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The emergence of 5G-IoT opens up unprecedented connectivity possibilities for new service use cases and players. Multi-access edge computing (MEC) is a crucial technology and enabler for Beyond 5G, supporting next-generation communications with service guarantees (e.g., ultra-low latency, high security) from an end-to-end (E2E) perspective. On the other hand, one notable advance is the platform that supports virtualization from RAN to applications. Deploying Radio Access Networks (RAN) and MEC, including third-party applications on virtualization platforms, and renting other equipment from legacy telecom operators will make it easier for new telecom operators, called Private/Local Telecom Operators, to join the ecosystem. Our preliminary studies have discussed the ecosystem for private and local telecom operators regarding business potential and revenue and provided numerical results. What remains is how Private/Local Telecom Operators can manage and deploy their MEC applications. In this paper, we designed the architecture for fully virtualized MEC 5G cellular networks with local use cases (e.g., stadiums, campuses). We propose an MEC/Cloud Orchestrator implementation for intelligent deployment selection. In addition, we provide implementation schemes in several cases held by either existing cloud owners or private and local operators. In order to verify the proposal’s feasibility, we designed the system level in E2E and constructed a Beyond 5G testbed at the Ōokayama Campus of the Tokyo Institute of Technology. Through proof-of-concept in the outdoor field, the proposed system’s feasibility is verified by E2E performance evaluation. The verification results prove that the proposed approach can reduce latency and provide a more stable throughput than conventional cloud services.

The fifth-generation mobile networks (5G) will not only enhance mobile broadband services, but also enable connectivity for a massive number of Internet-of-Things devices, such as wireless sensors, meters or actuators. Thus, 5G is expected to achieve a 1000-fold or more increase in capacity over 4G. The use of the millimeter-wave (mmWave) spectrum is a key enabler to allowing 5G to achieve such enhancement in capacity. To fully utilize the mmWave spectrum, 5G is expected to adopt a heterogeneous network (HetNet) architecture, wherein mmWave small cells are overlaid onto a conventional macro-cellular network. In the mmWave-integrated HetNet, splitting of the control plane (CP) and user plane (UP) will allow continuous connectivity and increase the capacity of the mmWave small cells. mmWave communication can be used not only for access linking, but also for wireless backhaul linking, which will facilitate the installation of mmWave small cells. In this study, a proof-of-concept (PoC) was conducted to demonstrate the practicality of a prototype mmWave-integrated HetNet, using mmWave technologies for both backhaul and access.

Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.

In this paper, we consider a cellular network in which the locations of the base stations are spatially correlated. We introduce an analytical framework for computing the distribution of the conditional coverage probability given the point process, which is referred to as the meta distribution and provides one with fine-grained information on the performance of cellular networks beyond spatial averages. To this end, we approximate, from the typical user standpoint, the spatially correlated (non-Poisson) cellular network with an inhomogeneous Poisson point process. In addition, we employ a new and recently proposed definition of the coverage probability and introduce an efficient numerical method for computing the meta distribution. The accuracy of the proposed approach is validated with the aid of numerical simulations.

We investigate the performance of a scheduling algorithm where the mobile terminals (MTs) may be turned off if they cause a level of interference greater than a given threshold. This approach, which is referred to as interference aware muting (IAM), may be regarded as an interference-aware scheme that is aimed to reduce the level of interference. We analyze its performance with the aid of stochastic geometry and compare it against other interference-unaware and interference-aware schemes, where the level of interference is kept under control in the power control scheme itself rather than in the scheduling process. IAM is studied in terms of average transmit power, mean and variance of the interference, coverage probability, spectral efficiency (SE), and binary rate (BR), which accounts for the amount of resources allocated to the typical MT. Simplified expressions of SE and BR for adaptive modulation and coding schemes are proposed, which better characterize practical communication systems. Our system-level analysis unveils that IAM increases the BR and reduces the mean and variance of the interference. It is proved that an operating regime exists, where the performance of IAM is independent of the cell association criterion, which simplifies the joint design of uplink and downlink transmissions.

In this paper, a novel Poisson hole process (PHP) modeling of wireless networks is proposed. Contrary to the prior PHP models with circular-shaped holes, we utilized circular sector holes in a random direction to capture the spatial separation between tiers in a millimeter wave (mmWave) heterogeneous cellular network (HCN). In this case, small cell base stations and macrocell base stations are distributed as a PHP and Poisson point process (PPP). Using tools from stochastic geometry, we derive approximate analytical expressions by regarding the effect of one or several holes on coverage probability. Simulation results reveal that compared to conventional PPP-modeling of HCNs, the proposed approaches can provide about 2–3 dB more accurate analysis in terms of signal-to-interference-and-noise ratio coverage probability. Moreover, some interesting insights about the effect of holes on coverage probability and the relation between proposed hole configuration and prior models with circular holes is discovered. It turns out that the analysis based on the proposed PHP model can provide a more general study than prior works.

The ultra‐dense deployment of unmanned aerial vehicles (UAVs) as mobile small cell base stations (SBS) is expected to support ultra‐high‐speed, ultra‐reliable, and ultra‐low‐latency wireless connections in sixth generation (6G) wireless cellular networks. Inevitably, the explosively increasing number of SBSs each with ever‐shrinking cell size will result in the spectrum scarcity and pose critical challenges to the spectrum management. Towards the centralized spectrum management system, a third‐party authority is employed to coordinate the spectrum sensing, sharing, and allocation among participants. However, a centralized authority is vulnerable to numerous security threats, such as single point of failure, denial of service attacks, and privacy disclosure, and cannot guarantee fair and efficient spectrum management without mutual trusts among participants. To address these problems, a novel framework of blockchain‐aided spectrum management is designed to securely record the spectrum auction data and spectrum allocation data in the decentralized spectrum sharing. Moreover, a trust management scheme is proposed to evaluate the trusts of all UAVs participating in the decentralized spectrum sharing, where the trust increase depends on legal use of spectrum allocation and honest report of spectrum sensing. In particular, it is shown that the proposed trust management can not only incentivize the UAVs to comply with the legal rule of spectrum sharing in blockchain, but also punish the malicious behaviour that either violates the spectrum sharing rule or provides misleading spectrum sensing results.

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

During a ship evacuation, many people panic as they do not know the direction that leads to the emergency muster station. Moreover, sometimes passengers get crowded in corridors or stairs, so they cannot save their lives. This paper proposes an IoT-enabled architecture for digital signage systems that directs passengers to the muster stations of a cruise ship by following the less dangerous route. Thus, crews’ and passengers’ safety risks during a ship evacuation can be low, and human health hazards may be limited. The system is based on a low-cost and open-source architecture that can also be used in a variety of fields in industrial IoT applications. The proposed modular digital signage architecture utilizes Light Emitting Diode (LED) strips that are remotely managed through a private Long-Term Evolution (LTE)/Fourth Generation (4G) cellular network. Publish–subscribe communication protocols were used for the control of the digital strips and particularly through a Message Queuing Telemetry Transport (MQTT) broker who publishes/subscribes every message to specific topics of the realized IoT platform, while the overall digital signage system centralization was implemented with an appropriate dashboard supported from an open-source RESTful API.

while the fifth generation (5G) cellular system is being deployed worldwide, researchers have started the investigation of the sixth generation (6G) mobile communication networks. Although the essential requirements and key usage scenarios of 6G are yet to be defined, it is believed that 6G should be able to provide intelligent and ubiquitous wireless connectivity with Terabits per second (Tbps) data rate and sub-millisecond (sub-ms) latency over three-dimensional (3D) network coverage. To achieve such goals, acquiring accurate location information of the mobile terminals is becoming extremely useful, not only for location-based services but also for improving wireless communication performance in various ways such as channel estimation, beam alignment, medium access control, routing, and network optimization. On the other hand, the advancement of communication technologies also brings new opportunities to greatly improve the localization performance, as exemplified by the anticipated centimeter-level localization accuracy in 6G by extremely large-scale multiple-input multiple-output (MIMO) and millimeter wave (mmWave) technologies. In this regard, a unified study on integrated localization and communication (ILAC) is necessary to unlock the full potential of wireless networks for dual purposes. While there are extensive studies on wireless localization or communications separately, the research on ILAC is still in its infancy. Therefore, this article aims to give a tutorial overview on ILAC towards 6G wireless networks. After a holistic survey on wireless localization basics, we present the state-of-the-art results on how wireless localization and communication inter-play with each other in various network layers, together with the main architectures and techniques for localization and communication co-design in current two-dimensional (2D) and future 3D networks with aerial-ground integration. Finally, we outline some promising future research directions for ILAC.