Adaptive beamforming wasinitially developed in the 1960s for the military applications of sonar andradar, in order to remove unwanted noise and jamming from output. The related the literature of the past 40 years is extremely rich [201-2371 and since this book is mainly concerned with the networking aspects of wireless systems, rather than with specific antenna array designs, here we will restrict our discussionson the topic to a rudimentaryoverview. | Third-Generation Systems and Intelligent Wireless Networking . Blogh L. Hanzo Copyright 2002 John Wiley Sons Ltd ISBNs 0-470-84519-8 Hardback 0-470-84781-6 Electronic Chapter _ Intelligent Antenna Arrays and Beamforming Introduction Adaptive beamforming was initially developed in the 1960s for the military applications of sonar and radar in order to remove unwanted noise and jamming from the output. The related literature of the past 40 years is extremely rich 201-237 and since this book is mainly concerned with the networking aspects of wireless systems rather than with specific antenna array designs here we will restrict our discussions on the topic to a rudimentary overview. The first fully adaptive array was conceived in 1965 by Applebaum 238 which was designed to maximise the Signal-to-Noise Ratio SNR at the array s output. An alternative approach to cancelling unwanted interference is the Least Mean Squares LMS error algorithm of Widrow 239 . While a simple idea satisfactory performance can be achieved under specific conditions. Further work on the LMS algorithm by Frost 240 and Griffiths 241 introduced constraints to ensure that the desired signals were not filtered out along with the unwanted signals. The optimisation process takes place as before but the antenna gain is maintained constant in the desired direction. For stationary signals both algorithms converge to the optimum Wiener solution 3 240 242 . A different technique was proposed in 1969 by Capon 243 using a Minimum-Variance Distortionless Response MVDR or the Maximum Likelihood Method MLM . In 1974 Reed et al. demonstrated the power of the Sample-Matrix Inversion SMI technique which determines the adaptive antenna array weights directly 244 . Unlike the algorithms of Applebaum 238 and Widrow 239 which may suffer from slow convergence if the eigenvalue spread of the received sample correlation matrix is relatively large the performance .
