論文

Inverse scattering transform (IST)-based transmission utilizing scattering coefficient modulation has gained attention owing to its capability to facilitate multilevel modulation that avoids Kerr nonlinearity distortions in optical fiber communication systems. However, the application of soft-decision forward error correction (SD-FEC) presents challenges in accurately computing the logarithmic ratio of a posteriori probabilities (L-value) because the noise statistics of the scattering coefficient deviate from the Gaussian distribution in long-haul transmission systems. Consequently, the conventional method of approximating L-values by Gaussian distribution significantly degrades the error correction performance and constrains the practical deployment of scattering coefficient modulation. To address this limitation, we propose an L-value calculation method based on a t -distribution approximation of the noise distribution on the converted plane and introduce a neural network-based demodulation scheme as another approach. Numerical simulations indicate that the proposed t -distribution-based method substantially enhances L-value accuracy compared with the Gaussian approximation in the scattering coefficient plane, thereby achieving error-free transmission of a 64-ary amplitude-phase shift keying scattering coefficient-modulated signal with two eigenvalues at 17.1 Gbps over 2,500 km through non-zero dispersion-shifted fiber. In addition, the neural network-based demodulator extends the transmission distance to approximately 3,000 km. These findings demonstrate that appropriate statistical and machine-learning-based demodulation enables SD-FEC to exploit the potential of IST-based scattering coefficient modulation.

We investigate characteristics of a soliton-assisted CFO estimation method in the optical eigenvalue domain by numerical simulations. The soliton-assisted method demonstrates a high estimation accuracy below 10 MHz with only 64 pilot soliton pulses.

This paper proposes a space-time-domain successive interference cancellation-based adaptive equalizer (STD-SIC-AE) for underwater acoustic communication (UAC). The demand for high-capacity real-time video transmission underwater has increased for exploring ocean resources and marine research. UAC is capable of long-haul transmission and is the promising means for deep-sea exploration. However, the transmission capacity is limited because of the reflected wave from the sea surface and seafloor. It causes a multipath interference with a long propagation delay that is not easy to remove by the conventional space-time-domain equalizer. This is because that the finite impulse response (FIR) filter requires impractically huge taps. In this paper, by taking advantage of the fact that the interference (delay) wave is a direct wave that has already been received, a replica is generated by the received direct wave and the SIC is operated in the time domain. The numerical simulation verifies that our proposed STD-SIC-AE can significantly improve BER performance in terms of SNR and SIR even under higher-order modulation such as 16QAM.

We propose a novel modulation scheme of square-root-processed and optical carrier-suppressed DSB modulation to facilitate simple analog radio-over-fiber transmission systems. We demonstrate the validity of the proposed method through numerical simulations and experiments.

Communication in extreme environments is an important research topic for various use cases including environmental monitoring. A typical example is underwater acoustic communication for 6G mobile networks. The major challenges in such environments are extremely high-latency and high-error rate. They make real-time image transmission difficult using existing communication protocols. This is partly because frequent retransmission in noisy networks increases latency and leads to serious deterioration of real-timeness. To address this problem, this paper proposes a stochastic image transmission with Constrained Application Protocol (CoAP) for extreme environments. The goal of the proposed idea is to achieve approximate real-time image transmission without retransmission using CoAP over UDP. To this end, an image is divided into blocks, and value is assigned for each block based on the requirement. By the stochastic transmission of blocks, the reception probability is guaranteed without retransmission even when packets are lost in networks. We implemented the proposed scheme using Raspberry Pi 4 to demonstrate the feasibility. The performance of the proposed image transmission was confirmed from the experimental results.

Underwater wireless communication primarily utilizes ultrasound or visible light. In underwater acoustic communication (UWAC), the significant limitations imposed by sound speed and carrier frequency lead to much larger delay spread and Doppler spread compared to terrestrial wireless communication. Due to the above constraints, orthogonal frequency division multiplexing (OFDM) in UWAC commonly employs extended symbol length and a longer cyclic prefix (CP). The throughput reduction caused by frequency limitations in UWAC poses a significant challenge. This paper proposes interference suppression and channel equalization by utilizing a minimum mean square error (MMSE) adaptive array with pilot symbols, while employing shorter OFDM symbol lengths.

Various types of all-optical modulation format conversions have been proposed for realizing low latency and efficient connection of networks that have different modulation formats. All-optical PAM4 to QPSK and 16QAM modulation format conversions using an HNLF and NOLM have been demonstrated for connecting data center networks. However, there has been no study on the transmission characteristics of the converted signal over a long distance. In this study, we numerically investigate the spectral efficiency and transmission characteristics of the QPSK and 16QAM signals generated by the all-optical modulation format converters. The all-optically converted 50 Gbps class QPSK and 100 Gbps class 16QAM signals require a bandwidth of 55 GHz to maintain the EVM below 10%. We demonstrate that the converted QPSK and 16QAM signals can be transmitted up to 19,500 km and 4,000 km, respectively, in both single and 8 channels transmission by preserving the sufficient channel spacing of 62.5 GHz. Because the transmission distance of the typical inter-data-center network is shorter than 200 km, the converted signals are applicable to inter-datacenter networks. Moreover, the QPSK and 16QAM signals converted from PAM4 signals after 40 km transmission in an intra-data center network are valid for transmission in inter-data-center networks.

Optical eigenvalues of the eigenvalue equation associated with the nonlinear Schrödinger equation (NLSE) are invariant during propagation in nonlinear dispersive fiber. Optical eigenvalue communication is expected to be a technology for overcoming the Kerr nonlinear limit. Several modulation methods based on optical eigenvalue have been proposed. In this paper, we demonstrate an arbitrary optical eigenvalue modulation scheme by numerical simulations and experiments. Based on the proposed scheme, the generation and transmission of a 65 536-ary eigenvalue-modulated signal is demonstrated by numerical simulation. Moreover, we experimentally demonstrate modulation, transmission, and demodulation of a 16-ary eigenvalue-modulated signal. The 16-ary signal represented by combinations of four eigenvalues can be successfully transmitted through a 100-km standard single-mode fiber line.

This paper proposes the concept of a visible light communication (VLC) system with LED-mounted drones for post-disaster monitoring and reports the results of the experimental feasibility evaluation. Post-disaster monitoring is a critical social issue given that in the case of a large-scale disaster, communication failure can occur, and the power supply can be interrupted. A VLC system is useful for investigating the disaster situation, can assist in life-saving activity by lighting the area, and allows for communication from the disaster area to a base station. In this study, the VLC system consists of LED-mounted drones and an image-sensor-based receiver. The drones are videoed and subsequently detected by the camera with a YOLOv3-based convolutional neural network (CNN). After detecting the position of the drones, the light signal is demodulated. This paper reports the following feasibility evaluation results; 90% accuracy in drone recognition and a low bit error rate with the distance of up to 80 m between the drones and the camera.