| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | June 26, 2017 |
| Latest Amendment Date: | June 26, 2017 |
| Award Number: | 1718551 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Phillip Regalia
pregalia@nsf.gov (703)292-2981 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | December 15, 2017 |
| End Date: | November 30, 2021 (Estimated) |
| Total Intended Award Amount: | $399,318.00 |
| Total Awarded Amount to Date: | $399,318.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
800 WEST CAMPBELL RD. RICHARDSON TX US 75080-3021 (972)883-2313 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
TX US 75080-3021 |
| 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): | Comm & Information Foundations |
| 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): | 47.070 |
ABSTRACT
In many emerging wireless communication technologies, the acquiring and transport of information about the state of the wireless channels has evolved into a first-order issue in the design and operation of the system. A prime example is observed in massive MIMO, especially the frequency-division duplex variety. This requires careful handling of channel resources that are used for training versus data transmission. The project is dedicated to a careful analysis of phenomena involving channel spatial correlations and synthesis of novel solutions that benefit from them, leading to more efficient channel training and transmission especially in scenarios where said efficiency can have a critical impact on wireless system performance. The research is complemented by educational and outreach activities, including training of graduate and undergraduate students.
In massive MIMO, scattering properties of physical channels produce rank-deficient covariance matrices, opening the door to a number of interesting proposals for recapturing efficiency of channel training notably to use medium- to long-term covariance eigenspaces for pre-beamforming. The variation between covariance properties of wireless nodes also occurs in scenarios other than massive MIMO. The proposed activity is dedicated to the building of a theory of correlation diversity in wireless networks animated by the idea that, much like a trader's arbitrage, it is possible to profit from differences of covariance eigenspaces, either via overlap or via separation of eigenspaces. The proposed activity aims to open a wider domain of applicability than present covariance-based techniques whose best gains are under certain antenna configurations, number of receivers, and covariance rank behavior. The proposed activity also ties in with close counterparts that are found in the time- and frequency-domain, providing an opening to a comprehensive theory of covariance and coherence disparity.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
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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 emerging wireless technologies, the collection and transport of channel state information has evolved into a first-order issue, and managing its overhead is a key component of the efficiency of wireless systems. The scattering properties of many wireless channels produces rank-deficient spatial covariance matrices, which opens the door to various proposals for improving the efficiency of channel training. For example, if the eigenspaces of the transmit correlation for different users are non-overlapping, their training can operate in parallel without interference. This idea has been developed earlier under the moniker of correlation diversity. This project broadens the idea of correlation diversity, i.e., the extraction of gains from the differences of spatial correlation matrices corresponding to different users. This broadening is motivated by practical considerations: in most operating regimes, transmit correlation matrices corresponding to different receivers have eigenspaces that are not disjoint. The project has developed a comprehensive theory for a much broader set of channel conditions, showing that the notion of correlation diversity can remain valid and useful when correlation matrices have eigenspaces that are completely or partially overlapping. Achievable degrees-of-freedom as well as achievable rates under generalized correlation diversity have been calculated. Algorithms were developed involving a combination of product superposition and rate-splitting whose implementation can approach the calculated achievable rates.
Last Modified: 03/03/2022
Modified by: Aria Nosratinia
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