For many countries in the world, 5G is of strategic significance. In the 5G era, telecom operators are expected to enable and provide multiple services with different communication characteristics like enhanced broadband, ultra-reliable and extreme real-time communications at the same time. To meet the requirements, the 5G network essentially will be more complex compared with traditional 3G/4G networks. The unique characteristics of 5G resulted from new technologies bring a lot of opportunities as well as significant challenges. In this paper we first introduce 5G vision and check the global status. And then we illustrate the 5G technical essentials and point out the new opportunities that 5G will bring to us. We also highlight the coming challenges and share our 5G experience and solutions toward 5G vision in many aspects, including network, management and business.

While the introduction of softwarelization technologies such as software-defined networking and network function virtualization transfers the main focus of network management from hardware to software, network operators still have to deal with various and numerous network and computing equipment located in network centers. Toward fully automated network management, we believe that a robotic approach will be essential, meaning that physical robots will handle network-facility management works on behalf of humans. This paper focuses on robotic assistance for on-site network maintenance works. Currently, for many network operators, some network maintenance works (e.g., hardware check, hardware installation/replacement, high-impact update of software, etc.) are outsourced to computing and network vendors. Attendance (witness work) at the on-site vendor's works is one of the major tasks of network operators. Network operators confirm the work progress for human error prevention and safety improvement. In order to reduce the burden of this, we propose three essential works of robots, namely delegated attendance at on-site meetings, progress check by periodical patrol, and remote monitoring, which support the various forms of attendance. The paper presents our implementation of enabling these forms of support, and reports the results of experiments conducted in a commercial network center.

In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.

Enterprises have paid attention to consortium blockchains like Hyperledger Fabric, which is one of the most promising platforms, for efficient decentralized transactions without depending on any particular organization. A consortium blockchain-based system will be typically built across multiple organizations. In such blockchain-based systems, system operations across multiple organizations in a decentralized manner are essential to maintain the value of introducing consortium blockchains. Decentralized system operations have recently been becoming realistic with the evolution of consortium blockchains. For instance, the release of Hyperledger Fabric v2.x, in which individual operational tasks for a blockchain network, such as command execution of configuration change of channels (Fabric's sub-networks) and upgrade of chaincodes (Fabric's smart contracts), can be partially executed in a decentralized manner. However, the operations workflows also include the preceding procedure of pre-sharing, coordinating, and pre-agreeing the operational information (e.g., configuration parameters) among organizations, after which operation executions can be conducted, and this preceding procedure relies on costly manual tasks. To realize efficient decentralized operations workflows for consortium blockchain-based systems in general, we propose a decentralized inter-organizational operations method that we call Operations Smart Contract (OpsSC), which defines an operations workflow as a smart contract. Furthermore, we design and implement OpsSC for blockchain network operations with Hyperledger Fabric v2.x. This paper presents OpsSC for operating channels and chaincodes, which are essential for managing the blockchain networks, through clarifying detailed workflows of those operations. A cost evaluation based on an estimation model shows that the total operational cost for executing a typical operational scenario to add an organization to a blockchain network having ten organizations could be reduced by 54 percent compared with a conventional script-based method. The implementation of OpsSC has been open-sourced and registered as one of Hyperledger Labs projects, which hosts experimental projects approved by Hyperledger.

Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.

With the popularity of smart devices, mobile crowdsensing, in which the crowdsensing platform gathers useful data from users of smart devices, e.g., smartphones, has become a prevalent paradigm. Various incentive mechanisms have been extensively adopted for the crowdsensing platform to incentivize users of smart devices to offer sensing data. Existing works have concentrated on rewarding smart-device users for their short term effort to provide data without considering the long-term factors of smart-device users and the quality of data. Our previous work has considered the quality of data of smart-device users by incorporating the long-term reputation of smart-device users. However, our previous work only considered a quality maximization problem with budget constraints on one location. In this paper, multiple locations are considered. Stackelberg game is utilized to solve a two-stage optimization problem. In the first stage, the crowdsensing platform allocates the budget to different locations and sets price as incentives for users to maximize the total data quality. In the second stage, the users make efforts to provide data to maximize its utility. Extensive numerical simulations are conducted to evaluate proposed algorithm.

Network Function Virtualization (NFV) enables the embedding of Virtualized Network Function (VNF) into commodity servers. A sequence of VNFs can be chained in a particular order to form a service chain (SC). This paper considers placing multiple SCs in a geo-distributed edge system owned by multiple service providers (SPs). For a pair of SC and SP, minimizing the placement cost while meeting a latency constraint is formulated as an integer programming problem. As SC clients and SPs are self-interested, we study the matching between SCs and SPs that respects individual's interests yet maximizes social welfare. The proposed matching approach excludes any blocking individual and block pair which may jeopardize the stability of the result. Simulation results show that the proposed approach performs well in terms of social welfare but is suboptimal concerning the number of placed SCs.

Service chaining is attracting attention as a promising technology for providing a variety of network services by applying virtual network functions (VNFs) that can be instantiated on commercial off-the-shelf servers. The data transmission for each service chain has to satisfy the quality of service (QoS) requirements in terms of the loss probability and transmission delay, and hence the amount of resources for each VNF is expected to be sufficient for satisfying the QoS. However, the increase in the amount of VNF resources results in a high cost for improving the QoS. To reduce the cost of utilizing a VNF, sharing VNF instances through multiple service chains is an effective approach. However, the number of packets arriving at the VNF instance is increased, resulting in a degradation of the QoS. It is therefore important to select VNF instances shared by multiple service chains and to determine the amount of resources for the selected VNFs. In this paper, we propose a cost-effective service chain construction with a VNF sharing model. In the proposed method, each VNF is modeled as an M/M/1/K queueing model to evaluate the relationship between the amount of resources and the loss probability. The proposed method determines the VNF sharing, the VNF placement, the amount of resources for each VNF, and the transmission route of each service chain. For the optimization problem, these are applied according to our proposed heuristic algorithm. We evaluate the performance of the proposed method through a simulation. From the numerical examples, we show the effectiveness of the proposed method under certain network topologies.

Connected cars generate a huge amount of Internet of Things (IoT) sensor information called Controller Area Network (CAN) data. Recently, there is growing interest in collecting CAN data from connected cars in a cloud system to enable life-critical use cases such as safe driving support. Although each CAN data packet is very small, a connected car generates thousands of CAN data packets per second. Therefore, real-time CAN data collection from connected cars in a cloud system is one of the most challenging problems in the current IoT. In this paper, we propose an Edge computing-enhanced network Redundancy Elimination service (EdgeRE) for CAN data collection. In developing EdgeRE, we designed a CAN data compression architecture that combines in-vehicle computers, edge datacenters and a public cloud system. EdgeRE includes the idea of hierarchical data compression and dynamic data buffering at edge datacenters for real-time CAN data collection. Across a wide range of field tests with connected cars and an edge computing testbed, we show that the EdgeRE reduces bandwidth usage by 88% and the number of packets by 99%.

Wireless sensor networks provide long-term monitoring of the environment, but sensors are powered by small batteries. Using a mobile charger (MC) to replenish energy of sensors is one promising solution to prolong their usage time. Many approaches have been developed to find the MC's moving path, and they assume that sensors have a fixed sensing rate (SR) and prefer to fully charge sensors. In practice, sensors can adaptively adjust their SRs to meet application demands or save energy. Besides, due to the fully charging policy, some sensors with low energy may take long to wait for the MC's service. Thus, the paper formulates a path and charge (P&C) problem, which asks how to dispatch the MC to visit sensors with adaptive SRs and decide their charging time, such that both survivability and throughput of sensors can be maximized. Then, we propose an efficient P&C scheduling (EPCS) algorithm, which builds the shortest path to visit each sensor. To make the MC fast move to charge the sensors near death, some sensors with enough energy are excluded from the path. Moreover, EPCS adopts a floating charging mechanism based on the ratio of workable sensors and their energy depletion. Simulation results verify that EPCS can significantly improve the survivability and throughput of sensors.

New services can use fog nodes to distribute Internet of Things (IoT) data. To distribute IoT data, we apply the publish/subscribe messaging model to a fog computing system. A service provider assigns a unique identifier, called a Tag ID, to a player who owes data. A Tag ID matches multiple IDs and resolves the naming rule for data acquisition. However, when users configure their fog node and distribute IoT data to multiple players, the distributed data may contain private information. We propose a table-based access control list (ACL) to manage data transmission permissions to address this issue. It is possible to avoid unnecessary transmission of private data by using a table-based ACL. Furthermore, because there are fewer data transmissions, table-based ACL reduces traffic. Consequently, the overall system's average processing delay time can be reduced. The proposed method's performance was confirmed by simulation results. Table-based ACL, particularly, could reduce processing delay time by approximately 25% under certain conditions. We also concentrated on system security. The proposed method was used, and a qualitative evaluation was performed to demonstrate that security is guaranteed.

This paper proposes a Software-Defined Network (SDN)-based Moving Target Defense (MTD) to protect the network from potential scans in a compromised network. As a unique feature, contrary to traditional MTDs, the proposed MTD can work alongside other tools and countermeasures already deployed in the network (e.g., Intrusion Protection and Detection Systems) without affecting its behavior. Through extensive evaluation, we showed the effectiveness of the proposed mechanism compared to existing solutions in preventing scans of different rates without affecting the network and controller performance.

This paper proposes an efficient scheduling algorithm for the layered decoding of block low-density parity-check (LDPC) codes. To efficiently configure check node-based scheduling groups, the proposed algorithm utilizes the base matrix of the block LDPC code for a block-by-block scheduling group configuration; i.e., the proposed algorithm generates a scheduling group of check nodes, satisfying the weight condition of the layered decoding, which is performed in block units (including several check nodes). Therefore, unlike the conventional scheduling algorithms performed in node units, the proposed algorithm can efficiently generate scheduling groups for layered decoding at low computational complexity and memory requirements. In addition, to accelerate the decoding convergence speed, check nodes are allocated in each scheduling group such that messages from check nodes up to the current group are delivered as evenly as possible to bit nodes. Simulation results confirm that the proposed algorithm can accelerate decoding convergence compared to other block-based scheduling algorithms for layered decoding of block LDPC codes.

This paper considers an emergency communication system controlling multiple unmanned aerial vehicles (UAVs) in the sky over a large-scale disaster-affected area. This system is based on delay-tolerant networking, and information from ground users is relayed by the UAVs through wireless transmission and the movement of UAVs in a store-and-forward manner. Each UAV moves autonomously according to a predetermined flight method, which uses the positions of other UAVs through communication. In this paper, we propose a new method for UAV flight considering the non-uniformity of user distributions. The method is based on the Voronoi cell using the predicted locations of other UAVs. We evaluate the performance of the proposed method through computer simulations with a non-uniform user distribution generated by a general cluster point process. The simulation results demonstrate the effectiveness of the proposed method.

With the rapid deployment of the Internet of Things, where various devices are connected to communication networks, remote driving applications for Unmanned Vehicles (UVs) are attracting attention. In addition to automobiles, autonomous driving technology is expected to be applied to various types of equipment, such as small vehicles equipped with surveillance cameras to monitor building internally and externally, autonomous vehicles that deliver office supplies, and wheelchairs. When a UV is remotely controlled, the control accuracy deteriorates due to transmission delay and jitter. The accuracy must be kept high to realize UV control system by a cloud server. In this study, we investigate the effectiveness of Digital Twin Computing (DTC) for path tracking control of a UV. We show the results of simulations that use transmission delay values measured on the Internet with some cloud servers. Through the results, we quantitatively clarify that application of DTC improves control accuracy on path tracking control. We also clarify that application of jitter buffer, which absorbs the transmission delay fluctuation, can further improve the accuracy.

This paper introduces the working principle of continuous wave (CW) illuminator and selects the test space by developing the wave impedance selection algorithm for the CW illuminator. For the vertical polarization and the horizontal polarization of CW illuminator, the law of wave impedance distribution after loading is analyzed and the influence of loading distribution on test space selection is studied. The selection principle of wave impedance based on incident field or total field at the monitoring point is analyzed.

5G has achieved large-scale commercialization across the world and the global 6G research and development is accelerating. To support more new use cases, 6G mobile communication systems should satisfy extreme performance requirements far beyond 5G. The physical layer key technologies are the basis of the evolution of mobile communication systems of each generation, among which three key technologies, i.e., duplex, waveform and multiple access, are the iconic characteristics of mobile communication systems of each generation. In this paper, we systematically review the development history and trend of the three key technologies and define the Non-Orthogonal Physical Layer (NOPHY) concept for 6G, including Non-Orthogonal Duplex (NOD), Non-Orthogonal Multiple Access (NOMA) and Non-Orthogonal Waveform (NOW). Firstly, we analyze the necessity and feasibility of NOPHY from the perspective of capacity gain and implementation complexity. Then we discuss the recent progress of NOD, NOMA and NOW, and highlight several candidate technologies and their potential performance gain. Finally, combined with the new trend of 6G, we put forward a unified physical layer design based on NOPHY that well balances performance against flexibility, and point out the possible direction for the research and development of 6G physical layer key technologies.

In this paper, we propose an online probabilistic activation/deactivation control method for base stations (BSs) in heterogeneous networks based on the temporal system throughput and activation states of neighbor BSs (cells). The conventional method iteratively updates the activation/deactivation states in a probabilistic manner at each BS based on the change in the observed system throughput and activation/deactivation states of that BS between past multiple consecutive discrete times. Since BS activation control increases the system throughput by improving the tradeoff between the reduction in inter-cell interference and the traffic off-loading effect, the activation of a BS whose neighbor BSs are deactivated is likely to result in improved system performance and vice versa. The proposed method newly introduces a metric, which represents the effective ratio of the activated neighbor BSs considering their transmission power and distance to the BS of interest, to the update control of the activation probability. This improves both the convergence rate of the iterative algorithm and throughput performance after convergence. Computer simulation results, in which the mobility of the user terminals is taken into account, show the effectiveness of the proposed method.

Global Navigation Satellite System (GNSS) receivers often realize anti-jamming capabilities by combining array antennas with space-time adaptive processing (STAP). Unfortunately, in suppressing the interference, basic STAP degrades the GNSS signal. For one thing, additional carrier phase errors and code phase errors to the GNSS signal are introduced; for another, the shape of the cross-correlation function (CCF) will be distorted by STAP, introducing tracking errors when the receiver is in tracking mode. Both of them will eventually cause additional Pseudo-Range (PR) bias, and these problems prevent STAP from being directly applied to high-precision satellite navigation receivers. The paper proposes a novel anti-jamming method based on STAP that solves the above problems. First, the proposed method constructs a symmetric STAP by constraining the STAP coefficients. Subsequently, with the information of the steering vector, a compensation FIR filter is cascaded after the symmetric STAP. This approach ensures that the proposed method introduces only a fixed offset to the code phase and carrier phase, and the order of the STAP completely determines the offset, which can be compensated during PR measurements. Meanwhile, the proposed method maintains the symmetry of the CCF, and the receiver can accurately track the carrier phase and code phase in tracking mode. The effectiveness of the proposed method is validated through simulations, which suggest that, in the worst case, our method does not increase carrier and code phase errors and tracking error at the expense of only a 2.86dB drop in interference suppression performance.