Referring to the system equipped with single-bit converters, 1-bit mmWave communications is gaining increasing attention for its superb cost efficiency. However, the inherent non-linear distortion renders the detectors designed for classical transparent communications inapplicable, leading to an urgent need for novel detecting solutions dedicated to 1-bit systems. Although a few endeavours have been made towards learning-based (as opposed to the traditional model-based) detectors for multi-user (MU) 1-bit systems, they are exclusively limited to narrowband channels and fail to cope with the multi-path effects inevitable to mmWave systems. In this paper, we first design a learning-based detector (LeaD) for general wideband multi-user (wMU) scenarios. Though stemming from block-based detection, the classic workhorse for transparent systems, LeaD faces either unaffordable complexity or unacceptable data rate in 1-bit systems. Given the impracticability of block-based detection, we resort to the serial detection mechanism and henceforth devise a so-termed model-enhanced (Me-)LeaD by utilizing the channel delay-domain information. Me-LeaD can be further augmented by exploiting the channel angular-domain information. Underpinned by a judiciously tailored method for extracting tbe model information, the proposed Me-LeaD demonstrates a decent overall performance in general 1-bit wMU scenarios.
Bibliographical noteFunding Information:
Manuscript received July 18, 2020; revised November 10, 2020; accepted February 7, 2021. Date of publication March 2, 2021; date of current version July 12, 2021. This work was supported in part by the Ministry National Key Research and Development Project under Grant 2019YFE0196600 and in part by the National Science Foundation under Grant CPS-2103256 and Grant ECCS-2102312. The associate editor coordinating the review of this article and approving it for publication was X. Cheng. (Corresponding author: Xiang Cheng.) Shijian Gao and Liuqing Yang are with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: firstname.lastname@example.org; email@example.com).
© 2012 IEEE.
- learning-based detector
- model enhanced learning
- model-base detector