Asynchronous Pulse Code Multiple Access (APCMA) has been proposed as a pulse-based LPWA technology for massive IoT, offering robustness against collisions. However, in ultra-dense environments, pulse overlaps cause misdetections. This paper proposes an enhanced CSS-APCMA decoding method that leverages per-message chirp frequency-offset consistency and accounts for clock jitter. The receiver was implemented using a USRP and GNU Radio, and large-scale experiments were conducted with 1,000 transmitters operating in the 920 MHz band. The results demonstrate that the proposed method maintains a high reception rate while significantly suppressing misdetections and achieving higher data rates compared to conventional CSS-APCMA in ultra-dense environments.

Millimeter-Wave (mmWave) communication enables high data rates but suffers from limited coverage and blockage. Network-Controlled Repeaters (NCRs), standardized in 3GPP Release 18, offer a cost-effective solution using analog beam-forming. This paper presents a software-controlled beamforming method for NCRs based on Taylor amplitude distribution to obtain extremely low sidelobe level. A 32-element phased array with phase-control ICs was implemented, and the radiation patterns were measured in an anechoic chamber at 28.0 GHz. The results confirm effective beam manipulation and sidelobe reduction, demonstrating the feasibility of software-defined NCR control.

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.

The widespread deployment of 5G networks has drawn growing attention to the optimization of wireless communication systems. Among various techniques, quantum-based approaches are distinguished by their remarkably fast computation time compared to conventional optimization methods. In this paper, we propose a quantum-inspired approach to the wireless resource allocation problem. The problem is formulated as an Ising model and solved using both Coherent Ising Machine (CIM) and D-Wave’s quantum annealer. Furthermore, the performance of these quantum-based methods is compared with that of the Simulated Annealing (SA) algorithm.

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.

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.

The realization of sustainable logistics and regional transportation services requires the deployment of automated driving technologies, which in turn demands further latency reductions in mobile networks. However, one of the major challenges in existing mobile networks, including 5G, is addressing communication quality degradation, such as latency spikes, that occur during inter-cell handovers (HOs). We have been investigating a multi-band redundant vehicle-to-network-to-vehicle (V2N2V) communication scheme as a potential solution to mitigate the impact of such degradation during inter-cell HOs in mobile-network-based inter-vehicle communications. This paper presents a field trial of the proposed multi-band redundant V2N2V scheme under single mobile network operator (MNO), assuming its application to bidirectional real-time control message exchange in automated Bus Rapid Transit (BRT) platooning. Experimental results conducted over a commercial 5G network demonstrate that the proposed scheme can suppress latency spikes during inter-cell HOs. These findings indicate that multi-band redundant V2N2V is a promising approach for achieving stable low latency in V2N2V communication.

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.

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.

As networks increasingly become the backbone of modern society, the demands placed on them by various applications have become more complex. In particular, the demand for high-capacity, low-latency services such as real-time streaming is increasing every year. Although 5G has been deployed to meet these needs, its effectiveness can vary significantly by location and time, and sometimes falls short of requirements. Traditionally, much of the research to improve communication stability has focused on TCP-based systems, which do not translate well to real-time UDP streaming applications. To address the above challenges, we propose a multipath redundant communication framework designed to improve the quality of real-time media streaming. This framework has been tested using multipath redundant communication over two mobile networks with a moving vehicle in an urban environment. Using a real-time streaming application based on WebRTC, our framework demonstrates a significant reduction in packet loss and an increase in bitrate, outperforming existing multipath redundant communication systems without interfering with the application’s congestion control mechanisms. • Proposal of a Framework: The paper proposes a multipath redundant communication framework to improve the streaming quality via multipath redundant communications in 5G networks, particularly focusing on UDP media streaming applications. • Implementation and Verification: The framework has been implemented and verified using multipath communication over two mobile networks, with a vehicle moving in a real experiment, leveraging a real-time streaming application based on WebRTC. • Significant Improvements Demonstrated in a real experiment: Results from the implementation show significant reductions in packet loss and increases in bitrate, which notably outperforms existing multipath redundant communication systems without disrupting the applications’ congestion control mechanisms. • Challenges Addressed: The paper discusses the specific challenges related to the traditional approaches of multipath redundancy, especially in maintaining communication quality across varied network fields and discusses the advantages of the proposed method. • Future Research Directions: The paper concludes by discussing potential future research directions, including the necessity to further evaluate the framework in varied environments to verify its general applicability and latency performance.

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.

The commercialization of 5G has been initiated for a while. Yet, we haven't seen wide penetrations of the disruptive applications as promised, like the extended reality (XR), internet of vehicles (IoV), telesurgery, etc. Digital twins (DTs) are one of these highly anticipated applications. However, DTs demand all 5G capabilities, including enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine type communication (mMTC) simultaneously, which makes their implementation more challenging. Owing to the advanced sensing, communication, and computing networks for beyond 5G (B5G) and 6G at Tokyo Institute of Technology, we are able to build a real-time DT on campus and demonstrate two smart mobility applications for safety and traffic efficiency, respectively, by utilizing DT-derived knowledge. Our demonstrations will convince society of the values and potentials of DTs and encourage current 5G progress towards B5G and 6G.

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.

This paper focuses on coordinating Intelligent Reflective Surfaces (IRS) and UAV networks to extend coverage while maintaining Line-of-Sight (LoS) conditions. We verify our methodology through numerical analysis in a realistically modeled 3D urban environment. Our results highlight remarkable enhancements in received SINR (Signal-to-Noise ratio) for multiple UEs with the integration of IRS. Therefore, our proposed method effectively enhances coverage in mmWave UAV networks.

The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. To address this, we introduce RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that rely on predefined racing lines, RaceMOP operates without a map, utilizing only local observations to execute high-speed overtaking maneuvers. Our approach combines an artificial potential field method as a base policy with residual policy learning to enable long-horizon planning. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Extensive experiments on twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during overtaking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners and generalizes to unknown racetracks, affirming its potential for broader applications in robotics. Our code is available at http://github.com/raphajaner/racemop.

The burgeoning Intelligent Transportation System (ITS) spurs global technological advancements, notably in innovative community development through vehicle-to-everything (V2X) communication. This study focuses on the high data rates and low latency offered by a millimeter-wave (mmWave) enabled vehicular network while addressing the significant challenge of link quality degradation due to blockages, exacerbated by the mmWave band's small wavelength in high mobility and traffic conditions. We propose an RSU-assisted ITS system tailored for multi-lane, straight-road scenarios, effectively identifying blockage status for vehicles. Combining Simulation of Urban Mobility (SUMO) and MATLAB, this blockage-aware scheme lays the groundwork for future ITS enhancements. The research also delves into the effects of various frequency bands, vehicle types, and communication ranges, offering a holistic system performance analysis.

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.

The rapid increase in the number of connected vehicles on roads has made vehicular ad-hoc networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This article proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a secure and efficient blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a homomorphic encryption system (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experimental results show that the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.

Discussions have begun worldwide toward Beyond 5G/6G in the 2030s. In these challenges, the topic of intelligence is a controversial point on various layers of RAN (radio-access-network), core, edge, automation, etc. One of the ideas on that topic is a digital twin platform that integrates physical and cyberspace. This study focuses on the RAN side as the data input to the digital twin. Mainly, this paper proposes a method to obtain accurate coverage data and store it in cyberspace in a dynamically changing coverage environment. Besides, we implement the proposed method and evaluate its measurement results and effectiveness in an outdoor proof-of-concept field.

The exhaustion of IPv4 addresses has driven the rapid adoption of IPv6 networks, which has created challenges in the domain name resolution process, particularly for IPv6-only iterative resolvers. This paper presents an experimental analysis to quantify the extent of this problem, revealing a significantly lower success rate of name resolution using IPv6-only resolvers (64.1%) compared to IPv4-only resolvers (98.8%). By analysing the success rates and percentages of A and AAAA records for the top 1,000,000 domains in the Tranco list, we identify the limitations of IPv6-only iterative resolvers and highlight the urgent need for comprehensive solutions to improve DNS resolution in IPv6-only networks. Our findings emphasise the importance of full IPv6 adoption for improved compatibility in IPv6-only environments, and serve as a basis for addressing the challenges faced by IPv6-only networks.

Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to-vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.

Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.

This paper introduces an "IPv6-only-Capable resolver" to address the issue of many zones remaining unresolvable due to a lack of IPv6 connectivity in authoritative name servers. The proposed method utilizes NAT64 to transmit packets to IPv4-only authoritative name servers and increases resolution success rates with competitive response times compared to a traditional IPv6-only resolver.

For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end (E2E) experimentation and evaluation of connected autonomous vehicles (CAV). The Collective Perception Service was also implemented, allowing the transmission of Collective Perception Messages (CPMs). A dual-channel mechanism that enables wireless link redundancy on the critical object information shared by CPMs is also proposed. Performance evaluation in field experiments has indicated that the CPM-based perception information can be transmitted in around 30 ms, and shared object data can be used by the AD software to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs transmits perception information through two different wireless technologies. The receiver-side CAV can then dynamically select the best CPM from CPMs received from both links, depending on the freshness of their information.

Millimeter-wave (mmWave) and terahertz (THz) technologies are under consideration for Beyond 5G (B5G) and 6G. However, mmWave/THz bands suffer from high propagation loss and blockage effects, making it challenging to deploy communication areas. To address these issues, we propose a digital twin platform for future wireless communication that expands communication coverage areas for mmWave bands in 5G and beyond. In this study, we construct cyberspace based on simulation results and measurement data for the mmWave environment and use augmented reality (AR) to visualize them. We also present the effectiveness of the proposed system by verifying it in an outdoor experimental field as a proof-of-concept using an actual mmWave base station.

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.

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.

In a high-density hotspot that occurs temporarily, the data rate per user is significantly reduced. In addition, if the base station stops working in the disaster area, it is necessary to provide wireless infrastructure promptly. To address in these situations, we focus on a UAV (Unmanned Aerial Vehicle) with wireless communication infrastructure to provide services. Moreover, for access links in UAV, it will be installed with mmWave interface to achieve high-speed throughput and line- of-sight environment could be guaranteed. However, under limited frequency bandwidth, when multiple UAVs are deployed, frequency interference will occur between UAVs, and the data rate will be deteriorated. To reduce the interference between neighboring UAVs, in this paper, we propose a combined method with placement and frequency division under the multiple UAVs environment. Hence, it turns out that the data rate is improved by proposal method under interference environment.

In recent years, the demand for further expansion of the capacity of wireless communication is increasing due to the drastic growth in the number of connected devices and the emergence of various services and applications utilizing wireless networks. It is necessary to decrease the traffic on the backhaul networks and reduce the end- to-end (E2E) latency. In order to address the problems, Multi-Access Edge Computing (MEC) where services, computing resource, storage, etc., would be deployed at the edge of the network is proposed. However, it is difficult for the operators to determine if and how MEC should be deployed in cellular networks because it is still unclear whether a return on investment of MEC could be obtained. In order to deploy MEC on the cellular network, to the best of authors knowledge, no research has been conducted on whether a business model can be established when additional funds are paid for MEC. In this paper, we propose a more sophisticated business model with an ecosystem for MEC deployment. This paper investigates E2E latency and analyzes the optimal MEC resource deployment in the cellular network, and the numerical results verify our analysis. The analysis results reveal that the profit for telecom operator’s revenue tended to increase with the addition of MECs, and the optimal number of MEC maximizing the profit is a function of MEC resource cost and application requirements.