TY - GEN
T1 - Waze-inspired spectrum discovery via smartphone sensing data fusion
AU - Lin, Sen
AU - Zhang, Junshan
AU - Ying, Lei
N1 - Funding Information:
This work is supported partially by National Science Foundation grants ECCS-1547294 and ECCS-1408409.
PY - 2018/5/22
Y1 - 2018/5/22
N2 - We study Waze-inspired spectrum discovery, where the cloud collects the spectrum sensing results from many smartphones and predicts location-specific spectrum availability based on information fusion. Observe that with limited sensing capability, each smartphone can sense only a limited number of channels; and further, the more channels each smartphone senses, the less accurate the sensing results would be. To develop a comprehensive understanding, we cast the spectrum discovery problem as a matrix recovery problem, which is different from the classical matrix completion problem, in the sense that it suffices to determine only part of the matrix entries in the matrix recovery formulation. It is shown that the widely-used similarity-based collaborative filtering method would not work well because it requires each smartphone to sense too many channels. With this motivation, we propose a location-aided smartphone data fusion method and show that the channel numbers each smartphone needs to sense could be dramatically reduced. Moreover, we analyze the partial matrix recovery performance by using the location-aided data fusion method, and numerical results corroborate the intuition that with each smartphone sensing more channels, the recovery performance improves at first but then degrades beyond some point because of the decreasing sensing accuracy.
AB - We study Waze-inspired spectrum discovery, where the cloud collects the spectrum sensing results from many smartphones and predicts location-specific spectrum availability based on information fusion. Observe that with limited sensing capability, each smartphone can sense only a limited number of channels; and further, the more channels each smartphone senses, the less accurate the sensing results would be. To develop a comprehensive understanding, we cast the spectrum discovery problem as a matrix recovery problem, which is different from the classical matrix completion problem, in the sense that it suffices to determine only part of the matrix entries in the matrix recovery formulation. It is shown that the widely-used similarity-based collaborative filtering method would not work well because it requires each smartphone to sense too many channels. With this motivation, we propose a location-aided smartphone data fusion method and show that the channel numbers each smartphone needs to sense could be dramatically reduced. Moreover, we analyze the partial matrix recovery performance by using the location-aided data fusion method, and numerical results corroborate the intuition that with each smartphone sensing more channels, the recovery performance improves at first but then degrades beyond some point because of the decreasing sensing accuracy.
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U2 - 10.23919/WIOPT.2018.8362852
DO - 10.23919/WIOPT.2018.8362852
M3 - Conference contribution
AN - SCOPUS:85048326821
T3 - 2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2018
BT - 2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2018
Y2 - 7 May 2018 through 11 May 2018
ER -