This paper validates the concept of spectrum superposing through blind adaptive array (BAA) inter-system interference suppression aided by blind interference estimation. Our proposed framework adaptively switches the BAA algorithm according to the output of the estimator, which classifies the polarity of signal-to-interference power ratio (SIR) trained by convolutional neural network using 2-dimensional time-frequency representation, i.e., spectrogram, of the received signal. Suppose LTE and wireless LAN coexistence scenario; simulation results show that the proposed scheme successfully selects the appropriate BAA algorithm to effectively suppress inter-system interference.

This demonstration illustrates the concept of spectrum superposing, enabled by interference suppression with a blind adaptive array (BAA). In the microwave band, the widespread deployment of various wireless communication systems has led to a shortage of available spectrum resources. In this study, two orthogonal frequency division multiplexing (OFDM)-based systems, namely LTE and wireless LAN (WLAN), are operated simultaneously on the same spectrum. The proposed BAA algorithm suppresses inter-system interference without requiring any prior knowledge of the interfering signals.

In millimeter-wave (mmWave) Vehicle-to-Everything (V2X) communications, dynamic mobility often leads to severe degradation in communication quality due to transitions between line-of-sight (LoS) and non-line-of-sight (NLoS) conditions or interference from surrounding vehicles. To address this challenge, we propose a handover (HO) control scheme across multiple base stations (BSs), and evaluate two distinct approaches: cooperative transmission and null-space expansion. In the cooperative transmission scheme, multiple BSs synchronously transmit identical data, transforming interference into a desired signal to enhance reception during HO. In contrast, null-space expansion suppresses interference by steering nulls toward undesired directions, which is particularly effective during vehicle approach. Through simulations with three BSs, two vehicles, and two blocking vehicles under three scenarios (interference only, blockage only, and combined), we show that cooperative transmission consistently achieves the highest SINR across cell boundaries and during blockage events, whereas null-space expansion provides partial gains under specific conditions. These results highlight the complementary strengths of both approaches and provide practical insights into the design of robust mmWave V2X HO.

Beamforming using massive MIMO has been considered to increase high-speed wireless communications, but the beam search time increases as the number of candidate beams increases. To reduce the beam search time, we have proposed a beam search scheme with fast beam steering. The fast beam steering is performed within a single synchronization signal block (SSB). It can reduce the number of beam search cycles by utilizing multiple beam searches within a single SSB, rather than one beam search per SSB, as in the conventional scheme. However, the performance of cell ID detection is degraded due to the high-speed beam steering. This paper clarifies the effect of the variation pattern of the fast switching phase rotation on the cell ID detection performance.

This paper proposes a novel low-complexity precoding weight for downlink of multi-user massive multiple-input multiple-output (MU-mMIMO) system in time-varying channel environments. Inter-user interference (IUI) suppression performance in the MU-MIMO system deteriorates due to the mismatch between the channel state used for precoding weight derivation and that during signal transmission. Leveraging the massive antenna degrees of freedom (DOFs) in mMIMO, null-space expansion (NSE) was previously conceived to improve IUI suppression performance by steering multiple nulls toward undesired user equipments (UEs), even in high-mobility environment. However, NSE has severe challenges, such as high computation complexity caused by the increasing nullification and near singular matrix structure of the Gram matrix due to high time correlation. Considering these challenges, this paper proposes Jacobi matrix-based iterative low-complexity NSE weight design. Focusing on the Gram matrix’s block diagonal structure in NSE, this paper presents a Jakes’ model-based approximation and Ridge parameter optimization using deep unfolding (DU) to enhance the convergence of the inverse matrix. The effectiveness of the proposed scheme is demonstrated through computer simulations.

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.

The importance of digital twins in 6G is rapidly increasing. However, while digital twins have been discussed in various contexts, defining their specific role within 6G remains crucial. This paper proposes a wireless digital twin (WDT) platform designed to ensure communication stability for connected autonomous vehicles (CAVs) integrated into networks. The proposed WDT reproduces 3D urban environments, calculates radio wave propagation using traffic simulators and ray tracing, and tracks CAV movement. Additionally, we discuss methods for evaluating multiple communication systems using proposed WDT, aiming to promote research and development toward 6G.

As discussions on autonomous driving continue to intensify, further development will require improvements in vehicle-to-everything (V2X) communication. This paper proposes broad-range null-steering (BRNS) incorporated with beam tracking for stabilizing multi-user spatial multiplexing transmission in high-speed mobile environments. BRNS is a method that utilizes signal radiation angles obtained from location information to steer additional nulls toward interfering vehicle, thereby improving inter-user interference (IUI) suppression capability. However, desired user moves out of the beam, which leads IUI deterioration even using BRNS. We combine a location-based beam tracking and BRNS to further enhance multiuser spatial multiplexing performance. Computer simulation verifies its effectiveness.

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.

In modern communication systems, meeting the growing demand for high-capacity transmission requires developing efficient and robust modulation techniques. To address this, we propose a low-PAPR page-style Orthogonal Time Frequency Space (OTFS) modulation framework that enhances communication capacity while maintaining a low peak-to-average power ratio (PAPR). The proposed design introduces a novel pilot signal placement and analysis method, improving channel estimation accuracy and system performance in high-mobility multi-user scenarios. This paper provides an overview of recent advancements in OTFS-based multi-user communication systems, emphasizing their contributions to enhancing spectral efficiency, reliability, and robustness. Through extensive simulations, we demonstrate the effectiveness of the proposed framework in achieving superior BER performance, improved interference mitigation, and robust transmission capabilities compared to traditional methods, validating its suitability for next-generation communication networks.

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 investigates a potentiality of spectrum superposing in different systems through blind adaptive array (BAA) interference suppression. Various kinds of wireless communication systems are widely deployed such as LTE and Wireless LAN (WLAN). Mobile traffic is increasing due to services like video streaming on smartphones, leading to a shortage of spectrum resources in the microwave band. With the growing demand for wireless communication, efficient use of limited spectrum resources is required. In this paper, we conduct a link-level simulation of interference suppression of two orthogonal frequency division multiplexing (OFDM) based different systems, i.e. LTE and WLAN, using BAAs which does not require prior knowledge of interfering signals. We demonstrate that the spectrum efficiency can be improved by up to 53%.

A peak cancellation technique with in-band distortion compensation has been developed to reduce the peak-to-average power ratio (PAPR) in massive multi-input multi-output orthogonal frequency division multiplexing (mMIMO-OFDM) systems using a uniform planar array. With this approach, in-band distortion compensation signals are transmitted using a subset of transmitting antennas to ensure that each user receives its dedicated data signals free of in-band distortion. This paper presents a proposal of an in-band distortion-aware modified minimum mean square error (MMSE)-based precoding (mMMSE) strategy that mitigates in-band distortion caused by peak cancellation and power amplifier nonlinearity in mMIMO-OFDM with a uniform-planar array. Simulation results demonstrate the effectiveness of the proposed precoding for improving the bit error rate and increasing the average system throughput compared to traditional approaches using mMIMO-OFDM configurations with peak cancellation and in-band distortion compensation while reducing PAPR of the transmit signal effectively.

The coordination of connected autonomous vehicles (CAVs) is expected to facilitate more efficient route planning and improve safety by enabling the exchange of sensor data between roadside infrastructure and other vehicles. Given the substantial volume of sensor data involved, the utilization of the millimeter-wave (mmWave) band is essential. While mmWave supports high-speed and high-capacity data transmission, wireless communication systems for CAVs are subject to radio signal degradation caused by vehicle mobility. To address these challenges, previous studies have proposed broad-range null-steering (BRNS) integrated with beam tracking to stabilize multi-user spatial multi-plexing transmission in high-speed mobile environments. BRNS utilizes signal radiation angles derived from location information to steer additional nulls toward interfering vehicles, thereby enhancing inter-user interference (IUI) suppression. Meanwhile, our previous approach, additional null-steering, relied on fixed mannar which limits its adaptability and efficiency. To resolve this issue, this paper newly proposes optimization of angular-based null space expansion exploiting vehice traveling onformation provided through O-RAN and cooperative awareness message. Its effectiveness is validated through sophisticated computer simulations combining realistics traffic simulator.

This paper proposes a low-complexity precoding weight suitable for multi-user massive multiple-input multiple-output (MU-mMIMO) in user terminals' (UTs') mobility environ-ment. Null-space expansion (NSE) achieves better inter-user in-terference (IUI) suppression performance than channel prediction schemes by steering several nulls for interfering UTs in the high mobility environment. However, NSE faces challenges in effectively managing the computation complexity of precoding. Focusing on the singularity of the Gram matrix in NSE, we incorporate Ridge regression into the Newmann series (NS) to achieve a low-complexity weight design optimized for NSE. Computer simulation demonstrates the superiority of the proposed scheme in terms of IUI suppression performance and computation complexity.

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.

To cope with the increasing mobile traffic, a centralized radio access network (C-RAN: Centralized Radio Access Network) has been investigated. Small cells are possible to distribute the traffic and improve system capacity. However, the effect is limited since the traffic fluctuates depending on location and time of day. To overcome fluctuations in traffic demand, we have proposed an adaptive network architecture that uses crowdsourced radio units (CRUs). CRUs installed in vehicles can function as small cells, adapting to communication demand and enabling efficient traffic accommodation. This paper evaluates the throughput characteristics of the proposed scheme when using actual user distribution fluctuations. Computer simulation clarifies that using CRUs can improve throughput compared to fixed small cells when using user distribution of Tokyo station in Japan.

This paper proposes a precoding weight that reduces computational cost and enhances interference suppression performance in time-variant multi-user massive MIMO (MU-mMIMO) environments. In MU-mMIMO, channel variations degrade the inter-user interference (IUI) suppression performance. Null Space Expansion (NSE) generates precoding weight that maintain nulls at the future channel state of interfering user terminals (UTs). On the other hand, mMIMO system requires high computational resources due to the large number of antennas. Iterative Interference Replica Subtraction (IIRS) eliminates the need for the matrix inversion calculation and provides a computationally efficient solution. The proposed precoding scheme integrates NSE and IIRS to address both challenges. We demonstrate significant improvements in computational efficiency and interference suppression under time-variant conditions through the computer simulation. The effectiveness of the proposed IIRS-NSE becomes particularly evident in scenarios with a small expansion factor and UTs' high mobility environment.

This paper proposes a novel and robust framework for velocity and position estimation in vehicle-to-everything (V2X) communication networks, targeting challenges that arise when traditional GPS systems and line-of-sight (LoS) channels fail due to signal blockages between vehicles, base stations (BSs), and satellites. The proposed solution integrates multiple complementary methods to enhance robustness and accuracy. Specifically, the framework first applies the MUSIC algorithm for angle-of-arrival (AoA) estimation to obtain reliable initial positioning. In parallel, a newly designed low peak-to-average power ratio (PAPR) frame structure is introduced under reconfigurable intelligent surface (RIS)-assisted Orthogonal Time Frequency Space (OTFS) modulation, enabling resilient velocity embedding against noise and interference. These methods jointly support an unscented Kalman Filter (UKF), which refines the velocity and position estimation with high reliability. Extensive numerical evaluations confirm the effectiveness of the proposed multi-method integrated framework. Various modulation techniques, including zero padding (ZP), cyclic prefix (CP), and their recursive variants (e.g., RZP, RCP), demonstrate improved bit error rate (BER) performance over traditional OFDM. The proposed low-PAPR signal design achieves a 21.5% PAPR reduction compared to conventional embedded pilot schemes, significantly improving BER in 4-QAM scenarios. Velocity estimation results also show that the UKF outperforms the extended Kalman Filter (EKF) in both straight and curved road conditions. By combining MUSIC-based AoA estimation with RIS assistance under NLoS conditions, the proposed framework remains effective even in high-Doppler and low-SNR environments.

The commercialization of 5G has been initiated for a while. Furthermore, millimeter wave (mmWave) has been introduced to small cells with small coverage due to its strong linearity and non-winding characteristics. On the other hand, in connected autonomous vehicles (CAV s), where various traffic systems can cooperatively perform recognition, decision-making, and execution, communication is assumed to be always connected. Therefore, to use low latency mm Wave for high-speed moving CAV, existing beamforming cannot follow them at high speed. This paper proposes an improved beam tracking algorithm for high-speed CAVs, which can be evaluated in a more general environment using a traffic simulator. We proposed an adaptive algorithm for a general road environment by increasing the number of beam searches and search dimensions.

This paper proposes an intensive null-steering around the target in angular domain to effectively suppress inter-user interference (IUI) leakage caused by channel varying environment such as vehicular multiuser spatial multiplexing. Multiuser MIMO can enhance spectral efficiency by multiplexing a number of user terminals in spatial domain. Suppose applying multiuser MIMO downlink in vehicle-to-everything (V2X) scenario, vehicles move at high speed which causes IUI. Null-space expansion has been conceived that can improve IUI suppression capability by steering nulls to the past and the present channel states on interfered users. Collective perception in intelligent transport systems (ITS) provides location information of vehicles every 100 ms. Exploiting this feature, this paper proposes angular-domain null-space expansion; broad-range null-steering (BRNS). Computer simulation verifies its effectiveness.

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.

Coordination of connected autonomated vehicles (CAVs) is expected to provide a more efficient sequential route design and enhance safety. This is accomplished by sharing sensor data among roadside equipment and other vehicles. Given the substantial volume of sensor data involved, it is advantageous to employ millimeter-wave (mmWave) band. MmWave offers high-speed and large-capacity for transmission. However, wireless communication systems designed for CAVs face the challenge of radio signal degradation caused by the movement of vehicles. This paper proposes a fast beam tracking Open Radio Access Network (O-RAN) architecture for CAVs. The most prominent aspect of this system is its Near-Realtime (Near-RT) RAN Intelli-gent Controller (RIC), which swiftly adjusts and tracks the beam using vehicle information transmitted every 100 msec by CAV. By conducting simulations using Simulation of Urban Mobility (SUMO), which emulates vehicle movement on various roads, we verified the effective operation of the proposed architecture.

In wireless power transfer (WPT) systems, multiple-input single-output (MISO) signal processing enhances the power transfer efficiency by optimizing array antenna weight vectors according to channel coefficients between the transmitter and receivers. In a MISO-based WPT system, a time division multiplexing (TDM) scheduling should be jointly optimized; transmitter periodically switches weight vectors for receivers by assigning a time slot to each weight vector. This paper proposes a receiver grouping strategy for TDM scheduling to improve the fairness and efficiency of the WPT system. Receivers with correlated channel conditions are classified into a group because the transferred energy with a beamforming weight directed to a certain receiver is supplied to other receivers, which is called beneficial interference. Beamforming weight vectors are derived to maximize the harvested energy of each group. The time slots duration assigned to weight vectors is then optimized to maximize the minimum harvested energy among receivers. The simulation experiments in an indoor multipath environment reveal that the proposed method improves the minimum and average harvested energy.

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.

In massive MIMO, precoding has an interference cancellation issue because the channel size has a considerable dimension based on the number of antennas and receivers. This paper proposes complexity-efficient precoding for single-cell multiuser massive MIMO systems that iteratively subtract interference replica signals based on a maximal ration transmission (MRT) weight. Moreover, it additionally proposes zeroing minute replica components to avoid excessive interference cancellation. Its remaining power can be diverted to the desired signal, improving the signal-to-interference-plus-noise power ratio (SINR) ratio. In addition, processing related to zero-ed components can be omitted, contributing to reduced computation complexity. Simulation results show its effectiveness in a specified region.

This paper presents improved semi-blind uplink interference suppression scheme which was previously proposed. When the number of spatial multiplexing is increased in a Massive MIMO system to increase capacity, the number of orthogonal sequences required for channel estimation has an upper limit, so they are used repeatedly in each cell, resulting in inter-cell interference (ICI) caused by pilot signals so called pilot contamination. It prevents sufficient suppression of not only ICI but also inter-user interference (IUI). Our previously proposed scheme is based on intra-cell channel state information (CSI) which contains pilot contamination. Blind adaptive array (BAA) signal processing such as constant modulus algorithm (CMA) can well suppress residual interference with capturing desired signals. This paper presents interference suppression performance improvement by employing beamforming at the user terminal side. User beamforming works more effectively for semi-blind interference suppression because it can suppress ICI in advance, although it cannot suppress IUI. Computer simulations 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.

Numerous radio units (RUs) are required to densely compose small cells in the beyond 5G mobile networks. The concept of vehicle-mounted RUs is a promising solution for dynamically deploying small cells in accordance with demand distribution. Wireless relay fronthaul networking is the key enabler for this concept, where forwarding paths of fronthaul streams are computed in real-time by an edge server using the link-state information reported from RUs. Optimization of the reporting interval is a significant issue because of the tradeoff between the freshness of report messages and network load. However, existing works have not investigated the freshness of link information in highly dynamic environments. In this paper, we introduce a new reliability metric called the reliability of information (RoI). The RoI is defined as the joint probability that all nodes in the network maintain correct information. We establish a tractable lower bound of the RoI under a mild assumption on the dynamics of node connectivity, which enables us to design small cells guaranteeing information reliability. We further formulate and solve an optimization problem to find an optimal reporting interval. The usefulness of the proposed scheme is demonstrated through simulation experiments for a highly dynamic vehicular network.

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.

This paper proposes an in-band distortion cancellation (IDC) scheme for peak cancellation (PC) based peak-to-average power ratio (PAPR) reduction in massive multi-input multi-output orthogonal frequency division multiplexing (mMIMO-OFDM) with a uniform planar array (UPA). In the proposed scheme, properly selected transmit antennas, i.e., in-band distortion compensation antenna, are designed to compensate in-band distortion because of PC, where the beam-formed IDC signals are transmitted through the compensation antennas so that each user can receive its own data signal with no in-band distortion. Based on the idea that higher in-band distortion compensation accuracy is achieved if the IDC signal itself is less affected by power amplifier nonlinearity, the proposed scheme performs the in-band distortion cancellation by selecting appropriate antenna distribution for compensation to minimize nonlinear distortion of the IDC signal. Simulation results show that the proposed scheme achieves better BER performance than conventional schemes and that instantaneous power at the complementary cumulative distribution function (CCDF)=10-4is reduced to 3dB.

The 5th generation (5G) mobile communication system has started to provide services in many countries. The millimeter-wave (mmW) and beamforming are emerging technologies therein to support ultra-high communication speed. To combat the limited coverage and blocking problem of mmW, deploying relay stations (RS, a.k.a repeaters) is a promising solution for a reliable and efficient service area. However, the design of service areas, or more specifically, the deployment of RS requires a detailed understanding of the propagation environment. This paper proposes a novel digital twin architecture that can reproduce the real-time propagation environment in virtual space to support the dynamic placement of the RSs. As an initial study, we carried out high-precision propagation measurements in the Beyond 5G R&D Proof-of-Concept field in Ookayama Campus of Tokyo Institute of Technology and investigated RS deployment challenges.

For the purpose of establishing a digital twin society, this paper proposes an object recognition network architecture using high-definition (HD) camera images. HD camera is installed to the edge computing resource in Road-Side Unit (RSU). The edge computing resource detects vehicles by object detection and Re-ID function based on deep learning. Its key feature is to reduce the amount of network data traffic by extracting vehicle images and directly transferring them to neighboring RSUs, i.e. vehicle identification and tracking can be achieved in the localized backhaul network. This paper experimentally verifies its fundamental feasibility using outdoor testbed field. The proposed framework can significantly reduce the data traffic by more than 90% while maintaining vehicle Re-ID accuracy.

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.

In order to meet the ever-growing demand for data traffic, highly efficient multiple access schemes, such as OFDMA, are widely used in modern communication standards. In such multiple access schemes, adaptive modulation and coding (AMC) are used to optimize the transmission rate of each user. However, feedback information, such as SNR and Doppler shift, characterizing the communication environment of each user is indispensable of key importance for AMC. In the past, these information and parameters were often estimated using reference signals. However, the reference signal becomes overhead, resulting in throughput degradation and processing delay. Furthermore, the computation burden can be large as it is necessary to perform channel parameter estimation individually for each user. Previously, over the single-user channel, we have proposed a joint SNR and Doppler shift detection method via a spectrogram-based data-driven method, without the reference signal. This paper extends this framework to multiuser OFDM multiple access channels. In the newly proposed method, SNR and Doppler shift for all users can be detected simultaneously via deep learning-guided object detection algorithms from each spectrogram image. Simulation results are provided to validate the effectiveness of the proposed method.

3G and 4G mobile services have become widespread in our lifecycle, and expectations for a better quality of throughput/latency experience are being placed to 5G era. In recent two years, 5G pilot services have been launched to deal with the drastic increase in mobile devices and application types. However, since 5G adopts a higher frequency than conventional 3G/4G, base stations must be densified. Therefore, virtualization resources for softwarization in various technologies (e.g., Virtualized RAN (Radio Access Network), Virtualized Core, Virtualized Applications) are more appreciated. In this research, we focus on edge computing, which is one of the cores of Beyond 5G, to utilize the virtualization resources. Critical potentials of edge computing are traffic reduction, low latency, and high security for mobile networks. However, various studies were made based only on numerical analysis or parts of the trial of the experiment due to a lack of technical preparation for end-to-end (E2E) environments (e.g., devices, RAN, Core, applications, human resources) with software/hardware flexibility. To show the state-of-the-art edge computing for Beyond 5G (B5G), we established a true E2E B5G Edge Cloud in Ookayama Campus of Tokyo Institute of Technology, Japan. In addition, focusing on the effective use of computation resources in Edge Clouds as an advantage of edge computing, we proposed a cloud-native with network function deployment architecture for computation resource sharing in different Edge Clouds. Specifically, we focused on Virtualized CU (Central Unit) in RAN and proposed to operate CU-CP (Control Plane) and CU-UP (User Plane) in different Edge Cloud. Its feasibility is validated through a measurement.

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 multiple-input multiple-output (MIMO) systems, space division multiplexing and spatial modulation (SD-SM) transmissions have been proposed. And then, in the SD-SM-MIMO, the bit error rate (BER) performance is improved by using the channel ranking QR decomposition with M-algorithm maximum likelihood detection (CR-QRM-MLD) in spite of the small number of searches. Moreover, the non-null and null modulation are utilized for the SM transmissions independently. However, these combinations are not evaluated such as the amplitude shift keying (ASK). Also, phase rotation (PR) which is the effective in the MLD is not also evaluated. In this paper, we propose the PR and ASK combination for the SD-SM-MIMO.

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.

Recently, vehicle to everything (V2X) communications is paid attention in order to realize a smart transportation such as safety driving and automated cruise. It necessitates an ultra-reliable and low-latency communications (URLLC) even at fast fading environment involved by moving vehicles. Generally, forward error correction (FEC) code such as polar code requires long block code length to raise error correction capability while it deteriorates the channel tracking accuracy in pilot-aided estimation. In our previous work, to meet these trade-off demands, we proposed a frame splitting method. It splits information data in half and estimate channel state information (CSI) of the former part. That of latter is simply extrapolated using the former CSI. In this paper, we expand the previous work into multiple-input and multiple-output (MIMO) eigenbeam-space division multiplexing (E-SDM) system, which can improve communication accuracy significantly. To keep the benefit of E-SDM for fast fading, we propose two data assignment methods based on the eigenbeams. The computer simulation results clarified that these two methods could improve the BER performance in the V2X communication.

With the increase in demand for wireless data traffic, high-speed communication systems are required in many environment. Adaptive modulation and coding is an important technique to realize this, but it requires feedback of communication environment information represented by SNR. Conventional SNR estimation methods have a problem of degrading estimation accuracy in fast-moving environments and the presence of carrier frequency offset (CFO). Convolutional neural network (CNN) is capable of estimating the SNR from the trained received signal dataset accurately. This paper investigates an input signal representation to further improve the SNR classification accuracy by CNN. We then propose to combine respective spectrogram data of IQ domains. It can extract the features related to SNR from the received signal to the maximum extent possible and thus achieve highly accurate SNR estimation. Simulation results show that the proposed approach outperforms the other existing candidates.

This paper proposes a framework of blind-spot visualization to see through obstacle via augmented reality (AR) glasses for safe driving enabled by millimeter-wave vehicle-to-everything (V2X) communications. Cooperative perception is known as promising technology to realize safe (automated-) driving where the driver can make immediate and accurate decisions by widely collecting information on the surrounding traffic environment. It is realized by extended sensors such as high-definition cameras and light detection and ranging (LiDAR) to be installed in road-side units (RSUs) all over the place. Camera and LiDAR sensor data of the blind-spot are transferred from RSU to the vehicle, and these sensor fusion data are created as the colored three-dimensional point cloud. It is then projected on the AR glasses. The key feature of our proposal is to provide blind-spot information as a realistic view so that drivers can recognize it without having to look at other display devices. Our initial implementation presents its feasibility through indoor and outdoor trials.

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.

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.

The concept of user-provided networks (UPNs) have been investigated to utilize the power of citizens in the deployment of mobile networks. In addition to conventional fixed host models, mobile models have been proposed to utilize movable hosts including mobile phone users. When mobile UPNs are considered, it becomes difficult for clients to establish a stable connection to hosts because they can be in moving environments, such as public buses and taxis. To address this problem, this article focuses on Age of Information (AoI) which a measure of the freshness of a continually updated piece of information. Thus, this article proposes an AoI-based host-selection algorithm for mobile UPNs for establishing stable connection. It determines the host device to establish a wireless link based on the history of values and AoI of received signal strength indicator (RSSI). The performance of the proposed scheme is confirmed through theoretical analysis and computer simulations.

This paper proposes a novel convolutional neural network (CNN) based joint classification method to characterize the signal-to-noise power ratio (SNR) and Doppler shift using spectrogram images, in order to enable efficient adaptive modulation and coding (AMC) designs. It is necessary to maintain high communication performances even in stringent environments where transceivers move at high speed due to the diversification of wireless applications. To optimize the transmission rate in such dynamic environments, AMC scheme is known to be effective. AMC is generally designed based on feedback information (FBI) such as the SNR and Doppler shift acquired on the receiving side. Here, the challenge is an increase in calculation burden, processing delay and estimation accuracy of the FBI. We focused on the spectrogram which is composed of power values in the time and frequency domains. Its two-dimensional fluctuation represents the Doppler shift as well as noise values. In the proposed method, such key information for AMC can be simultaneously extracted from a single spectrogram via a trained CNN. Therefore it is expected to contribute to reducing the computational burden and speeding up the signal processing. Simulation results are presented to demonstrate that the proposed method achieves better performance than traditional methods.

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.

Many radio units (RUs) are densely deployed to compose small cells with high frequency bands in the Centralized Radio Access Network (C-RAN) architecture which prevails in the 5G mobile networks. More and more cells will be required in the era of beyond 5G and 6G to ensure wide coverage with high data rate. The concept of on-board cells, i.e. vehicle-mounted RUs, is a promising solution for future mobile networks because it enables dynamic and efficient network deployment to deal with the fluctuations in mobile traffic demand. Cell zooming has also been proposed to adaptively adjust the cell size according to fluctuating traffic load. However, it has not been considered to employ cell zooming for vehicle-mounted cells. The existing cell zooming schemes cannot be employed for vehicle-mounted cells because of their high mobility. Therefore, this paper proposes a cell zooming scheme for vehicle-mounted RUs. The goal of the proposed technique is to optimize the size of each cell according to the current distribution of RUs for reducing transmission power. It contributes for energy efficiency of mobile networks. The performance of the proposed approach was confirmed through computer simulations.

This paper proposes a highly accurate signal to noise ratio (SNR) estimation method for adaptive modulation and coding (AMC) by learning power spectral density (PSD) images with a convolutional neural network (CNN). Accurate SNR estimation is indispensable for adaptive control schemes such as AMC. Proposed method trains the CNN using PSD images and the corresponding SNR value as a teacher signal, and outputs the SNR in response to an input of unknown PSD image. Once trained, the SNR can be estimated with high speed and high accuracy, so AMC performance can be improved. Furthermore, since PSD is hardly affected by the Doppler shift, proposed method is resistant to high-speed environment. Compared with the previously proposed method which estimates SNR from a PSD data using a neural network, the proposed method can improve the estimation accuracy, bit error rate (BER) and throughput performance when AMC is applied.

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.

This paper expands the analytic formulation of single-cell multi-user massive multiple-input-multiple-output (MIMO) systems with matched-filter (MF) precoding into multiantenna user environments. Massive MIMO technologies are now remarkably evolving such as pre/post coding including beamforming strategies. Not only a base station (BS) having hundreds of antenna elements, but also user terminals (UTs) are also capable of array signal processing. It is enabled by implementation of array antennas in practical scale due to widespread millimeter wave band. Evaluating performance on massive MIMO systems where both BS and UTs have array antennas could impose impractical computation complexity. As an introduction of this challenge, this paper newly derives mathematical formulation of outage probability performance on MF precoded massive MIMO systems with multi-antenna user circumstance. Its consistency is confirmed by comparing the derived analytical model to the simulation results.

Massive Multiple-Input-Multiple-Output (MIMO) systems, where the base station (BS) is equipped with hundred antennas, is expected to be one of the key technologies for the next generation wireless systems. Pilot contamination is one of performance degradation factor for massive MIMO. This paper presents an approach which aims to eliminate this pilot contamination impact. High Time Resolution Carrier Interferometry (HTRCI) was previously proposed which can perform channel estimation with the reduced number of pilot signals. It utilizes a comb-type pilot allocation and realizes highly accurate channel state information (CSI) estimation and interpolation by time-domain signal processing. Unused pilot subcarriers are available to obtain the more number of CSI. This paper expands the concept of HTRCI to estimate more channels and these CSIs can be separated by time-domain signal processing in the same manner. It is applied to the pilot de-contamination on downlink multi-cell MIMO transmission. Computer simulation results confirms that the BER performance can be improved as compared with the conventional method.

Orthogonal Freaquency Division Multiplexing (OFDM) is one of the transmission techniques that is widely used for high speed data transmission. The orthogonality allows signal spectrum to be separated without any interference or power loss due to leakage to other subcarriers. However, time varying channel with high Doppler shift could destroy the orthogonality, resulting in severe Inter Carrier Interference (ICI) and degrading BER performance. Accurate channel estimation is required to compensate for ICI effects. Decision Directed Channel Estimation (DDCE) is known as the effective time varying channel estimation method using demodulated data. In this paper, we propose a novel DDCE method by frame splitting and channel extrapolation. Under the fast fading environment, the estimated channel state information (CSI) is different from real channel state especially in the latter part of the data packet. In order to mitigate this impact, we halved the information data and performed channel estimation only in the former half part where the influence of fading is small. In the latter half part, we attempted to predict the channel state using quadratic extrapolation. Computer simulation results indicate that the proposed scheme can improve the BER performance even under the fast fading environment.

This paper proposes a novel weight derivation method to improve adaptive array interference suppression performance based on our previously conceived Sample Matrix Inversion algorithm using Common Correlation Matrix (CCM-SMI), by data-aided approach. In recent wireless communication system which possesses lots of subcarriers, the computation complexity is serious problem when using SMI algorithm. To mitigate this problem, CCM based SMI algorithm has been proposed. It computes the correlation matrix by the received time domain signals before fast Fourier transform (FFT). However, due to the limited number of pilot symbols, the estimated channel state information (CSI) is often incorrect. Therefore, it leads limited interference suppression performance. In this paper, we newly employ a data-aided channel state estimation. Decision results of received symbols are obtained by CCM-SMI and then fed-back to the channel estimator. It assists improving CSI estimation accuracy. Computer simulation result reveals that our method accomplishes better Bit Error Rate (BER) performance in spite of the minimum pilot symbols with only a little additional computation complexity.

This paper proposes a novel code length optimization for improving the throughput performance on Orthogonal Frequency Division Multiple - Interleave Division Multiple Access (OFDM-IDMA) based Visible Light Communication (VLC). VLC is under the global spotlight, since Light Emitting Diode (LED) has recently become a part of building infrastructure and VLC functionality is fairly easy by adding communication components to LED lighting. To enhance the VLC capability, OFDM-IDMA has been proposed as a connection method in a multiuser environment. By applying an interleave pattern peculiar to the transmission signal of each user, signals are well separated and multiple access becomes possible in VLC-OFDM-IDMA. However, VLC-OFDM-IDMA necessitates a low coding rate error correction code as well as a long length spreading code which limits the throughput performance. To resolve this problem, we newly introduce a channel ranking and allocates the short length spreading code to a user in good channel state. Overall throughput performance can be improved while the compatible BER performance.

This paper proposes an improved puncturing scheme exploiting the feature of systematic polar codes. Puncturing is the process of removing part of the encoded bits and can realize various coding rates to adopt to the channel condition. However, the puncturing deteriorates BER performance because the removed bits are generally unknown at the receiving side. Polar codes support systematic encoding which generate the codeword that has the same value of information bits. Using this characteristic, the transmitting side punctures known bits shared between transmitter and receiver in the codeword. The receiving side then decodes the received signal by setting punctured bits to the known value to assist the decoding capability. Computer simulation verifies the effectiveness of the proposed puncturing scheme.

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.

This paper expands our previously proposed semi-blind uplink interference suppression scheme on multicell multiuser massive MIMO systems to support multi modulus signals. The original proposal applies channel state information (CSI) aided blind adaptive array (BAA) interference suppression after the beamspace preprocessing and decision feedback channel estimation (DFCE). It employed the constant modulus algorithm (CMA) as the BAA scheme which can fully exploit the degree of freedom (DoF) of massive antenna arrays to suppress both inter-user interference (IUI) and inter- cell interference (ICI). Its effectiveness has been verified under the extensive pilot contamination constraint. Unfortunately, CMA basically works well only for the constant envelope signal such as QPSK and thus the proposed scheme should be expanded to cover QAM signals for more general use. This paper newly proposes to apply multi modulus algorithm (MMA) and data-aided sample matrix inversion based minimum mean square error (MMSE-SMI) weight derivation. It can successfully realize interference suppression under the use of multi-level envelope signals such as 16QAM with satisfactorily outage probability performance.

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.

Many small cells are densely deployed in 5G mobile networks. The efficiency of conventional centralized radio access network (C-RAN) architectures is drastically deteriorated by the spatio-temporal fluctuation in mobile traffic demand. To address this issue, this paper proposes a concept of adaptive C-RAN architecture for smart cities with crowdsourced radio units (CRUs) mounted on parked vehicles. The advantages of the proposed scheme are high flexibility and low cost, because the distribution of CRUs follows the distribution of mobile users. The performance of the proposed scheme is confirmed with numerical analysis and computer simulations.

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.

This paper proposes a novel concept of small cell called vehicle cell that utilizes the power of citizens for efficient deployment of mobile networks. Vehicle cell is an access point installed on a vehicle and provides network resources for mobile users. The vehicle cell is activated/deactivated by the driver on the basis of the cost and incentives from the mobile carrier. Mobile carriers control the network by optimizing the incentives for drivers in accordance with the mobile traffic dynamics.

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.

This paper proposes a novel semi-blind uplink interference suppression scheme for multicell multiuser massive multiple-input multiple-output (MIMO) systems. The inter-cell interference (ICI) becomes intenser when a number of users are spatially multiplexed via massive MIMO. Here arises a pilot contamination problem due to the limitation of orthogonal sequence set and thus inter-user interference (IUI) also remains unsuppressed. Our proposal employs a constant modulus algorithm (CMA) known as a blind interference suppression scheme and exploits channel state information (CSI)-aided minimum mean square error (MMSE) postcoding weight as an initial weight of CMA. Although MMSE weight cannot suppress ICI and IUI due to the pilot contamination, it can help subsequent CMA to capture the desired signals which is required for CMA to effectively work. Since CMA can fully exploit degree of freedom of massive arrays by using uncorrelated data signal part, derived weight successfully suppresses ICI/IUI while resolving the pilot contamination problem. A decision feedback channel estimation (DFCE) is then introduced to further improve CSI estimation accuracy and subsequent interference suppression performance. A series of signal processing, MMSE-CMA-DFCE, can be iteratively performed. The computer simulation reveals that our proposal can improve outage probability and it satisfies below the fifth percentile of cumulative distribution with saved symbol amount.

Metropolitan areas witness significant fluctuations in mobile traffic due to patterns of human mobility. This fluctuation drastically deteriorates the efficiency and financial viability of conventional maximum-based network design. If networks are deployed to deal with the peak traffic rate at each site, their capacities are underutilized for most of time. To improve the efficiency of deploying base stations (BSs), this paper proposes a concept of an Autonomous Base Station with Optical Reflex Backhaul (ABSORB) architecture that can adapt to fluctuations in mobile traffic. In the ABSORB architecture, traffic at demand nodes is forwarded to and from an ABS with an arbitrary radio access technology (RAT). An ABS is connected to a gateway node through ORB, which consists of fiber optic networks. ABSs move to new locations following the demand movement, according to a relocation schedule that is periodically rearranged by an ABSORB controller. The network is flexibly reconstructed according to the demand distribution. The ABSORB architecture can be employed in various networks, and can coexist with traditional static architectures. It will drastically reduce the number of BSs, total deployment cost, and power consumption in comparison with the traditional design.

This paper proposes a wired and wireless network cooperation (NeCo) system to quickly recover civilian telecommunication services in the aftermath of a catastrophic disaster. The proposed NeCo system achieves both rapid recovery and high throughput using wireless bypass routes backhauled by wired networks. With the NeCo system, active leaf nodes relay packets to and from dead leaf nodes whose wired communication channels have been disrupted. Thus, the dead leaf nodes can recover communication with root nodes outside the disaster area. In the this study, optimal bypass routes are computed to maximize the expected wireless throughput by solving a linear programming problem. Another issue is to overcome the limitation that the distribution of leaf nodes is determined by the demand distribution. We also introduce deploying additional recovery nodes to expand the application range of the NeCo system. Numerical simulations showed that the proposed NeCo achieved a higher throughput than an existing method, irrespective of the wired network's topology, and that our NeCo is suitable in cases where leaf nodes are widely distributed around a disaster area.

This paper proposes a simplified user scheduling method suitable for our proposed antenna arrangement and an antenna configuration with multi-planar antennas, for Massive MIMO. Massive MIMO has two key problems: the heavy overhead of feeding back the channel state information (CSI) for the very large number of transmission/reception antenna element pairs and the huge computation complexity imposed by the very large scale matrices. We previously proposed a parallelogram planar array (PPA) arrangement for Massive MIMO to realize channel spatial correlation reduction in LOS environments. Assuming that the proposed array is to cover omnidirectional areas, this paper proposes a user scheduling method that can easily enhance outage capacity performance in a simple manner and an antenna configuration that offers more efficient user accommodation. Simulations show that the proposed method can quadruple the system capacity over the uniform circular array antenna system.

This paper presents experimental results of our proposed null-space expansion scheme for multiuser massive MIMO in time varying channel. Multiuser MIMO transmission with proposed scheme can suppress inter-user interference (IUI) caused by outdated channel state information (CSI). The excess degrees of freedom (DoFs) of massive MIMO is exploited to perform additional null-steering using past estimated CSI. The signal-to-interference ratio (SIR) performance achieved by a nullspace expansion scheme that uses measured CSI is experimentally evaluated through CSI measurements. It is confirmed that the proposed scheme shows SIR performance superior to the conventional channel prediction scheme. In addition, IUI can be stably suppressed even in high mobility environments by further increasing the null-space dimension.

We previously proposed blind adaptive array (BAA) with subcarrier transmission power assignment (STPA) that enables spectrum superposing without any channel state information. This paper experimentally validates the basic feasibility of the proposed scheme using fabricated prototype; interference suppression performance of the proposed system with STPA-BAA and its interference reduction performance to the conventional system. Exploiting characteristics of constant modulus algorithm (CMA) and power inversion (PI), the transmitter of the proposed system provides two levels of power density for each subcarrier: high and low level. The receiver then uses CMA or PI to appropriately suppress interference on each subcarrier. Low-level subcarrier by the proposed system is also effective in reducing interference to the conventional system which has no interference suppression function. This paper conducts indoor experiments by wired setup of spectrum superposition in two multicarrier systems. Though the practical performance of STPA-BAA is limited due to the quantization level or dynamic range of the transceiver, effectiveness of the proposed scheme is confirmed. Improved operational SIR range of the proposed and the conventional system are presented.

This paper experimentally verifies the potential of higher order space division multiplexing for multiuser massive MIMO in line-of-sight (LOS) channels. We previously proposed an inter-user interference (IUI) cancellation scheme and simplified user scheduling method on Massive Antenna Systems for Wireless Entrance (MAS-WE). In order to verify the effectiveness of the proposed techniques, channel state information (CSI) for a 1×32 SIMO channel is measured in a real propagation environment with simplified test equipment. System level simulations using the measured CSI confirm that enlarging the angular gap between users reduces spatial correlation, and that the IUI cancellation proposal is effective under this condition. The degradation in SIR compared to ideal LOS channel model case is insignificant.

This paper proposes a novel Inter-User Interference (IUI) suppression scheme in time varying channels with null-space expansion for multiuser massive MIMO systems. The channel time variation deteriorates the accuracy of the channel state information at the transmitter (CSIT) and causes IUI on multiuser MIMO downlink transmission. In massive MIMO, the degrees of freedom (DoFs) of the base station antenna array are generally used to obtain the beamforming gain and capacity enhancement. The proposed null-space expansion scheme exploits the excess DoFs to perform additional null-steering, which extend the null-space dimension and thus suppress the IUI caused by the time varying channel. Computer simulations show that the proposed scheme has superior IUI suppression performance to existing channel prediction scheme; our scheme can improve the SIR by over 50dB.

This paper proposes to apply fractional frequency reuse (FFR) to our inter-cell interference (ICI) cancellation method in order to further improve spectral efficiency on large-scale base station cooperation. The proposed method realizes quasi-decentralized cooperation and can significantly reduce preprocessing complexity. System level simulations are conducted under practical conditions of estimation error on channel state information (CSI). The results clarify that the proposed method can achieve higher spectral efficiency than cooperative MU-MIMO, 11% on average value and 185% higher at the CDF=5%, even in imperfect CSI conditions.

This paper proposes an iterative inter-cluster interference (ICI) cancellation method for cooperative base station systems on the downlink/uplink. System level simulations were conducted with the parameter sets of cluster size and iteration order. The result showed that even in the imperfect CSI case, the spectral efficiency performance of the proposed method with the optimum parameter set is comparable to that of cooperative transmission/reception via MU-MIMO which can completely remove inter-cell interference. In addition, since the proposed method can be practiced in a simple manner, it also has a remarkable advantage to reduce computational complexity. It is a practical way to realize the nationwide wireless systems.

We propose a co-channel inter-cluster interference canceller for distributed antenna systems (DAS) that utilize the multiuser Multiple Input Multiple Output (MU-MIMO) technique in the downlink. We first define the cluster as a virtual cell that consists of a few remote antennas and subscriber stations which communicate cooperatively by MU-MIMO. Multiuser beamforming is applied to each cluster, so the proposed method can decrease the computational complexity of spatial division multiplexing processes compared to MIMO systems with large number of antennas. Though clustering causes co-channel inter-cluster interference, it can be cancelled simply by linear processing of the downlink transmission signals. Computer simulations show that the proposed method enables denser cluster configurations. The method contributes to the practical realization of distributed antenna systems through its effective frequency reuse.

We propose an effective resource allocation method to handle OFDMA and multiuser MIMO. This method offers two optimization techniques. In the frequency domain, those subcarriers that are useful for one subscriber station (SS) are allocated to that SS. In the spatial domain, several SSs are selected for spatial multiplexing. In frequency selective fading environments, the number of transmittable bits increases since the frequency and spatial resources can allocated efficiently. We define a permission table that indicates the allocation status. It is optimized using a genetic algorithm. The table is associated with GA chromosomes. They are renewed so as to improve two criteria, total throughput and minimum throughput among SSs. Calculations show that the proposed method improves the total throughput performance by up to around 50% compared to random resource allocation. The minimum throughput of each SS can also be improved with a small penalty in total throughput.