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

CNN-Based Signal Detection for Banded Linear Systems