| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
|
| Initial Amendment Date: | July 27, 2015 |
| Latest Amendment Date: | August 26, 2020 |
| Award Number: | 1526908 |
| 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: | September 1, 2015 |
| End Date: | December 31, 2020 (Estimated) |
| Total Intended Award Amount: | $500,000.00 |
| Total Awarded Amount to Date: | $516,000.00 |
| Funds Obligated to Date: |
FY 2020 = $16,000.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
3 RUTGERS PLZ NEW BRUNSWICK NJ US 08901-8559 (848)932-0150 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
94 Brett Road Piscataway NJ US 08901-8559 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | Comm & Information Foundations |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The availability of wireless channel maps can greatly improve the performance and reliability of wireless networks. In addition to traditional applications which depend on channel information, channel maps can be valuable in emerging applications such as communication-aware motion and path planning, network routing, connectivity maintenance and dynamic coverage, which will support improved wireless performance. In a realistic setting, the statistics of the wireless medium change dynamically in time and space. This research develops theory and algorithms for building wireless channel maps over a geographical area based on channel measurements obtained by the network nodes.
The descriptive statistics of the channel, referred to here as the channel state, are modeled as discrete time stochastic processes, evolving in time or space according to a fully or partially known statistical model. The channel state encompasses the path-loss exponent, the shadowing power and the correlation distance, and is hidden from the network nodes; the nodes can only observe their respective channel realizations. This project develops a novel framework for dynamic spatiotemporal estimation / tracking / prediction of both the channel state and the channel magnitude, in complex, nonlinearly evolving, time varying and possibly nonstationary environments. The estimation problem is approached through the rich theory of nonlinear filtering and stochastic control. Several issues are studied, including (1) Decentralized channel tracking & spatiotemporal channel prediction, (2) Event triggered sampling for efficient channel sampling, (3) Structured stochastic models for nonstationary channels. The project has an experimental component, which informs the analytical models and is also used to test/evaluate the developed methods. The project engages graduate and undergraduate students in a range of theoretical subjects and also measurements performed on WINLAB?s communications testbed.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
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.
The availability of wireless channel maps can greatly improve the performance and reliability of wireless networks. In addition to traditional applications which depend on channel information, channel maps can be valuable in emerging applications such as communication-aware motion and path planning, network routing, connectivity maintenance and dynamic coverage, which will support improved wireless performance. In a realistic setting, the statistics of the wireless medium change dynamically in time and space. The project developed a novel framework for dynamic estimation, tracking, prediction of both the channel state and the channel magnitude in fully dynamic, sufficiently structured wireless environments.
We employed the developed ideas for supporting high-speed wireless communications in urban environments. In particular, we considered the problem of enhancing Quality-of-Service (QoS) in mobile relay beamforming networks, by optimally and dynamically controlling the motion of the relays, in the presence of a dynamic channel. We assumed a time slotted system, where the relays update their positions before the beginning of each time slot. Modeling the wireless channel as a Gaussian stochastic field with spatiotemporal correlations, we proposed a novel 2-stage stochastic programming problem formulation for optimally specifying (predicting) the positions of the relays at each time slot, such that the expected QoS of the network is maximized, based on causal channel state information and under a total relay transmit power budget. Our proposed approach is particularly applicable in urban, millimeter wave (mmWave) communications, where channel spatiotemporal correlations arise due to shadowing. While mmWave communications promise high data rates, their sensitivity to blockage and severe signal attenuation present challenges in their deployment in urban settings. Our work allows mmWave communications to overcome those problems and enjoy increased communication range.
Initially, our work assumed complete knowledge of the channel model parameters. Later, we also investigated the case where parameters of the channel model are unknown, and proposed a novel reinforcement learning approach for controlling the relay motion.
Last Modified: 07/26/2021
Modified by: Athina P Petropulu
Please report errors in award information by writing to: awardsearch@nsf.gov.
