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Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

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Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University
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Page 1: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

Ultra Low-Delay @ Edge

Ashu SabharwalECE, Rice University

Page 2: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

How Do You Make An Information Theorist Uncomfortable?

Page 3: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

How Do You Make An Information Theorist Uncomfortable?Ask About Delay

Page 4: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

Delay Has Been a Challenge: Limited Progress

Page 5: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

What’s the Bottleneck ?

• Desire reliable communication over random channels – Fading due to mobility– Noise in hardware– …

• Theorems rely on smoothening out the variability – Law of large numbers

• Need very large signaling d.o.f to smoothen out variations

• Error exponents: block length as proxy for delay

Page 6: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

1. Hope for Future = Many Many Antennas ?

• 3GPP standardizing 64-antenna configuration• Higher freq bands mean even more antennas in

same aperture

Page 7: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

Many d.o.f Simultaneously Available

Power gain

Reduced variance

Page 8: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

2. Hope For Future = Large Bandwidths?

Larger BW = more signaling d.o.f per channel use

Page 9: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

3. Hope For Future = More Feedback ?

• Feedback Reduces Delay • Fewer d.o.f to achieve the same error

probability

Page 10: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

More Feedback Via Full-Duplex ?

• A lot of talk in full-duplex has been about 2-way data

• It’s real power may be in reducing control overhead or control latency

Page 11: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

4. Hope for Future = Clustered Devices ?

• Clustering is common - “Smartphone’ing” while waiting

• Higher delay is lost revenue– Ensighten’12, 1 second delay = 7% loss in customer

conversions– Amazon’12, 1 second delay = $1.6B lost revenue– Google’12, 4/10 second delay = 8 million fewer

searches• Base-stations are becoming cloudlets

Page 12: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

Turn Disadvantage into Advantage?

• Clustered devices may have similar channels – use it to reduce protocol overhead (and hence overall delay) ?

• Clustering allows device-to-device: leverage device-to-device channels ?

Page 13: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

New Opportunities

• New design dimensions– More antennas– More bandwidth– More feedback– More ideas from the workshop

• New use cases– Low mobility access– Clustered devices– Many more in the white papers

• New theory to understand– Large simultaneous d.o.f but each with lower power– Many many users, with different traffic types

Page 14: Ultra Low-Delay @ Edge Ashu Sabharwal ECE, Rice University.

Questions/Comments ?


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