A Deep Fading Minimization by Using Neuron-based Beamforming on 4X4 MIMO OFDM System

• Main Authors: Chen, Chen-Kuo, 陳振國
• Other Authors: Hsu, Terng-Yin
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/52807735833097175460

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 101 === Digital beamforming is known to have interference rejection and capability against multipath effect when applying the precise steering array vector to antenna array. Steering array vector is carried out by weighting received digital signals, thereby adjusting their amplitudes and phases to form the desired beam toward AOAs (Angles of Arrival) of desired signals. In this paper, we propose a neuron-based robust adaptive beamforming in MIMO-OFDM system to solve the deep fading effect. The propose algorithm is based on correlation of power with table look-up and neural network which can accurately iterated to calculate beamforming weighting pattern. Use power correlation algorithm to choose an initial weighting patterns, and then use neural network to converge weightings pattern closed to steering vectors in order to decrease deep fading effect of multipath channel. By simulation in MIMO 4-by4 OFDM wireless backhaul system indicates that the proposed algorithm can solve deep almost 63% fading effect under SNR=20 and iteration limit=10 for neural network.