OFDM-based waveform design for MIMO DFRC systems with reduced range sidelobes: a majorization-minimization approach

This paper focuses on waveform design for multi-input multi-output (MIMO) dual-function radar-communication (DFRC) systems, particularly tailored for environments with multiple single-antenna downlink user equipments (UEs). Our approach leverages orthogonal frequency division multiplexing (OFDM) tec...

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Bibliographic Details
Main Authors: Feng, Xiang, Zhao, Zhongqing, Zhao, Yufei, Zhao, Zhanfeng, Meng, Lingsheng, Guan, Yong Liang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2025
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Online Access:https://hdl.handle.net/10356/182843
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Institution: Nanyang Technological University
Language: English
Description
Summary:This paper focuses on waveform design for multi-input multi-output (MIMO) dual-function radar-communication (DFRC) systems, particularly tailored for environments with multiple single-antenna downlink user equipments (UEs). Our approach leverages orthogonal frequency division multiplexing (OFDM) technology to address the challenges of frequency-selective fading. To mitigate the peak-to-average power ratio (PAPR) issues inherent in OFDM signals, the desired low-PAPR property is also incorporated into the design of the waveforms. For enhanced radar sensing functionality, we introduce an advanced metric, the weighted peak or integrated sidelobe level (WPISL), meticulously crafted to measure and minimize low-range sidelobes. On the communication front, we integrate constructive interference (CI) techniques to significantly enhance quality of service (QoS) in data transmission. To address the intricate optimization challenges presented by our design objectives, we have developed an efficient algorithm anchored in the majorization-minimization (MM) framework. The numerical experiments demonstrate that this algorithm notably surpasses existing state-of-the-art benchmarks in reducing range sidelobe interference. Furthermore, our CI-based approach yields enhanced performance compared to traditional least squares (LS) methods, achieving lower symbol error rates (SER) and higher average achievable sum rates.