Channel-Coded Precoding for Multi-User MISO Systems

Precoding is a critical and long-standing technique in multi-user communication systems. However, the majority of existing precoding methods do not consider channel coding in their designs. In this paper, we consider the precoding problem in multi-user multiple-input single-output (MISO) systems, incorporating channel coding into the design. By leveraging the error-correcting capability of channel codes we increase the degrees of freedom in the transmit signal design, thereby enhancing the overall system performance. We first propose a novel data-dependent precoding framework for coded MISO systems, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">channel-coded precoding</i> (CCP), which maximizes the probability that information bits can be correctly recovered by the channel decoder. This proposed CCP framework allows the transmit signals to produce data symbol errors at the users’ receivers, as long as the overall information BER performance can be improved. We develop the CCP framework for both one-bit and multi-bit error-correcting capacity and devise a projected gradient-based approach to solve the design problem. We also develop a robust CCP framework for the case where knowledge of perfect channel state information (CSI) is unavailable at the transmitter, taking into account the effect of both noise and channel estimation errors. Finally, we conduct numerous simulations to verify the effectiveness of the proposed CCP and its superiority compared to existing precoding methods, and we identify situations where the proposed CCP yields the most significant gains.

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Publication Details

Journal
IEEE Transactions on Wireless Communications
Published
2025-05-28
DOI
https://doi.org/10.1109/twc.2025.3571900
Citations
1
Primary Topic
Wireless Communication Networks Research
Type
article
Field-Weighted Citation Impact
1.49

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article

Channel-Coded Precoding for Multi-User MISO Systems

A. Lee Swindlehurst, Ly V. Nguyen, Junil Choi, Björn Ottersten
1 citations
IEEE Transactions on Wireless Communications
Wireless Communication Networks Research
1.49
article

Channel-Coded Precoding for Multi-User MISO Systems

A. Lee Swindlehurst, Ly V. Nguyen, Junil Choi, Björn Ottersten
article en
1 citations

Abstract

Precoding is a critical and long-standing technique in multi-user communication systems. However, the majority of existing precoding methods do not consider channel coding in their designs. In this paper, we consider the precoding problem in multi-user multiple-input single-output (MISO) systems, incorporating channel coding into the design. By leveraging the error-correcting capability of channel codes we increase the degrees of freedom in the transmit signal design, thereby enhancing the overall system performance. We first propose a novel data-dependent precoding framework for coded MISO systems, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">channel-coded precoding</i> (CCP), which maximizes the probability that information bits can be correctly recovered by the channel decoder. This proposed CCP framework allows the transmit signals to produce data symbol errors at the users’ receivers, as long as the overall information BER performance can be improved. We develop the CCP framework for both one-bit and multi-bit error-correcting capacity and devise a projected gradient-based approach to solve the design problem. We also develop a robust CCP framework for the case where knowledge of perfect channel state information (CSI) is unavailable at the transmitter, taking into account the effect of both noise and channel estimation errors. Finally, we conduct numerous simulations to verify the effectiveness of the proposed CCP and its superiority compared to existing precoding methods, and we identify situations where the proposed CCP yields the most significant gains.

IEEE Transactions on Wireless CommunicationsVol. 24(11)
Korea Advanced Institute of Science and Technology (KR), University of Luxembourg (LU), University of California, Irvine (US), Samueli Institute (US)
National Science Foundation
Openalex Percentile: Top 17%
Wireless Communication Networks Research
1.49
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