Banded linear systems arise in many communication scenarios, e.g., those
involving inter-carrier interference and inter-symbol interference. Motivated
by recent advances in deep learning, we propose to design a high-accuracy
low-complexity signal detector for banded linear systems based on convolutional
neural networks (CNNs). We develop a novel CNN-based detector by utilizing the
banded structure of the channel matrix. Specifically, the proposed CNN-based
detector consists of three modules: the input preprocessing module, the CNN
module, and the output postprocessing module. With such an architecture, the
proposed CNN-based detector is adaptive to different system sizes, and can
overcome the curse of dimensionality, which is a ubiquitous challenge in deep
learning. Through extensive numerical experiments, we demonstrate that the
proposed CNN-based detector outperforms conventional deep neural networks and
existing model-based detectors in both accuracy and computational time.
Moreover, we show that CNN is flexible for systems with large sizes or wide
bands. We also show that the proposed CNN-based detector can be easily extended
to near-banded systems such as doubly selective orthogonal frequency division
multiplexing (OFDM) systems and 2-D magnetic recording (TDMR) systems, in which
the channel matrices do not have a strictly banded structure.

本研究使用卷积神经网络 (CNN) 设计高精度低复杂度信号检测器，通过利用通道矩阵的带状结构来提高性能，该 CNN 检测器在不同系统尺寸上表现出良好的自适应性并克服了深度学习中普遍存在的维数灾难问题。实验结果表明，与传统的深度神经网络和现有的基于模型的检测器相比，该 CNN 检测器在精度和计算时间上均表现出更好的性能，适用于具有大尺寸或宽频带的系统，并且可以轻松地扩展到近带线性系统。