Due to the influence of the atmospheric environment and pointing errors, the performance of free space optical communication is greatly limited. In this paper, we propose a parallel multi-hop hybrid free space optical (FSO)/radio frequency (RF) system to improve the system performance. The FSO sub-link and RF sub-link are modeled by Gamma–Gamma turbulence with pointing errors and Nakagami-m distributions, respectively. Based on the selective combination scheme, the probability density function (PDF) and cumulative distribution function (CDF) of the output signal-to-noise ratio (SNR) of the hybrid FSO/RF one-hop or direct link are obtained. Then, the PDF and CDF of the output SNR of the parallel multi-hop hybrid system are derived with the decoded forward (DF) protocol considered. Finally, the expressions of the average bit error rate (ABER) and outage probability are derived for the parallel multi-hop hybrid system, the hybrid FSO/RF direct link, and the FSO-only direct link. The results show that the parallel multi-hop hybrid system can effectively mitigate the negative impact of atmospheric turbulence and pointing errors and can significantly improve the system performance.

The performance of Low-Density Parity-Check (LDPC)-coded Orthogonal Frequency Division Multiplexing (OFDM) is investigated over turbulence channels in underwater wireless optical communications (UWOC). The relation between the bit error ratio (BER) and parameters such as the scintillation coefficient, signal-to-noise ratio (SNR), length of LDPC code, and order of OFDM is quantified by simulation. Results show that while the OFDM with subcarrier quadrature amplitude modulation (QAM-OFDM) has slightly better anti-turbulence performance than the OFDM with subcarrier phase shift keying modulation (PSK-OFDM), the LDPC-coded QAM-OFDM has a much better performance than the QAM-OFDM and the LDPC-coded PSK-OFDM, and, at SNR = 12, it decreases the BER by four orders of magnitude compared to the 16QAM-OFDM system when the scintillation coefficient ${\sigma }_{\xi }^2$= 0.05.

This letter presents a novel binary matching pursuit (BMP) algorithm for the decoding of hard-decision low-density parity-check (LDPC) codes. The traditional matching pursuit algorithm over GF(2) is susceptible to mismatching and cause a chain of errors. To overcome this problem, two strategies are proposed, one is to introduce a kind of new matching reliability, and the other is to adopt a scheme of multiple decoding modules. Simulation results show that the proposed algorithm not only greatly outperforms the bit-flipping (BF) algorithm, but also outperforms the hybrid soft-decision algorithm such as weighted BF algorithm in error performance, and has the advantage of low complexity.

Signal modulation identification (SMI) has always been one of hot issues in filter-bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM), which is usually implemented by the machine learning-based feature extraction. However, it is difficult for conventional methods to extract the signal feature, resulting in a limited probability of correct classification (PCC). To tackle this problem, we put forward a novel SMI method based on deep learning to identify FBMC/OQAM signals in this paper. It is noted that the block repetition is employed in the FBMC/OQAM system to achieve the imaginary interference cancelation. In the proposed deep learning-based SMI technique, the in-phase and quadrature samples of FBMC/OQAM signals are trained by the convolutional neural network. Subsequently, the dropout layer is designed to prevent overfilling and improve the identification accuracy. To evaluate the proposed scheme, extensive experiments are conducted by employing datasets with different modulations. The results show that the proposed method can achieve better accuracy than conventional methods.

Underwater visible light communication (UVLC) system with light emitting diode (LED) as transmitter has a low transmission rate due to the limited device bandwidth and it is greatly affected by water environment quality that makes the communication comprehensive performance poor. In order to improve the channel capacity and communication quality, the channel multiplexing technology into the LED-UVLC system was applied. The FPGA was used as the data processing core, LED blue and green light emission array and avalanche photodiode (APD) receiving array were built, and a 4×4 multichannel UVLC system was designed. We placed the system in an underwater environment with a length of 20 m and achieved bidirectional transmission with a bit error rate (BER) of approximately 10⁻⁷ at a communication rate of 26.7 Mbit/s. The experimental results show that when transmitting data files of different sizes, compared with the traditional single input single output (SISO)-UVLC system, the communication time is saved about 4 times and the channel capacity gain is consistent with the simulation. In addition, with the same water quality and communication distance, the system can achieve better BER performance.