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CMA and soft decision-directed scheme for semi-blind beamforming of QAM systems

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  • Communication
  • Computer Science
  • Design


CMA and Soft Decision-Directed Scheme for Semi-Blind Beamforming of QAM Systems CMA and Soft Decision-Directed Scheme for Semi-Blind Beamforming of QAM Systems S. Chen, W. Yao and L. Hanzo School of Electronics and Computer Science University of Southampton, Southampton SO17 1BJ, UK E-mails: {sqc,wy07r,[email protected] Abstract—We propose a semi-blind adaptive beamforming approach for wireless systems that employ high-throughput quadrature amplitude modulation signalling schemes. A mini- mum number of training symbols, equal to the number of receive antenna-array’s elements, are first utilised to provide a rough initial least squares estimate of the beamformer’s weight vector. A concurrent constant modulus algorithm and soft decision- directed scheme, originally developed for single-user blind chan- nel equalisation, is then applied to adapt the beamformer. It is demonstrated that this semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution. I. INTRODUCTION The ever-increasing demand for wireless communication capacity has motivated the development of antenna array assisted spatial processing techniques [1], [2], [3], [4], [5], [6], [7] in order to further improve the achievable spectral efficiency. In particular, adaptive beamforming with antenna arrays [2], [5], [7] has shown real promise in achieving substantial capacity enhancement. This is because adaptive beamforming is capable of separating signals transmitted on the same carrier frequency, and thus provides a practical means of supporting multiusers in a space division multiple access scenario. In order to further improve the achievable bandwidth efficiency, high-throughput quadrature amplitude modulation (QAM) schemes [8] has become popular in numerous wireless network standards. For example, the 16-QAM and 64-QAM schemes were adopted in the WiMax standard [9]. The clas- sical beamforming design is the minimum mean square error (MMSE) solution, whi

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