标题: | 基于类神经的波束成型技术应用于4x4正交分频多天线系统的深衰落减免 A Deep Fading Minimization by Using Neuron-based Beamforming on 4X4 MIMO OFDM System |
作者: | 陈振国 Chen, Chen-Kuo 许腾尹 Hsu, Terng-Yin 资讯科学与工程研究所 |
关键字: | 数位波形;多输入多输出;无线通讯;天线;Digital Beamforming;MIMO;Wireless;Antenna |
公开日期: | 2012 |
摘要: | 数位波束成型技术可以有效地对抗干扰讯号和多重路径效果。利用波束成型技术生成权重来调整天线阵列可以有效地运用天线多重性来调整接收讯号的振幅和相位,以针对接受讯号到达接收端实的到达角度。 本论文针对多输入多输出的传输系统提出基于类神经网路的适应性数位波形技术架构,用意在于快速消除多重路径效应产生的深衰弱现象。本论文提出的演算法事基于能量关联性和类神经网路来计算数位波束成形的权重。利用能量关联性先选取出适合的波束成形权重,再利用类神经网路在有限的循环次数内收敛出可消除深衰弱的波束成形权重。 在基于SCM和数位波型传输通道中模拟,基于4*4 MIMO OFDM的Wireless Backhual平台可以显示出此演算法可有效地消除通道中所产生的深衰弱效应。在SNR=20且类神经网路的循环次数限制为10之下,可以有效地解决63%的深衰弱效应。 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. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT070056053 http://hdl.handle.net/11536/72603 |
显示于类别: | Thesis |