TY - GEN
T1 - Millimeter wave beamforming based on WiFi fingerprinting in indoor environment
AU - Mohamed, Ehab Mahmoud
AU - Sakaguchi, Kei
AU - Sampei, Seiichi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/8
Y1 - 2015/9/8
N2 - Millimeter Wave (mm-w), especially the 60 GHz band, has been receiving much attention as a key enabler for the 5G cellular networks. Beamforming (BF) is tremendously used with mm-w transmissions to enhance the link quality and overcome the channel impairments. The current mm-w BF mechanism, proposed by the IEEE 802.11ad standard, is mainly based on exhaustive searching the best transmit (TX) and receive (RX) antenna beams. This BF mechanism requires a very high setup time, which makes it difficult to coordinate a multiple number of mm-w Access Points (APs) in mobile channel conditions as a 5G requirement. In this paper, we propose a mm-w BF mechanism, which enables a mm-w AP to estimate the best beam to communicate with a User Equipment (UE) using statistical learning. In this scheme, the fingerprints of the UE WiFi signal and mm-w best beam identification (ID) are collected in an offline phase on a grid of arbitrary learning points (LPs) in target environments. Therefore, by just comparing the current UE WiFi signal with the pre-stored UE WiFi fingerprints, the mm-w AP can immediately estimate the best beam to communicate with the UE at its current position. The proposed mm-w BF can estimate the best beam, using a very small setup time, with a comparable performance to the exhaustive search BF.
AB - Millimeter Wave (mm-w), especially the 60 GHz band, has been receiving much attention as a key enabler for the 5G cellular networks. Beamforming (BF) is tremendously used with mm-w transmissions to enhance the link quality and overcome the channel impairments. The current mm-w BF mechanism, proposed by the IEEE 802.11ad standard, is mainly based on exhaustive searching the best transmit (TX) and receive (RX) antenna beams. This BF mechanism requires a very high setup time, which makes it difficult to coordinate a multiple number of mm-w Access Points (APs) in mobile channel conditions as a 5G requirement. In this paper, we propose a mm-w BF mechanism, which enables a mm-w AP to estimate the best beam to communicate with a User Equipment (UE) using statistical learning. In this scheme, the fingerprints of the UE WiFi signal and mm-w best beam identification (ID) are collected in an offline phase on a grid of arbitrary learning points (LPs) in target environments. Therefore, by just comparing the current UE WiFi signal with the pre-stored UE WiFi fingerprints, the mm-w AP can immediately estimate the best beam to communicate with the UE at its current position. The proposed mm-w BF can estimate the best beam, using a very small setup time, with a comparable performance to the exhaustive search BF.
UR - https://www.scopus.com/pages/publications/84947809653
U2 - 10.1109/ICCW.2015.7247333
DO - 10.1109/ICCW.2015.7247333
M3 - Conference contribution
AN - SCOPUS:84947809653
T3 - 2015 IEEE International Conference on Communication Workshop, ICCW 2015
SP - 1155
EP - 1160
BT - 2015 IEEE International Conference on Communication Workshop, ICCW 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Communication Workshop, ICCW 2015
Y2 - 8 June 2015 through 12 June 2015
ER -