de Lamare Rodrigo C, Sampaio-Neto Raimundo
Communications Research Group, Department of Electronics, University of York, Heslington, York YO105DD, North Yorkshire, UK.
IEEE Trans Neural Netw. 2008 Nov;19(11):1887-95. doi: 10.1109/TNN.2008.2003286.
A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes.
针对配备天线阵列的直接序列码分多址（DS-CDMA）系统中的联合均衡和干扰抑制问题，提出了一种使用递归神经网络（RNN）的空时自适应判决反馈（DF）接收机。所提出的接收机结构在前馈部分采用动态驱动的RNN进行均衡和多址干扰（MAI）抑制，在反馈部分采用有限脉冲响应（FIR）线性滤波器进行干扰消除。提出了一种基于集员（SM）设计框架的数据选择梯度算法，用于估计RNN结构的系数，并应用于所提出的神经接收机结构的参数估计。仿真结果表明，所提出的技术比现有方案具有显著的性能提升。
