論文

In recent years, advances in deep learning (DL) technology have greatly improved research and services related to artificial intelligence (AI). In particular, real-time object recognition has become an important technology in smart cities. To achieve this, low-cost network deployment and low-latency data transfer are the key technologies. In this paper, we focus on Time- and Wavelength-Division Multiplexed Passive Optical Network (TWDM-PON) based inference systems to deploy cost-efficient networks that accommodate many network cameras. A significant issue for a GPU-based inference system via TWDM-PON is optimally allocating upstream wavelength and bandwidth to enable real-time inference. However, it has not been considered to increase the batch size of arrival data at edge servers ensuring low-latency transmission. Therefore, this paper proposes a concept of an inference system in which a large number of cameras periodically upload image data to a GPU-based server via TWDM-PONe We also propose a cooperative wavelength and bandwidth allocation algorithm to ensure low-latency and time-synchronized data arrival at the edge. The performance of the proposed scheme is verified with computer simulation.

In recent years, advances in deep learning technology have significantly improved the research and services relating to artificial intelligence. Real-time object recognition is an important technique in smart cities, in which low-cost network deployment and low-latency data transfer are key technologies. In this study, we focus on time- and wavelength-division multiplexed passive optical network (TWDM-PON)-based inference systems to deploy cost-efficient networks that accommodate several network cameras. A significant issue for a graphics processing unit (GPU)-based inference system via a TWDM-PON is the optimal allocation of the upstream wavelength and bandwidth to enable real-time inference. However, an increase in the batch size of the arrival data at the edge servers, thereby ensuring low-latency transmission, has not been considered previously. Therefore, this study proposes the concept of an inference system in which a large number of cameras periodically upload image data to a GPU-based server via the TWDM-PON. Moreover, we propose a cooperative wavelength and bandwidth allocation algorithm to ensure low-latency and time-synchronized data arrivals at the edge. The performance of the proposed scheme is verified through a computer simulation.

Developing dedicated hardware for each application in future optical access network systems causes significant initial investment costs. Thus, these costs are alleviated by optical access networks using general-purpose devices related to software-defined networking (SDN) and network function virtualization (NFV). We have studied physical layer virtualization to bring the advantages of SDN and NFV in optical access networks. We have focused on digital coherent technology to enhance capacity and extend reach within optical access networks. However, the future virtualized coherent access faces stringent frequency characteristics and the impact of chromatic dispersion (CD). The number of taps of finite impulse response (FIR) filters in the receiver increases rapidly for the CD compensation. The previously proposed DSP algorithm may not operate in this situation because the processing time exceeds the requirement. Therefore, this paper proposes and implements software for a lightweight multi-input-to-multi-output (MIMO) adaptive equalizer that separates FIR filters depending on the tap update period. The proposed method uses two types of FIR filters; one is used for polarization demultiplexing with short taps, and the other is a filter with long taps for the compensation of the CD in the fiber channel and the frequency distortion from the transceiver. Owing to filter separation, the calculation cost can be reduced. We indicate the processing time meeting desired requirements through real-time transmission experiments, even with increased taps. Additionally, we conduct offline simulations and experiments at a higher symbol rate than the real-time experiment.

Access-metro convergence networks (AMCN) have been proposed as a future optical communication network to realize flexible and dynamic bandwidth allocation. An AMCN requires transceivers with different wavelengths for the optical network units (ONUs). This causes an increase in production costs. Although an all-optical wavelength converter (AOWC) mitigates this issue, the AOWC increases the installation cost because it consists of many expensive optical devices. We propose the use of a remotely pumped AOWC (RP-AOWC) with an AMCN. The proposed technique can reduce the number of pump light sources in the AOWC. This paper demonstrates this reduction in the number of light sources and the feasibility of wavelength conversion using pump light propagation. We calculate the reduction in the number of pump sources and the number of transceiver types caused by the use of RP-AOWC and experiment with the wavelength conversion using pump light propagation.

This paper proposes a retransmission control system based on image recognition accuracy as a traffic reduction technique for improving network bandwidth usage efficiency in image recognition service using wireless edge computing. For traffic reduction, image compression is useful. However, it is known to deteriorate the recognition accuracy. Our proposed system is applied to guarantee this deterioration. By retransmitting images according to the recognition accuracy, we aim to reduce traffic and to guarantee recognition accuracy. When compressing the image, PSNR is calculated to adaptively change the ratio of the image compression, to maintain the image quality. This paper uses down-sampler as an image compression method and demonstrates the effectiveness of the proposed system through a wireless network simulation. We confirm that our proposed retransmission system can reduce the network traffic congestion, and guarantee the recognition accuracy as same as the conventional method.

Optical camera communication (OCC), a type of visible light communication, is expected to have various applications such as sensor devices because it can be realized at low cost and license-free by using general-purpose devices. Although the communication speed is limited by the frame rate of the camera and other factors, Color Shift Keying (CSK) is being considered as an effective means of increasing capacity. However, in mobile environments, the hue of the captured light source changes depending on the angle of the transmitter and receiver, resulting in demodulation errors. This paper proposes a dual-camera diversity selection to stabilize received optical symbols. Experiments using a synchronized dual-camera sensor kit confirm the effectiveness of the proposed approach.

Optical camera communication (OCC) represents a promising visible light communication technology. Nonetheless, typical OCC systems utilizing frame-based cameras are encumbered by limitations, including low bit rate and high processing load. To address these issues, OCC system utilizing an event-based vision sensor (EVS) as receivers have been proposed. The EVS enables high-speed, low-latency, and robust communication due to its asynchronous operation and high dynamic range. In existing event-based OCC systems, conventional modulation schemes such as on-off keying (OOK) and pulse position modulation have been applied, however, to the best of our knowledge, no modulation method has been proposed that fully exploits the unique characteristics of the EVS. This paper proposes a novel modulation scheme, called the event interval modulation (EIM) scheme, specifically designed for event-based OCC. EIM enables improvement in transmission speed by modulating information using the intervals between events. This paper proposes a theoretical model of EIM and conducts a proof-of-concept experiment. First, the parameters of the EVS are tuned and customized to optimize the frequency response specifically for EIM. Then, the maximum modulation order usable in EIM is determined experimentally. We conduct transmission experiments based on the obtained parameters. Finally, we report successful transmission at 28 kbps over 10 meters and 8.4 kbps over 50 meters in an indoor environment. This sets a new benchmark for bit rate in event-based OCC systems.

We experimentally demonstrate a CFO estimation method in the optical eigenvalue domain. A fine CFO estimation of 5.3 MHz with only one soliton pilot pulse of 6.4 ns duration is achieved.

We experimentally demonstrate a CFO estimation method in the optical eigenvalue domain. A fine CFO estimation of 5.3 MHz with only one soliton pilot pulse of 6.4 ns duration is achieved.

We demonstrate deep joint source-channel coding (DeepJSCC) over underwater visible light communication. Our system successfully reconstructs images beyond the FEC threshold at BER >10-3 where OOK fails. Experimental results confirm PSNR >22 dB under the BER conditions, highlighting DeepJSCC's robustness for lossy underwater channels.

We propose a robust demodulation scheme for optical camera communication systems using an event-based vision sensor, combining OOK with toggle demodulation and a digital phase-locked loop. This is the first report to achieve a BER<10-3 at 200 m - 60 kbps and 400 m -30kbps in outdoor experiments.

We propose a novel compensation method for nonlinear distortion in analog radio-over fiber transmission systems applicable to high-frequency band wireless communication. We demonstrate the validity of the proposed method through numerical simulations and experiments.