Deep Learning-Aided Faster-Than-Nyquist Rate Optical Spatial Pulse Position Modulation

doi:10.3788/AOS231709

IR > 计算机与通信学院

	Deep Learning-Aided Faster-Than-Nyquist Rate Optical Spatial Pulse Position Modulation
其他题名	深度学习辅助的超奈奎斯特速率光空间脉冲位置调制
	Zhang, Yue; Ye, Xiangwen; Cao, Minghua; Wang, Huiqin
	2024-03
发表期刊	Guangxue Xuebao/Acta Optica Sinica
ISSN	0253-2239
卷号	44 期号:5
摘要	Objective As an innovative multiple-input-multiple-output (MIMO) technology, optical spatial modulation (OSM) resolves antenna interference and synchronization challenges in MIMO systems by selecting a single antenna to carry information and collectively transmits the antenna index as additional information. However, existing OSM research predominantly adheres to the orthogonal transmission criterion, and imposes limitations on enhancing the transmission rate of the system although the research is effective in avoiding inter-symbol interference. To this end, the introduction of non-orthogonal transmission via Faster-Than-Nyquist (FTN) technology compresses symbol intervals during pulse shaping, enabling an increase in transmission rate within the same bandwidth per unit time. As a result, we propose a novel Faster-Than-Nyquist rate optical spatial pulse position modulation scheme that combines OSM with FTN to further enhance the transmission rate and spectrum efficiency of the system. Additionally, in response to the highly complex receiver issue, a multiclassification neural network (MNN) decoder is proposed to significantly reduce computational complexity and achieve approximate optimal detection. Methods At the transmitting end, the input binary bit stream is divided into two groups of data blocks after serial/parallel transformations. The first group of data blocks is mapped to the index of the selected lasers for each symbol period, while the second group is mapped to pulse position modulation (PPM) symbols. An FTN shaping filter is employed to compress the PPM symbols. Then, the compressed PPM-FTN signals are loaded onto the chosen lasers for transmission. The signal traverses the Gamma-Gamma channel, and it is received by photodetectors (PDs) and converted into an electrical signal for further signal processing at the receiving end. Initially, downsampling is conducted to obtain a signal with the same dimensionality as the input signal. The downsampled signal is then classified based on its effective features, with each class being assigned the corresponding label. Subsequently, different samples with varying signal-to-noise ratios (SNRs), along with their associated label values, are utilized as input and output for offline training of a neural network model. The objective is to achieve optimal decoding accuracy by defining average loss and learning rate parameters to construct an MNN, which helps determine the number of hidden layers and neurons. Finally, the well-constructed MNN is employed for online signal detection. Then, inverse mapping is conducted on output label values from the decoder to recover the corresponding modulation symbols and laser index. Results and Discussions Monte Carlo simulations are conducted to evaluate the proposed scheme in a Gamma-Gamma channel. We first derive an upper bound of the average bit error rate (ABER) of the system and provide a comparison of the simulated BER with the ABER in Fig. 3. The results show that the two curves asymptotically coincide at high SNRs, which demonstrates the correctness of the derived ABER. Then, an analysis is performed on the influence of various parameters such as the number of lasers, the number of detectors, and modulation order on the error performance of the OSPPM-FTN system. The findings reveal that an increase in these parameters can enhance both the transmission rate and BER performance of the system, despite at varying costs. Furthermore, in Fig. 5, we compare the transmission rate, spectrum efficiency, and BER performance of the proposed system with traditional OSPPM. The results indicate that under the acceleration factor of 0.9, compared to the OSPPM system, the proposed system shows a 17% increase in spectrum efficiency and a 5.5% increase in transmission rate with only 1 dB SNR lossy. As the acceleration factor decreases from 0.9 to 0.7, the spectrum efficiency and transmission rate of the OSPPM-FTN system rise by 73% and 21.5% respectively. Thus, the proposed scheme demonstrates a significant improvement in both transmission rate and spectrum efficiency with the reduction of the acceleration factor. Through the comparison with the maximum likelihood (ML) algorithm, Figs. 7 and 8 illustrate the computational complexity reduction and BER performance of the proposed MNN decoder. The results show that the MNN decoder achieves near-optimal decoding performance, and as the detectors increases, the computational complexity of the MNN decoder is significantly lower than that of ML. For instance, when there are 8 or 16 PDs, our decoder can reduce computational complexity by 69.75% and 89.95% respectively. Conclusions A Faster-Than-Nyquis rate optical spatial pulse position modulation scheme is proposed by combining optical spatial pulse position modulation with the FTN technique, which effectively improves the transmission rate and spectrum efficiency of the system. Compared to traditional optical spatial modulation, simulation results show that the proposed scheme achieves a significant improvement in transmission rate and spectrum efficiency with the decreasing acceleration factor. Simultaneously, increasing the modulation order, the number of lasers, and the number of detectors can improve the transmission rate and error performance of the system. However, the cost associated with each parameter varies, and the selection of these parameters should be contingent on specific circumstances. Additionally, the MNN decoder proposed for the OSPPM-FTN scheme achieves near-optimal decoding performance while substantially reducing computational complexity. It is noteworthy that this advantage is particularly pronounced in large-scale MIMO systems. © 2024 Chinese Optical Society. All rights reserved.
关键词	Antennas Bit error rate Complex networks Decoding Deep learning MIMO systems Neural networks Optical communication Optical signal processing Pulse position modulation Signal to noise ratio Bit-error rate Deep learning Fast-than-nyquist Nyquist Optical spatial modulation Optical- Pulse-position modulation Spatial modulations Spectra efficiency Transmission rates
DOI	10.3788/AOS231709
收录类别	EI
语种	中文
出版者	Chinese Optical Society
EI入藏号	20241215789371
EI主题词	Spectrum efficiency
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 711.2 Electromagnetic Waves in Relation to Various Structures ; 716.1 Information Theory and Signal Processing ; 717.1 Optical Communication Systems ; 722 Computer Systems and Equipment ; 723.1 Computer Programming ; 723.2 Data Processing and Image Processing
原始文献类型	Journal article (JA)
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/170277
专题	计算机与通信学院
通讯作者	Cao, Minghua
作者单位	School of Computer and Communication, Lanzhou University of Technology, Gansu, Lanzhou; 730050, China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhang, Yue,Ye, Xiangwen,Cao, Minghua,et al. Deep Learning-Aided Faster-Than-Nyquist Rate Optical Spatial Pulse Position Modulation[J]. Guangxue Xuebao/Acta Optica Sinica,2024,44(5).
APA	Zhang, Yue,Ye, Xiangwen,Cao, Minghua,&Wang, Huiqin.(2024).Deep Learning-Aided Faster-Than-Nyquist Rate Optical Spatial Pulse Position Modulation.Guangxue Xuebao/Acta Optica Sinica,44(5).
MLA	Zhang, Yue,et al."Deep Learning-Aided Faster-Than-Nyquist Rate Optical Spatial Pulse Position Modulation".Guangxue Xuebao/Acta Optica Sinica 44.5(2024).