TY - GEN
T1 - Analysis of convex adaptive structures and algorithms for smart antennas
AU - Orozco-Tupacyupanqui, W.
AU - Carpio-Alemán, M.
AU - Nakano-Miyatake, M.
AU - Pérez-Meana, H.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/23
Y1 - 2017/1/23
N2 - In this paper, two different filter structures for smart antennas based on a convex combination of independent transversal adaptive sub-filters are analyzed. The first structure combines the least-mean-squares (LMS) and the augmented complex least-mean-squares (ACLMS) algorithms, whereas the second one uses the recursive least-squares (RLS) and the complex dual least-mean-squares (CDU-LMS) algorithms. The individual sub-filters are independently adapted using their own error signals, while the whole smart system is adapted by means of a convex stochastic gradient algorithm that generates an third independent error signal. The number of iterations required to reach convergence and the effects of the control parameter τ on the learning curve of the whole structure are studied. According to the simulation, these hybrid smart structures turned out to be more robust than a smart antenna that uses an unique adaptive filter. In general, both hybrid smart beamformers show to have a better filtering capacity than the standard LMS and RLS smart antenna systems. General equations for the overall output and the radiation pattern have been developed for both variations.
AB - In this paper, two different filter structures for smart antennas based on a convex combination of independent transversal adaptive sub-filters are analyzed. The first structure combines the least-mean-squares (LMS) and the augmented complex least-mean-squares (ACLMS) algorithms, whereas the second one uses the recursive least-squares (RLS) and the complex dual least-mean-squares (CDU-LMS) algorithms. The individual sub-filters are independently adapted using their own error signals, while the whole smart system is adapted by means of a convex stochastic gradient algorithm that generates an third independent error signal. The number of iterations required to reach convergence and the effects of the control parameter τ on the learning curve of the whole structure are studied. According to the simulation, these hybrid smart structures turned out to be more robust than a smart antenna that uses an unique adaptive filter. In general, both hybrid smart beamformers show to have a better filtering capacity than the standard LMS and RLS smart antenna systems. General equations for the overall output and the radiation pattern have been developed for both variations.
KW - ACLMS algorithm
KW - CDU-LMS algorithm
KW - Convex smart antennas
KW - LMS algorithm
KW - RLS algorithm
KW - adaptive algorithms
KW - hybrid smart structures
UR - http://www.scopus.com/inward/record.url?scp=85013743363&partnerID=8YFLogxK
U2 - 10.1109/ROPEC.2016.7830529
DO - 10.1109/ROPEC.2016.7830529
M3 - Contribución a la conferencia
AN - SCOPUS:85013743363
T3 - 2016 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2016
BT - 2016 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2016
Y2 - 9 November 2016 through 11 November 2016
ER -