Deep Learning at the Physical Layer for Adaptive Terahertz Communications
Deep Learning at the Physical Layer for Adaptive Terahertz Communications
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DOI：
10.1109/tthz.2023.3237697
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发表时间：
2023-03
影响因子：
3.2
通讯作者：
Jacob Hall;J. Jornet;Ngwe Thawdar;T. Melodia;Francesco Restuccia
中科院分区：
文献类型：
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作者：
Jacob Hall;J. Jornet;Ngwe Thawdar;T. Melodia;Francesco Restuccia
太赫兹（THz）频段的无线通信将成为第六代（6G）网络的基石。然而，太赫兹通道提出了一些挑战，例如与距离相关的吸收系数，在移动情况下可能会显着改变带宽。因此，未来的太赫兹发射机将不得不几乎连续地切换调制和带宽。此外，使用相同的传输方案可以使对手利用智能干扰以更少的能源消耗造成更大的损害。为了帮助实现自适应和安全的太赫兹通信，本文首次通过深度学习 (DL) 技术对太赫兹频率下的调制和带宽分类 (MBC) 进行实验研究。我们在 120 GHz 频率下使用不同的调制方案、信号带宽（高达 20 GHz）和不同的信噪比 (SNR) 水平进行了广泛的实验数据收集活动。我们首次证明了 MBC 在太赫兹频率下的可行性和有效性，我们的 DL 模型在低和高 SNR 条件下的准确度分别达到 78% 和 90%。此外，我们研究了需要满足的内存和延迟约束，作为信号带宽的函数，并提出了一种增强技术，通过权衡延迟和准确性来提高推理质量。最后，我们通过 FPGA 实现实验评估 CNN 模型的延迟。
Wireless communications in the terahertz (THz) band will become a cornerstone of sixth-generation (6G) networks. The THz channel, however, presents several challenges, such as distance-dependent absorption coefficients that can change the bandwidth significantly in case of mobility. Thus, future THz transmitters will have to switch modulation and bandwidth almost continuously. Moreover, using the same transmission scheme can enable adversaries to leverage smart interfering to inflict more damage with less energy expense. To help enable adaptive and secure THz communications, this article presents the first ever experimental study of modulation and bandwidth classification (MBC) at THz frequencies through deep learning (DL) techniques. We have performed an extensive experimental data collection campaign at 120 GHz with different modulation schemes, signal bandwidth (up to 20 GHz), and different signal-to-noise ratio (SNR) levels. We prove for the first time the feasibility and effectiveness of MBC at THz frequencies, with our DL models reaching accuracy up to 78% and 90% in low- and high-SNR conditions. Furthermore, we investigate the memory and latency constraints that need to be satisfied as a function of the signal bandwidth, and propose a boosting technique to improve the inference quality by trading off latency for accuracy. Finally, we experimentally evaluate the latency of our CNN models through FPGA implementation.