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Improved gain vector-based recursive least squares for smart antenna applications

Improved gain vector-based recursive least squares for smart antenna applications

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In future, the improvement of the utilization of the frequency spectrum for a more desirable quality of service (QoS) hinges upon the use of smart antenna (SA) systems in wireless and cellular networks. A signal's output power can be enhanced by using SA techniques. These techniques enhance the signal power toward expected directions within the network. However, long-range communication applications still face some unsolved problems such as signal fading and cochannel interference. This chapter provides solutions to the problems of signal fading and cochannel interference by enhancing the already conventional recursive least squares (RLS) algorithms in SA arrays over long-range communication channels. This chapter further provides performance-based comparisons between the enhanced RLS algorithm and other beamforming algorithms such as the conventional RLS algorithm and the least mean squares (LMS) algorithm. In the conventional RLS algorithm, matrix inversion computations are not required. This is because the conventional RLS algorithm already determines the inverse correlation matrix directly. For this reason, the RLS algorithm can save computational power. The RLS algorithm is enhanced through the introduction of a constant denoted as m to the gain factor of the algorithm and the reason for this introduction is to yield an improved gain vector. Results from our simulation results indicate that the enhanced RLS decreases mean square error (MSE) of the signals, which makes the output of the filter smoother. These results also show that our enhanced RLS improves SNR when compared to the conventional methods. The results of this study show the benefits of improving the gain of the SA, which yields an increase in the sharpness and range of the SA over a long-range communication channel.

Chapter Contents:

  • 14.1 Introduction
  • 14.2 Related work
  • 14.3 System model
  • 14.4 Performance evaluation and results
  • 14.5 Conclusions
  • Acknowledgments
  • References

Inspec keywords: cochannel interference; interference suppression; filtering theory; adaptive antenna arrays; matrix algebra; least mean squares methods; recursive estimation; adaptive filters; least squares approximations; cellular radio; mean square error methods; recursive filters; quality of service; matrix inversion; array signal processing

Other keywords: beamforming algorithms; mean squares algorithm; signal power; conventional recursive least squares algorithms; long-range communication channel; improved gain vector-based recursive least squares; smart antenna systems; wireless networks; cochannel interference; long-range communication applications; conventional RLS algorithm; cellular networks; smart antenna applications; mean square error; enhanced RLS algorithm; signal fading

Subjects: Interpolation and function approximation (numerical analysis); Radio links and equipment; Mobile radio systems; Filtering methods in signal processing; Signal processing and detection; Interpolation and function approximation (numerical analysis); Antenna arrays; Other topics in statistics; Electromagnetic compatibility and interference

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