Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm that integrates source and channel coding into an end-to-end learned transmission pipeline. While conventional DeepJSCC models are typically trained for a fixed compression rate, practical communication requires flexible adaptation to varying bandwidths and channel conditions. This work proposes a simple yet effective design of rateless DeepJSCC that applies stochastic masking along the channel dimension of the encoder’s final convolutional layer. By assigning learned priorities to channels and transmitting only a subset according to available resources, the model achieves rateless adaptability while preserving the CNN-based property of being independent of input image resolution. Through experiments, we validate the effectiveness of the proposed approach. On CIFAR-10, the method achieves more stable training and slightly better PSNR and SSIM compared with the conventional rateless DeepJSCC, especially at low symbol-per-pixel (SPP) rates. On DIV2K, the proposed method achieves performance comparable to individually trained fixed-rate DeepJSCC models, while maintaining the advantage of size-independence. Overall, the results demonstrate that the proposed method provides stable and efficient rate adaptation.

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.

Deep joint source-channel coding (DeepJSCC) has attracted attention as a type of semantic communication that shares not only information but also meaning and intent, and it is a type of deep learning that uses an autoencoder instead of conventional source and channel coding. DeepJSCC with convolutional neural network (CNN) layers has been studied specifically for image transmission, where it avoids cliff effects and achieves higher image quality even in the low signal-to-noise power ratio (SNR) range. In addition, DeepJSCC is suit for services related to the Internet of things (IoT), and is likely to be widely applicable to monitoring-related applications such as surveillance cameras. For this reason, it may not be possible to provide sufficient GPU resources, especially for the transmitter where the encoder is installed. Therefore, we propose a fast Fourier transform (FFT)-based DeepJSCC that replaces CNN with FFT and element-wise product to reduce the computation time of DeepJSCC. This paper evaluates the impact of changing the layer structure on image quality.

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.

Optical Camera Communication (OCC) is one of the types of visible light communication which enables low-cost and license-free communication by using general-purpose devices, such as LEDs or displays as transmitters and optical cameras as receivers. It is expected to have variety of applications. Its one of the challenges is constrained communication speed; limited by the frame rate of display and image sensors. Employing multi-level Color Shift Keying (CSK) is being considered as an effective approach to achieve higher data capacity. However, demodulation errors occur when the constellation of the received signal deviates from the reference point due to hue changes of the light source in the shooting environment or internal processing of the image sensor device. This paper proposes an equalization method that uses deep learning to compensate for received constellations and improve communication performance. A simple indoor experiment using LEDs as a light source demonstrates its fundamental effectiveness.

This paper proposes image transmission using multiple-input multiple-output (MIMO) Deep Joint Source-Channel Coding (DeepJSCC) with focusing on value-based spatial stream allocation. DeepJSCC integrates source and channel coding through deep learning, enabling robust image transmission even under low signal-to-noise power tratio (SNR) conditions. For applications such as underwater acoustic communication, limited communication capacity due to significant SNR degradation over long distances is a challenging issue. Key proposal of this paper is to assign signal stream of MIMO eigenmode spatial division multiplexing (E-SDM) for divided image blocks according to their visibility importance. Its effectiveness is validated in terms of structural similarity index measure (SSIM) and edge preservation index (EPI) under low SNR regime.

This paper proposes optimizing the compression rate of segmented image region in deep joint source-channel coding (DeepJSCC) transmission in order to improve both image quality and communication efficiency. When transmitting highresolution images with DeepJSCC, segmented transmission is employed due to computational constraints. Additionally, the structure of the output layer of the convolutional neural network (CNN) can adjust its number of transmitted symbols for each region. Focusing on this feature, we attempt to optimize the compression rate for each region of the image based on the segment's entropy value. Its validity is examined through singleinput single-output (SISO) and multiple-input multiple-output (MIMO) channels demonstrating improvements in decoding accuracy of target regions while maintaining overall image quality, especially in low-SNR environments. Beyond the entropy-based importance classification used in this paper, the potential for further accuracy improvements through more precise importance classification is also suggested.

This paper investigates the impact of time-varying channels on deep joint) source-channel coding (Deep JSCC over multiple-input multiple-output (MIMO) systems. While Deep JSCC is known for its robustness to noise, its resilience to time variations in mobile environments remains underexplored. In this paper, we first train the autoencoder using a single-input single-output (SISO) transmission under an AWGN channel on the premise that MIMO channel is equalized separately. During testing, images are transmitted over time-varying MIMO channels with various precoding techniques. The received signals are processed through a MIMO precoder before decoding. We evaluate image quality using PSNR and SSIM under various MIMO configurations. The results reveal that time variations significantly degrade Deep JSCC performance, particularly at higher mobility speeds. Its coutermeasure is increasing the number of receiving antennas that can effectively mitigates impact of channel aging.

Optical eigenvalue transmission based on inverse scattering transform (IST) has been studied for one of approaches to overcome the Kerr nonlinearity limit in fiber optic communications. In the recent decade, several multilevel modulation schemes that are based on IST, such as 16-ary and 64-ary signals using on-off encoding and b-modulation of multieigenvalues, have been proposed. To increase the transmission capacity and extend the transmission distance, applications of machine learning-based approaches to IST-based transmission have been proposed and demonstrated. Another prospective approach to increase the transmission capacity and extend the transmission distance in fiber optic communication involves the application of soft-decision forward error correction (SD-FEC). However, the applicability of SD-FEC to eigenvalue-modulated signals has not yet been investigated in detail because the distribution of the received signal is complicated for IST-based transmissions. In this paper, we describe in detail the theory of optical eigenvalue transmission, including the design of multilevel eigenvalue-modulated signals and the effects of noise and scaring parameters (coefficients for normalization of the nonlinear Schrödinger equation). We explain why neural network (NN) demodulators are advantageous for eigenvalue transmission systems. Consequently, we propose a combination of NN-based demodulators and SD-FEC decoding. A multilabel NN-based demodulator is employed to compute the L-value from the received eigenvalue pattern at the receiver. For a 16-ary eigenvalue-modulated signal, the proposed method outperformed a combination of the Gaussian approximation and SD-FEC in the simulation. Moreover, the experimental results show successful operation with error-free transmission through a 3000-km optical fiber line. In addition, we experimentally demonstrate the applicability of SD-FEC to a 4096-ary eigenvalue-modulated signal. The experimental results indicate that an achievable transmission distance can be extended to 1200 km using the NN demodulator and SD-FEC.

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.

This paper proposes a simplified enhancement of deep learning-based Joint Source-Channel Coding (Deep JSCC) over multiple-input multiple-output (MIMO) channel. Deep JSCC utilizes trained encoder and decoder weights, allowing for the integrated implementation of source coding and channel coding for batch image transmission. During the training phase, the system is trained based on single-input single-output (SISO) with additive white Gaussian noise (AWGN) communication channel principles. In the testing phase, image signals are transmitted via MIMO channels after passing through the respective Deep JSCC encoder and precoding, and subsequently, these signals are input to the decoder after MIMO detection. We examine fundamental image transmission performance in terms of PSNR and SSIM under various MIMO weight designs.

Deep learning-based joint source-channel coding (Deep JSCC) has attracted attention. Deep JSCC maps information source features directly to IQ symbols using an autoencoder instead of source and channel coding. In image transmission, severe image degradation due to the cliff effect has been a problem in the lower signal-to-noise ratio (SNR) regions. Meanwhile, Deep JSCC provides a better peak signal-to-noise ratio (PSNR) even in low SNR regions. However, a high-resolution image needs to be divided into small patch images to input into Deep JSCC, consideration of the limitation of transmission capacity and delay. The image division causes block noise and PSNR degradation. This paper proposes Deep JSCC with overlapping image division to suppress the block noise. The proposed overlap division overlaps several pixels between patch images when dividing into patch images. After image recomposition by the decoder of Deep JSCC, the receiver removes the overlapping pixel information and combines the edge of the patch images smoothly. This paper conducts the experiments to reveal the effectiveness of block noise reduction owing to the proposed scheme with DIV2K data set. Consequently, we indicate the PSNR is drastically improved in the specific SNR region.

This paper investigated the impact of a quantizer in analog-to-digital and digital-to-analog converters in communication devices on image quality when using deep learning-based joint source-channel coding modulation (JSCCM) for image transmission. In recent years, JSCCM, which efficiently encodes images and videos with low information entropy, has attracted great attention. JSCCM has a structure based on an autoencoder and determines the compression ratios for the image input by adjusting the number of IQ symbol output. The IQ symbol output from the encoder are allocated to symbol constellations with higher degrees of arbitrariness than those in typical square quadrature amplitude modulation and are therefore expected to be strongly affected by the quantization noise. In this paper, we employed quantization to the IQ symbol sequence and investigated its effect. Adjusting the quantizer's clipping ratio and the number of quantization bits, we examined the images' tolerance of the peak signal-to-noise ratio (PSNR). The simulation results showed that by adequately adjusting the clipping ratio, the image quality can be guaranteed to be equivalent to ideal conditions without quantization noise, and the number of required quantization bits that do not degrade the PSNR, was calculated.

Asynchronous Quick Link (A-QL) is the tri-color-band screen code specified as one of the transmission symbol formats in IEEE802.15.7. An edge detection algorithm is required to extract information from A-QL symbol. However, edge detection wastefully recognizes unwanted objects and it results in communication failure. To improve its detection performance, this paper proposes to apply YOLO as deep learning-based object detection and demonstrates its effectiveness.

Deep joint source-channel coding (JSCC) has been attracting attention for achieving task-oriented communication. It replaces traditional information source coding and channel coding with a deep learning-based autoencoder, directly mapping information sources such as images to IQ symbols. For images, it is claimed to avoid the cliff effect and achieve a higher peak signal noise ratio (PSNR) even in low SNR regions. While related work has assumed various propagation channel models and validated the effectiveness of Deep JSCC, there are few reports confirming its principles through experiments. Specifically, to the best of our knowledge, there are no reported examples of experiments of Deep JSCC in 5G systems. In this paper, we present a proof-of-concept of Deep JSCC in a 5G system. We modified commercially available 5G base stations (gNB) and 5G terminals to enable input and output of IQ data from external devices. We connect the 5G devices using coaxial cables and attenuators, transmit and receive JSCC signals, and evaluate the PSNR. The results demonstrate that even when communicating at power levels lower than the minimum receiver sensitivity specified in the receiver’s datasheet, the image can be successfully restored with less than 1 dB degradation in PSNR compared with the simulation result.

Optical camera communication (OCC) can be realized by commercial LEDs or displays as a transmitter and image sensors as a receiver. One of the challenges to enhance the transmission capacity in OCC is a two-dimensional light source with a display at the transmitting side. So far, the optimization of the imaging process and the method of tracking and detecting the light source have not been studied in detail. This paper proposes a dynamic light source detection system based on the A-QL method specified in IEEE 802.15.7 as a transmission symbol format. It employs YOLO, a deep learning-based object detection algorithm, and optimizes the image capture process as a symbol detection system. The proposed system can detect two-dimensional symbols with adjusting its angle and orientation. Its effectiveness and feasibility are demonstrated through an experimental evaluation.

Device-to-Device (D2D) communication is a promising solution for providing on-demand network connectivity to numerous devices. In particular, the autonomous D2D approach enables personal devices to flexibly communicate with each other with less operation. Despite all the benefits of D2D communication, security is a significant concern because of the broadcast nature of wireless communication. The biggest threats for the autonomous D2D are masquerading, impersonation, man-in-the-middle (MITM) attacks due to absence of a trusted third party. There have been many research efforts on this problem including physical layer based and the well-known Diffie-Hellman based approaches. However, they cannot be employed for authentication between physically distant devices. To address this problem, this paper proposes a multi-channel authentication for the autonomous D2D using optical camera communication (OCC). It executes the Diffie-Hellman key exchange in an optical link between a light source and a camera. The idea behind the proposed scheme is to leverage the limited reachability of OCC for ensuring security; a device can only communicate with a visible device. In this paper we introduce the security analysis for the proposed authentication and preliminary results using smartphones.

The world is often stricken by catastrophic disasters. On-demand drone-mounted visible light communication (VLC) networks are suitable for monitoring disaster-stricken areas for leveraging disaster-response operations. The concept of an image sensor-based VLC has also attracted attention in the recent past for establishing stable links using unstably moving drones. However, existing works did not sufficiently consider the one-to-many image sensor-based VLC system. Thus, this paper proposes the concept of a one-to-many image sensor-based VLC between a camera and multiple drone-mounted LED lights with a drone-positioning algorithm to avoid interference among VLC links. Multiple drones are deployed on-demand in a disaster-stricken area to monitor the ground and continuously send image data to a camera with image sensor-based visible light communication (VLC) links. The proposed idea is demonstrated with the proof-of-concept (PoC) implemented with drones that are equipped with LED panels and a 4K camera. As a result, we confirmed the feasibility of the proposed system.

As one of the realization for real-time remote monitoring and object recognition using high-definition camera images that can support safe and automated driving, this paper proposes image size reduction by differentiation from the background, focusing on fixedly installed cameras at Road-Side Unit (RSU). The video images acquired by the camera installed on RSU are transferred to the edge server, and information related to the traffic situation is recognized by image processing. Since it requires high-capacity transmission, the use of the millimeter-wave band having large bandwidth available is essential. Meanwhile, a multi-hop relay is desirable due to its short coverage. In this case, a multi-hop relay should support video image traffic from multiple RSU nodes and hence it accumulates the transmission latency. The proposed scheme greatly reduces the size of the image that needs to be transmitted, while maintaining object (vehicle) detection accuracy.

The deep joint source-channel coding and modulation (JSCCM) is a promising technology to realize efficient communication over extreme environments such as underwater area. In previous works, it is shown that deep convolutional neural networks (CNN) can successfully learn JSCCM encoder and decoder, outperforming conventional separation-based coding and modulation schemes in low signal-to-noise ratio settings. This paper proposes a new architecture for deep JSCCM based on the self-attention mechanism. We show that the proposed architecture achieves significant performance improvement compared with the CNN-based schemes while requiring a smaller network size in terms of the number of weight parameters. Furthermore, we present efficient hardware implementation of the proposed JSCCM encoder on a field programmable gate array (FPGA). In particular, we demonstrate that a systolic-array-like structure is effective for FPGA implementation of the proposed JSCCM scheme based on the self-attention mechanism.

Optical Camera Communication (OCC) is a promising solution for long-range point-to-multipoint communication between drones and a ground camera. OCC requires line-of-sight (LoS) channels for a camera to receive optical signals transmitted from drone-mounted LED lights or panels. When multiple drones are deployed in a certain area to transmit optical signals simultaneously, inter-light interference avoidance is a significant issue. The inter-light interference has been modeled in the previous works. However, existing works have not sufficiently investigated the trajectory control of drones. To address this issue, in this paper, we propose a distributed trajectory control algorithm for drones to avoid inter-light interference. Based on the approximate interference model in the image plane, each drone controls its trajectory to ensure LoS communication links of other drones. The performance of the proposed algorithm is confirmed via intense multi-agent simulation. The proposed algorithm contributes to establishing stable point-to-multipoint OCC links between drones and a ground camera.

Underwater networks are expected to be service platforms for broad-sea and deep-sea activities. The significant challenge of underwater communication has been achieving high-speed and long-distance data transmission due to the high-attenuation and time-varying channel state in underwater environments. It is reasonable to get the underwater data above the water surface for establishing underwater-ground networks. However, it is still an unsolved issue to efficiently establish underwater-ground communication channel. To address this problem, we propose an aquatic fronthaul for underwater-ground communication, where floating aquatic relay nodes relay data from underwater drones/sensors to a ground radio unit. We propose a relocation algorithm for aquatic relay nodes to efficiently reconstruct the network according to the distribution of underwater nodes. The advantage of the proposed algorithm is robustness for the uncertainty of underwater node locations due to the difficulty in underwater localization. The performance of the proposed algorithm was evaluated with multi-agent simulations. The feasibility of the aquatic fronthaul network was confirmed via the experimental results with a Wi-Fi mesh network above the water.

Underwater communication is one of the biggest challenges for 6G communications to provide ubiquitous connectivity all over the world. Although optical communication is a strong option for underwater communication, the directivity control of laser beams has been a considerable practical issue due to oceanic turbulence. While optical camera communication (OCC) is an emerging technology for next generation wireless communication, there have been few works on underwater OCC (UWOCC). The propagation characteristics of the optical signals transmitted from LED lights in UWOCC have not been well studied. Therefore, This paper proposes a predictive equalization technique for UWOCC assuming color shift keying (CSK), where the optical signals are modulated by modifying the intensity of the three-color LED luminaires. The proposed technique predictively equalizes the received signals from the symbol color and link distance leveraging the different attenuation of light intensity depending on the wavelength. We demonstrate the feasibility of the proposed idea via experimental results at a depth of 3.5 meters. The proposed equalization improved symbol error rate (SER) so that the overall bit error rate (BER) was significantly suppressed.

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.

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.

The Internet of Things (IoT) has been growing rapidly in recent years. With the appearance of 5G, it is expected to become even more indispensable to people's lives. In accordance with the increase of Distributed Denial-of-Service (DDoS) attacks from IoT devices, DDoS defense has become a hot research topic. DDoS detection mechanisms executed on routers and SDN environments have been intensely studied. However, these methods have the disadvantage of requiring the cost and performance of the devices. In addition, there is no existing DDoS mitigation algorithm on the network edge that can be performed with the low-cost and low-performance equipment. Therefore, this paper proposes a light-weight DDoS mitigation scheme at the network edge using limited resources of inexpensive devices such as home gateways. The goal of the proposed scheme is to detect and mitigate flooding attacks. It utilizes unused queue resources to detect malicious flows by random shuffling of queue allocation and discard the packets of the detected flows. The performance of the proposed scheme was confirmed via theoretical analysis and computer simulation. The simulation results match the theoretical results and the proposed algorithm can efficiently detect malicious flows using limited resources.

Underwater communication is a promising technology to provide ubiquitous network connectivity, where acoustic waves are used as the primary carrier for long-range communication. It has been a challenging research topic to efficiently transmit images with under-water acoustic communication (UAC), due to its inherently narrow bandwidth, strong signal attenuation, time-varying multipath propagation, and low propagation speed. In this paper, we present a new approach to addressing these limitations in UAC, namely the joint source-channel coding and modulation (JSCCM) based on a deep neural network (DNN). We develop a training method of DNN-based encoder and decoder, which directly encode/decode image-pixel values to modulated symbols, unlike conventional separation-based source and channel coding and modulation. Through numerical simulations, the deep JSCCM is confirmed to achieve significantly higher data-rate than conventional schemes.

Optical Camera Communication (OCC) is a promising solution for future wireless communication thanks to the advantages including security, license, and cost-efficiency. Widely available smart devices with em-bedded cameras such as smartphones, tablets, and digital cameras can be employed as receivers in OCC with-out modifying hardware. A complementary metal-oxide-semiconductor (CMOS) sensor in a camera can receive optical signals from multiple light sources at the same time. Since the light source occupies a certain area in the image plane, the received signal powers differ among the corresponding pixels. When the number of light sources increase, the signals from a light source can be blocked by another light source or affected by the blooming effect of other optical signals. However, there has never been a general model for avoiding such interference in OCC. Therefore, in this paper we propose a general model for avoiding inter-light sources interference. The proposed model formulates the constraints with perspective transformation based on the parameters of an image sensor and a camera. We also provide preliminary experimental results to validate the proposed model.

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) is an emerging technology for wireless communication between smart devices such as smartphones. A light source such as a LED light and a LED panel is employed as a transmitter in OCC. Among the various modulation schemes, color shift keying (CSK) has attracted considerable attention to improve throughput. CSK exploits the design of three-color LED luminaires. Although CSK is a promising modulation technique, the relationship between symbol colors and external environments has not been considered yet. Some colors become difficult to identify depending on environments including ambient light. To address this problem, this paper proposes an adaptive (N+1)-CSK to define one more color in addition to general CSK. The goal of the proposed scheme is to reduce bit errors by explicitly defining a base color, i.e. NULL color. One symbol is adaptively selected as the base color from the N+1 symbols according to external conditions, and data signals are transmitted using other symbols. We also provide preliminary experimental results to validate the proposed scheme. The bit error rate (BER) is significantly suppressed by appropriately setting the base color depending on the pilot signals of each symbol.

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.

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.

A mobile base station (MBS) comprises a central unit (CU), a distributed unit (DU), and a remote unit (RU) for efficient deployment in 5th-generation mobile communications system and beyond. The link between a DU and an RU is established using an optical fiber and is renowned as fronthaul (FH). Our study aims to reduce the cost of this FH link. Networking and wirelessly connecting the FH link have been studied to suppress the FH link cost and to flexibly deploy RU1s. Wireless fronthauling is an important solution because it can dispense with optical fiber laying. The location of MBSs is also an important issue. RUs are densely deployed to gain high wireless throughput per area. A DU should be located at a place where RUs can perform cooperative operation and the latency requirement is satisfied. To reduce the optical fiber deployment cost, we studied the DU placement design using wireless multihop links and point-to-point (PtP) optical links. It determines the DU locations to increase the wireless links for reducing the deployment cost. The problem of this scheme is that most of the RUs are still connected to the DU via PtP optical links. This paper proposes a novel FH designing scheme that employs a passive optical network (PON) and wireless multi-hop as well as PtP optical links. We propose a separate designing algorithm for the wireless and PON links. The optical fiber link cost is observed to reduce when compared with that of the previously proposed scheme using a simulation.

Centralized radio access network (C-RAN) architecture prevails to efficiently forward the ever increasing mobile traffic towards beyond 5G era. Densely deployed radio units (RUs) compose small cells and an ultra high-density distributed antenna system (UHD-DAS). Distributed units (DUs) are placed close to RUs and linked to them via fronthaul to satisfy strict latency requirement. The DUs are connected to a central unit (CU) installed in a central office through optical midhaul links. Although it has been a hot research topic to compose a fronthaul network, there has been little research on the concept of midhaul networking. Therefore, this paper proposes the C-RAN architecture which consists of passive optical network (PON)-based midhaul links and wireless relay fronthaul networks. The goal of the proposed idea is to reduce fiber deployment cost by optical fiber reduction. We also propose a joint routing and dynamic wavelength and bandwidth allocation (DWBA) algorithm for optimally allocating resources considering the bandwidth utilization and the latency requirement. It is confirmed through computer simulations that the accommodation efficiency of optical midhaul can increase fourfold with the proposed scheme.

This paper proposes the blind interference power estimation via deep learning approach exploiting the visualized wireless signal information. Blind adaptive array (BAA) signal processing is the powerful solution to suppress various kinds of interference such as inter-cell interference (ICI) and intersystem interference (ISysI) for which receivers cannot obtain a priori information represented as channel state information (CSI). However, BAAs cannot always suppress interference due to its blind nature. Depending on signal-to-interference power ration (SIR), adequate BAA algorithms should be switched. In order to estimate SIR in a blind manner, we propose to apply a convolutional neural network (CNN) trained by IQ constellation images where contains the desired and interference signals. This paper presents its methodology and fundamental possibility.

The concept of vehicle-mounted crowdsourced radio units (CRUs) for a smart city has been proposed to utilize the power of citizens in the deployment of small cells of the centralized radio access network (C-RAN) architecture. Wireless relay fronthaul networking is a promising solution for efficient utilization of vehicle-mounted small cells. However, there have been no routing schemes that can satisfy the strict delay requirements of mobile fronthaul coping with the high dynamicity of vehicles. Thus, this paper proposes a real-time routing scheme for establishing wireless relay fronthaul with vehicle-mounted CRUs. The route optimization is formulated as a boolean satisfiability problem (SAT), and an FPGA-based SAT solver is employed for the fast computation. It can dynamically optimize the forwarding paths in real-time with the constraints of delay requirements. The performance of the proposed routing scheme is confirmed via computer simulations.

In metropolitan areas, spatio-temporal patterns of human mobility result in significant fluctuations of mobile traffic. Such fluctuations drastically deteriorate the efficiency and financial viability of conventional mobile networks. This is because mobile networks have been designed to cope with the peak traffic, and thus their capacities are underutilized for most of time. To make matters worse, this trend will be intensified with the increase in mobile traffic. To address this issue, this paper proposes a concept of adaptive mobile network architecture with moving nodes towards beyond 5G era. It consists of densely deployed radio units (RUs) and moving distributed units (DUs) in the centralized radio access network (C-RAN) architecture. The mobile traffic is forwarded through optical midhaul links and wireless relay fronthaul links satisfying the latency requirement. This paper also proposes an algorithm for optimizing the activation states of RUs, the relocation schedule of DUs, and forwarding paths of fronthaul streams according to the demand distribution. It was confirmed with computer simulations that the proposed architecture can efficiently activate RUs and DUs by optimizing the location of DUs and forwarding paths of fronthaul streams.

The centralized radio access network (C-RAN) architecture prevails in beyond 5G mobile networks. Along with the cell size reduction, the efficiency of C-RAN architecture is drastically deteriorated by the spatio-temporal fluctuations in mobile traffic demand. To address this problem, we proposed a concept of adaptive C-RAN architecture for smart cities with crowdsourced radio units (CRUs). The advantages of the proposed scheme are high flexibility and low cost, because the distribution of CRUs follows that of mobile users. This paper introduces the experimental results on the performance of radio units mounted on parked vehicles to show the efficacy of the proposed scheme.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) has attracted considerable attention for the next generation optical access systems. Among potential applications of TWDM-PON, a major application is the support of mobile fronthaul streams between radio units (RUs) and distributed units (DUs) in the centralized radio access network (C-RAN) architecture, which consists of central units (CUs), DUs, and RUs. The upstream fronthaul traffic that an optical line terminal (OLT) receives is expected to become highly bursty due to the variable data rate generated by employing new functional split options and the synchronization of data transmission between neighboring RUs caused by time-division duplex (TDD). However, there has been no wavelength and bandwidth allocation scheme for TWDM-PON that is designed to efficiently accommodate fronthaul streams satisfying the strict delay requirement. Therefore, in this paper we propose a novel wavelength and bandwidth allocation algorithm that can minimize the number of active wavelength channels considering the high burstiness and delay requirement of fronthaul data transmission. Through computer simulations it was confirmed that the number of active wavelength channels can be reduced by 50% with the proposed algorithm, and thus more RUs can be efficiently accommodated using TWDM-PON.

A new functional split mobile base station (MBS) has been getting attention for 5G and beyond 5G(BSG) to reduce an optical bandwidth. The MBS is split into three components: a central unit (CU), a distributed unit (DU), and a radio unit (RU). The link between a DU and a RU is connected by an optical fiber and well known as fronthaul. In particular, RUs will be densely deployed in antenna site to increase the mobile data rate. There is inefficiency in the bandwidth usage in the fronthaul link because all the RUs are not always in activated state. Therefore, a mobile operator needs a cost-effective fronthaul network. Employing the wireless multihop system has been studied to construct 5G/B5G fronthaul network. Both optical and wireless links should be employed since it is challenging to replace all of the optical link to the wireless link. However, the method of the deployment of the DU site to reduce the optical fiber cost has not been considered. This paper proposes a novel efficient deployment design of DU site incorporating wireless multihop connection in order to satisfactory reduce optical fiber deployment cost.

Vehicle-installed access points (VAPs) based mobile network emerges as a flexible and efficient wireless access means for 5G and beyond. This paper reports analytical results on vehicle-to-pedestrian (V2P) connectivity on higher frequency band. Introducing 5G new radio (NR) numerology can expand applicability of orthogonal frequency division multiplexing (OFDM) on such intensive mobility environment where VAPs move past. Additional proposal is a coordinated multipoint (CoMP) based coverage expansion via multiple VAPs. Simulative evaluation also presents its effectiveness based on street canyon scenario.

In future radio access systems, since the number of mobile base stations will increase to cope with the increasing mobile traffic, the number of mobile fronthaul (MFH) links will also have to increase. To reduce the MFH link cost, MFH networking has been attracting attention. In particular, the use of a time-division multiplexed passive optical network (TDM-PON) makes the MFH link cost effective. On the other hand, a TDM-PON has a huge latency when forwarding uplink traffic. In a typical dynamic bandwidth allocation (DBA) scheme, an optical network unit (ONU) has a very long transmission waiting time, e.g., several milliseconds. This transmission waiting time in the ONU is a critical problem since the latency requirement for the MFH link is very strict, e.g., less than 250 μs defined by the Third Generation Partnership Project. In this paper, we propose a novel statistical DBA scheme taking high-speed traffic fluctuation to reduce the transmission waiting time. Our proposed scheme allocates bandwidth based on the only ratio of the data amount of each distributed unit and detects the pattern of the transmission interval of the burst signal. We show the feasibility with a numerical simulation and experiments.

A time sensitive converged network that can aggregate fronthaul and machine-to-machine (M2M) streams is required for the fifth generation mobile communication system (5G) era. Recently, a time sensitive network (TSN) for fronthaul in IEEE 802.1CM has been attracting attention. A time aware shaper (TAS), which is a component technology of a TSN, is an effective way to realize a converged network. However, lower priority streams suffer from a lack of usable bandwidth and a huge latency when employing a TAS because the broad bandwidth is reserved for high priority streams in TAS schemes. In this paper, we propose a gate shrunk (GS-)TAS, where a GS-frame is added at the end of high priority streams. We evaluate bandwidth use efficiency by using a numerical simulation. We also simulate the cumulative distribution function (CDF) for the latency performance in a bridged node. We report that the latency reduction rate for the M2M stream is 55.2% for a CDF of 99.7%. In addition, the GS-TAS use does not affect the fronthaul streams.

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) have attracted attention as the next step in relation to optical access systems. A major application of TWDM-PON is expected to be fronthaul streams in the centralized radio access network (C-RAN) architecture of mobile networks. In the 5G era, the upstream traffic that OLT receives becomes highly bursty, because of the variable data rate generated by the functional split between baseband unit and remote radio heads, and the global synchronization of data transmission with time-division duplex (TDD). To flexibly allocate upstream bandwidth via the dynamic wavelength and bandwidth allocation (DWBA) scheme has been a hot research topic for TWDM-PON. However, there is no existing DWBA scheme for TWDM-PON that is designed to satisfy the strict delay requirement for fronthaul with minimum number of active wavelength channels. Therefore, in this paper we propose a novel efficient DWBA algorithm that minimizes active wavelength channels considering the high burstiness of fronthaul data transmission. With the proposed algorithm, the allowable delay and constraint for allocation timing for each ONU is formulated based on the different propagation delay between the OLT and ONUs. Then, the wavelength channel and bandwidth is allocated with the simple allocation algorithm based on the descending sort by propagation delay. It was confirmed through computer simulations that the proposed algorithm can reduce the number of active wavelength by 50% by considering propagation delay.

A fronthaul bridged network has attracted attention as a way of efficiently constructing the centralized radio access network (C-RAN) architecture. If we change the functional split of C-RAN and employ time-division duplex (TDD), the data rate in fronthaul will become variable and the global synchronization of fronthaul streams will occur. This feature results in an increase in the queuing delay in fronthaul bridges among fronthaul flows. This paper proposes a novel low-latency routing scheme designed to satisfy the latency requirements in fronthaul networks with path-control protocols. The proposed scheme formulates the maximum queuing delay by defining competitive links and flows. It selects the set of paths that satisfy the latency requirements with the Markov chain Monte Carlo method using machine learning (MCMC-ML). The initial paths are selected from candidate paths using the learned solutions, and path-reselection is performed with the MCMC method. We confirmed with computer simulations that the proposed scheme can compute routes for all flows that satisfy the delay requirements. We also confirmed that the route computation is accelerated with the learned solutions, even if the flow distribution changes.

We propose a novel technique for the accommodation of mobile base stations (BSs) and a secondary system with a time division duplex (TDD) frame monitor. By increasing the number of BSs accommodated in a passive optical network (PON), the cost of mobile fronthaul (MFH) can be reduced. To realize even further cost reduction, a secondary system is accommodated in the PON with the MFH while the mobile signal is transferred with low latency. We propose a technique for overlapping the signal of the secondary system on the signals of the TDD mobile BSs by using a function for distinguishing the unallocated interval of TDD uplink signals. We evaluate the improvement in the optical uplink bandwidth efficiency with a numerical calculation. By employing the proposed technique, in a numerical simulation we greatly increase the optical bandwidth of the secondary system compared with that without a TDD frame monitor. For the uplink of the mobile system, the MFH latency is kept at a low value regardless of TDD frame monitoring.

In 5th generation mobile communications systems (5G), the number of small cells with centralized-radio access network (C-RAN) architecture is expected to increase greatly. Time division multiplexing-passive optical networks (TDM-PON) are promising transmission systems for networks between a baseband unit (BBU) and a remote radio head (RRH). However, there is a challenge as regards the ultra-low latency requirements of 5G including less than 1 ms for end-to-end communication, because a TDM-PON generally transmits using time division multiple access (TDMA) as a basis. In this paper, we derive the achievable latency and the number of RRHs accommodable by TDM-PON, and analyze the applicable range of a TDM-PON for mobile use. Our analysis shows that it is possible to design a PON application in accordance with the latency requirements of mobile systems.