Ecole d'ingénieur et centre de recherche en Sciences du numérique

Learning radio maps for UAV-aided wireless networks: A segmented regression approach

Chen, Junting; Yatnalli, Uday; Gesbert, David

ICC 2017, IEEE International Conference on Communications, IEEE ICC 2017 Signal Processing for Communications Symposium, 21-25 May 2017, Paris, France

This paper targets the promising area of unmanned aerial vehicle (UAV)-assisted wireless networking, by which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the networks. In order to feed the UAV's autonomous path planning and positioning algorithm, a radio map is exploited, which must be, in practice, reconstructed from UAV-based measurements from a limited subset of locations. Unlike existing methods that ignore the segmented propagation structure of the radio map, this paper proposes a machine learning approach to reconstruct a finely structured map by exploiting both segmentation and signal strength models. A data clustering and parameter estimation problem is formulated using a maximum likelihood approach, and solved by an iterative clustering and regression algorithm. Numerical results demonstrate significant performance advantage in radio map reconstruction as compared to the baseline.

Document Doi Bibtex

Titre:Learning radio maps for UAV-aided wireless networks: A segmented regression approach
Département:Systèmes de Communication
Eurecom ref:5224
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Bibtex: @inproceedings{EURECOM+5224, doi = {}, year = {2017}, title = {{L}earning radio maps for {UAV}-aided wireless networks: {A} segmented regression approach}, author = {{C}hen, {J}unting and {Y}atnalli, {U}day and {G}esbert, {D}avid}, booktitle = {{ICC} 2017, {IEEE} {I}nternational {C}onference on {C}ommunications, {IEEE} {ICC} 2017 {S}ignal {P}rocessing for {C}ommunications {S}ymposium, 21-25 {M}ay 2017, {P}aris, {F}rance}, address = {{P}aris, {FRANCE}}, month = {05}, url = {} }
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