| 面向复杂太赫兹信道的智能调制系统设计 |
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| 引用本文： | 杨平,江科,赵悦凌,肖悦.面向复杂太赫兹信道的智能调制系统设计[J].信号处理,2023,39(3):419-427. |
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| 作者姓名： | 杨平 江科 赵悦凌 肖悦 |
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| 作者单位： | 电子科技大学通信抗干扰技术国家重点实验室，四川 成都 611731 |
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| 基金项目： | 国家重点研发项目2020YFB1807203国家自然科学基金项目61876033国家自然科学基金联合基金项目U19B2014 |
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| 摘 要： | 太赫兹通信是未来高速无线通信极具潜能的技术，受到广泛关注。在本文中，提出基于简单、稀疏空域调制的太赫兹通信系统，探索了由硬件缺陷导致的信号失真对系统性能的影响，并结合收发端失真相关性，进行了系统噪声建模，得到了发端噪声、背景噪声和收端噪声的联合模型。在此多维度噪声背景下，依据后验概率最大化准则，本文推导了太赫兹空间调制系统极大似然信号检测算法。此外，考虑到未来太赫兹通信在多域多维度通信的应用场景，传统检测算法匹配度差且复杂，本文提出了利用具有简单结构的极端学习机来实现太赫兹空间调制系统的低复杂度智能算法。仿真结果表明，本文所提出的极大似然检测算法的性能优于传统的极大似然算法，另外本文中所提出的基于极端学习机的接收机方案，其性能接近最优检测方案，并且明显优于基于深度学习网络和支持向量机的方案。
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| 关 键 词： | ??太赫兹通信 空间调制 硬件失真 极端学习 误码率 |
| 收稿时间： | 2022-11-09 |
Intelligent Modulation System Designs for Complicated Terahertz Communication Channels |
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| Affiliation: | National Key Laboratory of Science and Technology on Communications，University of Electronic Science and Technology of China，Chengdu，Sichuan 611731，China |
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| Abstract: | ? ?Terahertz （THz） communication is one of the most promising candidate techniques for future ultra-high-speed wireless communications and has attracted wide attentions from academia and industry. In this paper， a novel THz communication system based on simple spatial sparse modulation—THz spatial modulation （THz-SM） was proposed， and the performance degradations caused by signal distortions， which result from transceiver hardware impairments， were investigated. According to the a posteriori maximum criterion， the maximal likelihood （ML） detection method was derived for the proposed THz-SM based on the multi-dimensional noise model. In addition， considering the application of THz to future multi-domain and multi-dimensional communication scenarios， where the conventional detection methods may be unsuitable and the imposed complexity is usually high， an extreme learning machine （ELM）-based intelligent detection algorithm having simple structure and low computational complexity was proposed. Simulation results demonstrated that the proposed ML algorithm outperforms the conventional ML， and the performance of the proposed ELM-based scheme approaches that of the optimal scheme， also are better than these of deep neural networks （DNN）-based and support vector machine （SVM）-based schemes. |
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