• DocumentCode
    2345883
  • Title

    Adaptive Combining of Signals with Unequal Noise Variances

  • Author

    Qureshi, Athar ; Kanakis, Triantafyllos ; Rapajic, Predrag

  • Author_Institution
    Medway Sch. of Eng., Univ. of Greenwich, Chatham, UK
  • fYear
    2010
  • fDate
    28-30 Sept. 2010
  • Firstpage
    488
  • Lastpage
    493
  • Abstract
    This paper demonstrates the signal combining by use of adaptive algorithms for wireless communication networks. The adaptive combiner operates under different noise variances on each branch of the multi-antenna receiver. We utilise adaptive signal combining technique with least mean squares (LMS) algorithm based on Newton´s Recursion Method. It is shown that the adaptive combining filter with LMS converges with respect to signal to noise ratio (SNR) and not to the transmit power Simulation results show that the Adaptive Combining technique proposed in this paper provides significant mean square error(MSE) improvement which reflects to BER performance improvement. This improvement is more obvious when two independent signals arrive at the receiver communication terminal (at different antennas) with 10dB of SNR difference, which is a very common situation in wireless communication systems.
  • Keywords
    antenna arrays; error statistics; least squares approximations; mean square error methods; radiocommunication; receivers; receiving antennas; BER performance improvement; Newton recursion method; adaptive combiner; adaptive combining technique; adaptive signal combining technique; least mean squares algorithm; mean square error improvement; multi-antenna receiver; receiver communication terminal; signal to noise ratio; unequal noise variances; wireless communication networks; wireless communication systems; Adaptive Signal Combining; LMS; Newton´s Recursion base LMS; Optimum Adaptive signal combining; Optimum signal combining; RLS; Unequal noise variances; formatting; style; styling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence, Modelling and Simulation (CIMSiM), 2010 Second International Conference on
  • Conference_Location
    Bali
  • Print_ISBN
    978-1-4244-8652-6
  • Electronic_ISBN
    978-0-7695-4262-1
  • Type

    conf

  • DOI
    10.1109/CIMSiM.2010.21
  • Filename
    5701894