
The Power of Cooperation
Node density
Cap
acity
Packet Multi-hop
[Ref: Gupta/Kumar’00]• shorter-range to reduce interference• a network effect
[Courtesy: R. Chandra, Microsoft Research]
Wireless Meshes
1pn
pn
The Power of Cooperation
Node density
Cap
acity
Ultimate Cooperation
[Ref: Ozgur/Leveque/Tse’07]
Cooperative MIMO
Construct large effective-aperture antenna array by combining many terminals, simultaneous transmission of many streams over longer range hierarchical cooperation minimizes overhead
Hierarchical Cooperation: A New Architecture
Shannon meets Moore: Compute to Communicate
• 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
Abstracting a model for complexity
Key idea: communicate to compute to communicate
• 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.
Joint communication/computation
Complexity shifting in distributed systems
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
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?
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.
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
Disneyland vs Yosemite: the policy dimension
• 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
Cognitive Radio Slides Follow
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
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
)
Video and Image Processing Lab
• Theories, algorithms and applications of signals; image, video, and 3D data processing;
• Director: Prof. Zakhor; founded in 1988• 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