Advertisement

A Novel MAMP Antenna Array Configuration for Efficient Beamforming

  • Dimitra D. KalyvaEmail author
  • Dimitrios K. Ntaikos
  • Constantinos B. Papadias
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 283)

Abstract

Multi-Active Multi-Passive (MAMP) antenna arrays with reduced number of active elements are studied, for matching the patterns of all-active uniform linear arrays. Based on previous work on MAMP antenna arrays, we present a novel configuration, namely a circular one. By jointly calculating the PEs’ loads and baseband weights of the proposed MAMP array, we can produce a radiation pattern similar to that of a ULA with accuracy up to 97.5%, while the number of AEs is reduced by 33% and in some cases with suppressed side lobes. Moreover, a reduction in the width of the array by 3 times is achieved. Thus, the complexity, compactness and cost of the antenna array can be reduced without compromising the quality of the resulting beam.

Keywords

Multi-active multi-passive (MAMP) arrays Hybrid antenna arrays Load and weight optimization 

References

  1. 1.
    Kalis, A., Kanatas, A.G., Papadias, C.B.: Parasitic Antenna Arrays for Wireless MIMO Systems. Springer, New York (2014).  https://doi.org/10.1007/978-1-4614-7999-4CrossRefGoogle Scholar
  2. 2.
    Papageorgiou, G.K., Ntaikos, D.K., Papadias, C.B.: Efficient beamforming with multi-active multi-passive antenna arrays. In: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5, June 2018.  https://doi.org/10.1109/SPAWC.2018.8445890
  3. 3.
    Marantis, L., et al.: The pattern selection capability of a printed ESPAR antenna. In: 2017 11th European Conference on Antennas and Propagation (EUCAP), pp. 922–926, March 2017. https://doi.org/10.23919/EuCAP.2017.7928841
  4. 4.
    Barousis, V., et al.: A stochastic beamforming algorithm for ESPAR antennas. IEEE Antennas Wirel. Propag. Lett. 7, 745–748 (2008)CrossRefGoogle Scholar
  5. 5.
    Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, vol. 65. Wiley, New York (2005)zbMATHGoogle Scholar
  6. 6.
    Wilmott, P., Howison, S., Dewynne, J.: The Mathematics of Financial Derivatives: a Student Introduction. Cambridge University Press, Cambridge (1995)CrossRefGoogle Scholar
  7. 7.
    Styblinski, M., Tang, T.S.: Experiments in nonconvex optimization: stochastic approximation with function smoothing and simulated annealing. Neural Netw. 3(4), 467–483 (1990)CrossRefGoogle Scholar
  8. 8.
    Chin, D.C.: A more efficient global optimization algorithm based on Styblinski and Tang. Neural Netw. 7(3), 573–574 (1994)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Dimitra D. Kalyva
    • 1
    Email author
  • Dimitrios K. Ntaikos
    • 1
  • Constantinos B. Papadias
    • 1
  1. 1.Athens Information TechnologyAthensGreece

Personalised recommendations