Cooperative receiver for ambient backscatter communications with multiple antennas

Gang Yang, Ying-Chang Liang, Qianqian Zhang
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引用次数: 29

Abstract

In ambient backscatter communications (Am-BC), a backscatter device can harvest power from ambient RF signals and modulate its information symbols over the ambient carriers without using complex RF transmitter. Conventional receiver design for AmBC focuses on tackling the direct link interference from the RF source. In this paper, a novel receiver, which is called cooperative receiver, is proposed to recover signals not only from the ambient backscatter device (A-BD), but also from the RF source. We first study the optimal maximum-likelihood (ML) detection for such system. Then, by exploiting the structural property of the system model, linear detectors and successive interference cancellation (SIC) based detectors are proposed. We also derive the closed-form bit error rate (BER) expressions for both ML detection and the proposed SIC algorithms. Finally, extensive numerical results show that the existence of backscattered signal in the considered system can significantly enhance the ML detection performance of the source signal, and the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios.
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多天线环境后向散射通信的协同接收机
在环境反向散射通信(Am-BC)中,反向散射设备可以从环境射频信号中获取能量,并在环境载波上调制其信息符号,而无需使用复杂的射频发射机。传统的AmBC接收机设计侧重于解决射频源的直接链路干扰。本文提出了一种新型的接收机,即协作接收机,它不仅可以从环境后向散射装置(a - bd)中恢复信号,而且可以从射频源中恢复信号。我们首先研究了这种系统的最优最大似然(ML)检测。然后,利用系统模型的结构特性,提出了基于线性检测器和基于逐次干扰消除(SIC)的检测器。我们还推导了ML检测和SIC算法的闭式误码率(BER)表达式。最后,大量的数值结果表明,考虑系统中背散射信号的存在可以显著增强源信号的机器学习检测性能,并且基于sic的检测器可以在典型应用场景下实现接近机器学习的检测性能。
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