RIS 辅助共生无线电:数字和相位调制方法

IF 7.4 1区 计算机科学 Q1 TELECOMMUNICATIONS IEEE Transactions on Cognitive Communications and Networking Pub Date : 2024-08-05 DOI:10.1109/TCCN.2024.3438461
Yehuai Feng;Qiang Li;Yingyang Chen;Qingqing Wu;Miaowen Wen
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引用次数: 0

摘要

本文为可重构智能表面(RIS)辅助共生无线电(SR)提出了一种新的反向散射调制方案,称为数相位调制(NPM)。在 RIS-NPM 中,RIS 元件的数移和相移共同用于携带反向散射信息。利用这两个信息承载单元设计了一种位映射方法。我们开发了两种分别基于瞬时和统计信道状态信息的最大似然(ML)检测器。然后设计了一种低复杂度检测器,在性能和复杂度之间实现了权衡。我们推导出了两种 ML 探测器在 Rician fading 信道上的误码率(BER)闭合上界。我们进一步将 RIS-NPM 扩展到多接收天线场景,通过将部分信息嵌入天线指数来提高频谱效率。针对这一扩展,我们通过假设基于能量的 ML 检测来分析误码率性能。计算机仿真验证了分析结果,并表明 RIS-NPM 优于 SR 中最先进的基于 RIS 的调制方案。
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RIS-Aided Symbiotic Radio: A Number and Phase Modulation Approach
In this paper, a novel backscatter modulation scheme, termed number and phase modulation (NPM), is proposed for reconfigurable intelligent surface (RIS)-aided symbiotic radio (SR). In RIS-NPM, the number and phase shifts of RIS elements are jointly utilized to carry backscatter information. A bit-mapping method is designed by using the two information-bearing units. We develop two types of maximum-likelihood (ML) detectors that are based on instantaneous and statistic channel state information, respectively. A low-complexity detector that achieves a trade-off between performance and complexity is then designed. Closed-form upper bounds on the bit error rate (BER) are derived for both ML detectors over Rician fading channels. We further extend RIS-NPM to the scenario with multiple receive antennas, enhancing the spectral efficiency by embedding partial information into the antenna indices. For this extension, BER performance is analyzed by assuming energy-based ML detection. Computer simulations verify the analysis and show the superiority of RIS-NPM over the state-of-the-art RIS-based modulation schemes in SR.
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来源期刊
IEEE Transactions on Cognitive Communications and Networking
IEEE Transactions on Cognitive Communications and Networking Computer Science-Artificial Intelligence
CiteScore
15.50
自引率
7.00%
发文量
108
期刊介绍: The IEEE Transactions on Cognitive Communications and Networking (TCCN) aims to publish high-quality manuscripts that push the boundaries of cognitive communications and networking research. Cognitive, in this context, refers to the application of perception, learning, reasoning, memory, and adaptive approaches in communication system design. The transactions welcome submissions that explore various aspects of cognitive communications and networks, focusing on innovative and holistic approaches to complex system design. Key topics covered include architecture, protocols, cross-layer design, and cognition cycle design for cognitive networks. Additionally, research on machine learning, artificial intelligence, end-to-end and distributed intelligence, software-defined networking, cognitive radios, spectrum sharing, and security and privacy issues in cognitive networks are of interest. The publication also encourages papers addressing novel services and applications enabled by these cognitive concepts.
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