| NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
| Recipient: |
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| Initial Amendment Date: | January 24, 2013 |
| Latest Amendment Date: | January 24, 2013 |
| Award Number: | 1254786 |
| Award Instrument: | Standard Grant |
| Program Manager: |
akbar sayeed
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
| Start Date: | March 1, 2013 |
| End Date: | February 28, 2018 (Estimated) |
| Total Intended Award Amount: | $400,000.00 |
| Total Awarded Amount to Date: | $400,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
450 JANE STANFORD WAY STANFORD CA US 94305-2004 (650)723-2300 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Stanford CA US 94305-4100 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CCSS-Comms Circuits & Sens Sys |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Objective:
The objective of this project is to develop a fundamental framework for simplifying wireless networks by extracting small sub-networks that provably preserve a large fraction of the entire capacity. Recent results in information theory show that optimal cooperation of a large number of wireless devices can achieve large gains in capacity. In practice, however, optimally operating many distributed devices is notoriously difficult and current cooperative techniques are limited to the use of few relays and suboptimal strategies. This project aims to bridge this divide by building on the observation that most networks can be greatly simplified through a better understanding of the inherent correlations between signals carried by different relays. It builds an information-theoretic understanding of why, how and by how much to simplify wireless networks and develops efficient simplification algorithms and low-complexity codes that approximately achieve capacity.
Intellectual merit:
The intellectual merit of the project lies in its transformative approach to the way wireless networks are studied today. Instead of focusing on a single number, the network capacity, it clarifies how information propagates through a wireless network, revealing the significance of individual relays. The study explores the rich set of connections to combinatorial optimization.
Broader impacts:
The broad goal of the project is to bridge the large disconnect between the current theory and practice of wireless relay networks by removing the complexity barrier via network simplification. The project aims to transform the way relaying is currently done in practice, allowing for significantly higher rates and smaller energy consumption.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
In this project, we have developed a fundamental framework for simplifying wireless networks by extracting small sub-networks that provably preserve a large fraction of their entire capacity. Recent results in information theory have shown that optimal cooperation of a large number of wireless devices can achieve large gains in capacity. In practice, however, optimally operating many distributed devices is notoriously difficult and current cooperative techniques are limited to the use of few relays and suboptimal strategies. In this project we have bridged this divide by developing an information-theoretic framework that allows to systematically simplify wireless networks by building on a better understanding of the inherent correlations between signals carried by different relays. We have also developed efficient simplification algorithms for both half and full-duplex wireless networks and new relaying techniques that provably approach the information theoretic network capacity.
There were many new scientific results that were discovered and published during the course of this project, with details in the publications as well as summarized in annual reports of the project. One of the main technical outcomes of the proposal was to demonstrate that a relay network with a diamond topology, where a source node communicates to a destination over a layer of relays is particularly amenable to simplification. We have shown that regardless of the number of relays and the configurations of the individual channels we can always find a single relay that alone achieves half the capacity of the whole network. More generally, we can always find a subnetwork of k relays that is sufficient to achieve a fraction of k/(k+1) of the whole network capacity. We have also developed a new relaying technique for the diamond network which provably achieves its information-theoretic capacity within a logarithmic gap in the number of relays, significantly improving on previous results which have a linear gap to capacity. We have extended our results to networks with arbitrary topology and multi-source or multicast traffic. The theoretical outcomes of the project can impact how wireless networks are designed and operated in the future leading to drastically higher communication rates with smaller network complexity.
The project resulted in many publications in top tier conferences and journals. Several undergraduate, MS students, Ph.D. students and postdoctoral researchers have worked on parts of this project and have been trained to become experts in wireless communication, information theory, networking, coding theory and combinatorial optimization. We have also integrated several ideas from this project into the curriculum through the courses taught at Stanford University.
Last Modified: 06/24/2018
Modified by: Ayfer Ozgur
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