D-Band 4.6 km 2 × 2 MIMO Photonic-Assisted Terahertz Wireless Communication Utilizing Iterative Pruning Deep Neural Network-Based Nonlinear Equalization

• Published in: Photonics 2024-11, Vol.11 (11), p.1009
• Main Authors: Lin, Jingwen, Xu, Sicong, Wang, Qihang, Zhang, Jie, Ge, Jingtao, Wang, Siqi, Ou, Zhihang, Ma, Yuan, Zhou, Wen, Yu, Jianjun
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Bandwidths; Bit error rate; Communications systems; Data transmission; deep neural network; Digital signal processing; Digital signal processors; Equalization; Error correction; Error reduction; Fiber optics; Horizontal polarization; Information processing; iterative pruning; Lasers; MIMO communication; Neural networks; photonic-aided terahertz communication system; Photonics; Polarization; Resampling; Researchers; Signal distortion; Signal processing; Signal quality; Support vector machines; Vertical polarization; Wireless communications; Wireless networks
• ISSN: 2304-6732; 2304-6732
• DOI: 10.3390/photonics11111009

In this paper, we explore the enhancement of a 4.6 km dual-polarization 2 × 2 MIMO D-band photonic-assisted terahertz communication system using iterative pruning-based deep neural network (DNN) nonlinear equalization techniques. The system employs advanced digital signal processing (DSP) methods, including down-conversion, resampling, matched filtering, and various equalization algorithms to combat signal distortions. We demonstrate the effectiveness of DNN and iterative pruning techniques in significantly reducing bit error rates (BERs) across a range of symbol rates (10 Gbaud to 30 Gbaud) and polarization states (vertical and horizontal). Before pruning, at 10 GBaud transmission, the lowest BER was 0.0362, and at 30 GBaud transmission, the lowest BER was 0.1826, both of which did not meet the 20% soft-decision forward error correction (SD-FEC) threshold. After pruning, the BER at different transmission rates was reduced to below the hard decision forward error correction (HD-FEC) threshold, indicating a substantial improvement in signal quality. Additionally, the pruning process contributed to a decrease in network complexity, with a maximum reduction of 85.9% for 10 GBaud signals and 63.0% for 30 GBaud signals. These findings indicate the potential of DNN and pruning techniques to enhance the performance and efficiency of terahertz communication systems, providing valuable insights for future high-capacity, long-distance wireless networks.