Multi-Antenna Broadband Backscatter Communications
<|reference_start|>Multi-Antenna Broadband Backscatter Communications: Backscatter communication offers a promising solution to connect massive Internet-of-Things (IoT) devices with low cost and high energy efficiency. Nevertheless, its inherently passive nature limits transmission reliability, thereby hindering improvements in communication range and data rate. To overcome these challenges, we introduce a bistatic broadband backscatter communication (BBBC) system, which equips the backscatter device (BD) with multiple antennas. In the proposed BBBC system, a radio frequency (RF) source directs a sinusoidal signal to the BD, facilitating single-carrier block transmission at the BD. Meanwhile, without requiring channel state information (CSI), cyclic delay diversity (CDD) is employed at the multi-antenna BD to enhance transmission reliability through additional cyclically delayed backscattered signals. We also propose a receiver design that includes preprocessing of the time-domain received signal, pilot-based parameter estimation, and frequency-domain equalization, enabling low-complexity detection of the backscattered signal. Leveraging the matched filter bound (MFB), we analyze the achievable diversity gains in terms of outage probability. Our analysis reveals that spatial diversity is achievable under general Rayleigh fading conditions, and both frequency and spatial diversity are attainable in scenarios where the forward link experiences a line-of-sight (LoS) channel. Simulation results validate the effectiveness of the proposed BBBC system. As the number of BD antennas increases, our results show that the proposed scheme not only enhances array gain but also improves diversity order, significantly reducing both outage probability and bit error rate (BER). Consequently, it outperforms conventional schemes that yield only minor gains.<|reference_end|>
@article{chen2024multi-antenna,
    title={Multi-Antenna Broadband Backscatter Communications},
    author={Hao Chen, Zhizhi Huang, Ying-Chang Liang, Robert Schober},
    journal={arXiv preprint arXiv:2408.08796},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08796},
    primaryClass={cs.IT eess.SP math.IT}
}