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