Programmable Radios: A Personal Viewpoint

Ashu Sabharwal Rice University

Houston, TX

Congratulations to SoRa Team !

Ashu Sabharwal Rice University

Three Questions

• Why did I get involved ?

• Have I learnt anything ?

• What is on my wishlist ?

Ashu Sabharwal Rice University

Why Did I Get Involved ?

• Major speed innovations occur at PHY/MAC layer– Coding (Convolutional, Viterbi, LDPC, Turbo)– MIMO– Opportunistic scheduling

• Many more in pipeline– Cooperative coding, network codes, interference

alignment…

• Big departure from traditional networking– Need sanity checks…

But do I really need it ?

Ashu Sabharwal Rice University

Do I Really It ?

• Wireless blessed with information/comm/coding theory– Many many success stories

Ashu Sabharwal Rice University

Do I Really It ?

• Wireless blessed with information/comm/coding theory– Many many success stories

• But hear only success stories– Multiuser detection: 15+ yrs of work, never got

deployed– Beamforming: 15+ yrs, maybe finally in WiMax

Ashu Sabharwal Rice University

CONCEPT PROOF-OF-CONCEPT

Feedback loop too slow & largely broken !

What do I Need ?

• Quick and accurate answers– Physical, MAC layer tests– Full control of all variables

Programmable, measurable and deployable

Ashu Sabharwal Rice University

Hardware & Design Flows

Ashu Sabharwal Rice University

USRP WARP SoRa

WARPWARPLabWARPMAC

GNURadio SoRa

Delivering on Promises ?

• Many publications for proof-of-concept– GNUradio– WARP– Very soon using SoRa

• Fundamental shifts ?– Change in models ?– New dominant effects ?– New problem formulations ?

Ashu Sabharwal Rice University

Case I: Quantized Beamforming (WiMax/802.11n)

• Large expected gains from closed loop beam-forming

Ashu Sabharwal Rice University

Alamouti(theory)

Beamforming(theory)

Robustness to Channel Model

• Large expected gains from closed loop beam-forming

• Error floor with a small model perturbation

Ashu Sabharwal Rice University

Alamouti(actual)

Beamforming(actual)

Reason for Breakdown

• Very sensitive to how long the channel remains constant

• Breaks the equalizer and thus, whole PHY

Ashu Sabharwal Rice University

Alamouti(actual)

Beamforming(actual)

New Model, Simple Fix

• Re-model, accounting for channel change• New packet structure

Ashu Sabharwal Rice University

New Model, Simple Fix

• Beamforming advantage returns• Original model did not capture all dominant effects

Ashu Sabharwal Rice University

NewBeamforming

(actual)

New Foundations

• More generally– Feedback errors and delay can cause havoc– Transmitter and receiver get mismatched– Nearly all theory predictions breaks down

• Better models for physical layer models with fast feedback

• New fundamental results (Aggarwal & Sabharwal’09)– Proof that too many feedback bits not useful– Often more than one feedback bit is a waste !

Ashu Sabharwal Rice University

Case II: Cooperative Coding

• Physical layer, symbol time-scale cooperation• Use both routes simultaneously• Pool distributed resources of power/antennas

Ashu Sabharwal Rice University

Receiver

Relay

Source

Case II: Cooperative Coding

• No system demonstration till date• Cannot wait 15 years to know its fate

Ashu Sabharwal Rice University

Receiver

Relay

Source

Case II: Cooperative Coding on WARP

• Built with WARPLab• Allows fine-grained control of each piece• Systematic experiments to understand

dominant effects

Ashu Sabharwal Rice University

2x2x2MIMO Relay

Case II: Cooperative Coding, First Results

• Large gains with optimal processing– 6-9 dB over non-relay– 3-6 dB over simple

• No RF or A/D

Ashu Sabharwal Rice University

Optimal

Simple

Wu, Amiri, Duarte, Cavallaro’09

Case II: Cooperative Coding, First Results

• With RF– Optimal degrades a lot – Simple is robust

• Optimal very sensitive to perturbations

• Why ?– A/D robs important bits– More antennas need more

bits

Ashu Sabharwal Rice University

Optimal

Simple

Wu, Amiri, Duarte, Cavallaro’09

Wish 1: Higher Quality Radios

• Low-quality signals no post-processing can save the day

• WARP radios top of the line– But we need better to push the limits !– Better dynamic range, lower noise floor and bigger

A/D,D/A

• Clean-slate research– Platforms should be an order of magnitude better– Then research can find new sweet spots

Ashu Sabharwal Rice University

Case III: Local View in Networks

• Why current info theory of networks of little use ?

• Models miss an important component– Nodes only have local network information– Nodes mismatched in their knowledge

Ashu Sabharwal Rice University

Theory of Distributed Decisions

• Two elements (Aggarwal, Liu and Sabharwal’09)– A protocol abstraction which quantifies local view– Distributed protocols as channel codes

• First info theory analysis with hidden nodes– Predicts the losses seen in practice– Losses are unavoidable

Ashu Sabharwal Rice University

Local view

Full view limit

Cap

aci

ty

Wish 2: Cross-community Fertilization

• Wireless is many communities– CE + EE + CS– Different languages: VHDL, MATLAB, C– WARP, WARPLab, WARP_MAC

• Isolation and Integration– Isolated controlled experiments– Integration of concepts

Ashu Sabharwal Rice University

Wish 2: Cross-community Fertilization

Ashu Sabharwal Rice University

USRPWARP SoRa

WARPWARPLabWARPMAC

GNURadio SoRa

• Much remains to be done– Tools remain hard to use– Little coherence across communities

Wish 3: Hardware-“normalized” Results

• How do you compare results from different testbeds

• Different hardware– System bandwidth– Speed of processing

• Some examples– EVM, spectral efficiency– Situation likely to get worse

• Much remains to be done !

Ashu Sabharwal Rice University

Answers

• Why did I get involved ?– Problems which are unsolved and relevant

• Have I learnt anything ?– Yes, more to come !

• What is on my wish list ? – Higher quality radios– Cross-community fertilization– Hardware-normalized metrics

Ashu Sabharwal Rice University

Ashu Sabharwal Rice University

Exciting times, fun path ahead !

Questions ?

WARP: http://warp.rice.edu