Search

Intelligent reflecting surfaces (IRSs) enhance the robustness of wireless transmissions against shielding. However, addressing channel-estimation overheads caused by the large number of IRS elements remains a major challenge. In particular, these overheads increase proportionately with an increase in the number of IRS elements, thereby making it difficult to handle multiple IRSs. Therefore, we investigate a passive beamforming method with partial channel state information (CSI) in distributed IRS communication systems called selective reflection control (SRC). Specifically, the proposed method determines the associations between the IRS and user equipment (UE) to obtain the CSI of the associated IRS-UE links. Simulations were conducted, and the obtained results confirm that the proposed method based on SRC outperforms the benchmark method in terms of the user sum rate. Furthermore, the proposed method realizes robust transmissions against shielding owing to appropriate IRS–UE associations. Therefore, the proposed SRC achieves better communication performance with less channel estimation overheads, which is expected to accelerate large-scale IRS deployment. [ABSTRACT FROM AUTHOR]

Channel estimation plays a crucial role in intelligent reflecting surface (IRS)-aided communication systems, in which the channel state information must be acquired to configure the optimal IRS reflection coefficient. However, using large numbers of IRS elements induce large channel estimation overheads, which cause crucial problems in systems utilizing IRSs. In this study, we investigate the optimal pilot interval design for IRS-aided communication systems to reduce the channel estimation overheads. The channel coherent time depends on user equipment (UE) velocity. Therefore, the proposed method aims to find a pilot interval that balances accuracy of the IRS reflection coefficient with system throughput by considering UE velocity. In the proposed method, we identify the relationship between the mobility of the UE and complex spatial correlation of the channel matrix, and the transmission capacity in a time slot is formulated to maximize the achievable sum rate. Numerical results from simulations show that the proposed method adaptively adjusts the pilot interval under various communication environments. [ABSTRACT FROM AUTHOR]

In recent years, intelligent reflecting surfaces (IRSs) for large-capacity and highly reliable wireless communication have attracted widespread attention. However, a multiuser access system with multiple IRSs poses limitations in reducing the large signaling overhead of channel estimation for numerous links between the IRSs and users. One approach to reduce the exhaustive channel estimation involves associating the IRS with a user and performing beam tracking for a certain period. However, as the IRS–user association is fixed during the tracking period, the dynamic variations in their link status caused by user mobility degrade the system performance if the association is decided without prior planning. Therefore, this paper proposes an IRS–user association strategy considering user mobility for IRS-aided multibeam transmission systems. Contrary to prior works, our association strategy aims to optimize the long-term performance of systems in terms of capacity and reliability. The proposed strategy minimized performance degradation even under drastic fluctuations of link conditions, thereby reducing channel estimation overhead because both the IRS and user can be associated for long periods with low performance degradation. [ABSTRACT FROM AUTHOR]

The demand for satellite communication increases with digitalization and globalization in the era of 5G and beyond; therefore, research and development of high-throughput satellites (HTSs) to increase the communication capacity and improve the flexibility of satellite communication systems is underway. Additionally, digital beamforming (DBF), which can control the directivity of multiple beams through high-speed digital signal processing, has received significant attention as a technology to improve the flexibility of resource allocation with HTSs. The principal control parameters in the DBF-based HTS, including the transmission power, beam gain, and placement location of each beam, cause alterations in the positional characteristics of the total throughput in the coverage area of an HTS. However, there are no power resource allocation models with dual control of these control parameters, based on the above positional characteristic. This study clarifies the effect of such control parameters on the total throughput in the coverage area of an HTS. We demonstrate a method for improving the flexibility of resource allocation to satisfy the geographic distribution of traffic requirements by constructing a power resource allocation model with a DBF-based fusion control in the HTS communication system. We evaluated the effectiveness of our proposed method through a simulation analysis using exponential annealing. [ABSTRACT FROM AUTHOR]

As one of the typical applications in the 6G mobile network, autonomous driving will significantly benefit from the extremely high network throughput and capacity, and an increasing number of computation-aggressive tasks will be generated by vehicles and offloaded to edge and cloud servers. Since many tasks generated by vehicles are delay sensitive, it is of great importance to ensure the transmission rate and stability. However, signals transmitted with high-frequency bands, even the terahertz, in 6G are seriously affected by physical obstacles, while the vehicle mobility can cause the dynamic channel conditions. [ABSTRACT FROM AUTHOR]

Satellite networks can provide Internet of Things (IoT) devices in remote areas with seamless coverage and downlink multicast transmissions. However, the large transmission latency, serious path loss, as well as the energy and resource constraints of IoT terminals challenge the stringent service requirements for throughput and latency in the 6G era. To address these problems, technologies including space-air-ground integrated networks (SAGINs), machine learning, edge computing, and energy harvesting are highly expected in 6G IoT. In this article, we consider the unmanned aerial vehicles (UAVs) and satellites to offer wireless-powered IoT devices edge computing and cloud computing services, respectively. To accelerate the communications, Terahertz frequency bands are utilized for communications between UAVs and IoT devices. Since the tasks generated by terrestrial IoT devices can be conducted locally, offloaded to the UAV-based edge servers or remote cloud servers through satellites, we focus on the computation offloading problem and consider deep learning techniques to optimize the task success rate considering the energy dynamics and channel conditions. A deep-learning-based offloading policy optimization strategy is given where the long short-term memory model is considered to address the dynamics of energy harvesting performance. Through the theoretical explanation and performance analysis, we discover the importance of emerging technologies including SAGIN, energy harvesting, and artificial intelligence techniques for 6G IoT. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRSs) have emerged as a key enabler for beyond fifth-generation (B5G) communication technology and for realizing sixth-generation (6G) cellular communication. In addition, B5G and 6G networks are expected to support aerial user communications in accordance with the expanded requirements of data transmission for an aerial user. However, there are challenges in providing wireless communication for aerial users owing to the different radio wave propagation properties between terrestrial areas and aerial areas. In this article, we propose an IRS-aided cellular network coverage extension for aerial users. In our proposed network, IRS and base stations (BSs) cooperate with each other to provide air-ground communication to aerial users (AUs), the aim of which is to prevent interference signals from spreading to a wide area. Furthermore, IRS placement is designed to maximize the network performance in terms of the spatial signal-to-interference-plus-noise ratio (SINR) while mitigating inter-cell interference. Numerical analysis results indicate that the proposed IRS-aided network outperforms the benchmark system without IRSs when the IRS installation positions are optimally determined. [ABSTRACT FROM AUTHOR]

As the 5G standard is being completed, academia and industry have begun to consider a more developed cellular communication technique, 6G, which is expected to achieve high data rates up to 1 Tb/s and broad frequency bands of 100 GHz to 3 THz. Besides the significant upgrade of the key communication metrics, Artificial Intelligence (AI) has been envisioned by many researchers as the most important feature of 6G, since the state-of-the-art machine learning technique has been adopted as the top solution in many extremely complex scenarios. Network intelligentization will be the new trend to address the challenges of exponentially increasing number of connected heterogeneous devices. However, compared with the application of machine learning in other fields, such as computer games, current research on intelligent networking still has a long way to go to realize the automatically- configured cellular communication systems. Various problems in terms of communication system, machine learning architectures, and computation efficiency should be addressed for the full use of this technique in 6G. In this paper, we analyze machine learning techniques and introduce 10 most critical challenges in advancing the intelligent 6G system. [ABSTRACT FROM AUTHOR]

Satellite communication systems have attracted considerable attention recently because they can communicate in various conditions, including terrestrial, airspace, and marine terrain. In addition, high-throughput satellites (HTSs) that allow high-speed and large-capacity communication are currently being launched. However, the allocation of communication resources to each beam is fixed in conventional HTSs, which exhibit low flexibility under state changes. This includes requests to the satellite communication systems or environmental changes around the systems. Therefore, the use of a digital channelizer in a satellite communication system can allocate frequency resources to each beam. However, even if a digital channelizer is used, conventional frequency resource allocation methods that consider inter-beam interference do not use the same frequency resource as the adjacent beam. As a result, the frequency resources cannot be used effectively. To alleviate this issue, we propose a frequency resource allocation method with inter-beam interference so that the satellite communication system can allocate frequency resources more flexibly. In addition, we extend the conventional flexibility analysis model such that it quantifies the flexibility more accurately. Finally, the effectiveness of the proposed method is demonstrated under state changes of the satellite communication system using the extended flexibility analysis model. [ABSTRACT FROM AUTHOR]

Recently, the explosive increase in the number of IoT devices makes the IoT becomes extremely large-scaled, and the security of such a large scale IoT emerges as a big challenge. As a classic security technique, the port scan is widely used around the world. However, as IP resources are limited, a large number of devices are located in the LAN or WLAN behind the NAT which cannot be directly accessed by the port scanner. Furthermore, port scanning generated a tremendous number of probe and response packets which may cause heavy traffic load and frequent congestion. To conquer those problems, in this article, we first propose a reverse proxy based NAT penetration system for scanning ports behind NAT. Based on the NAT penetration system, we proposed a probe delay based adaptive scanning algorithm referred to as ProDASA, which adaptively changes port scanning frequency and scanning methods to balance the network performance and security requirements of the IoT. The experiment in a real environment demonstrates the feasibility of the proposed NAT penetration system and the computational simulation with multiple virtual devices shows the advantage of our proposed ProDASA in terms of both network performance and security by comparing with a conventional method. [ABSTRACT FROM AUTHOR]

As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration. [ABSTRACT FROM AUTHOR]

Drones are being increasingly used to create new services and support the expansion of existing applications based on communication networks due to their flexibility and potential coverage. However, drone control and safety guarantees are difficult to attain, especially when drones fly beyond visual line of sight. To address this problem, we consider the real-time location awareness of drones flying beyond visual line of sight. In this article, we first introduce trends concerning location awareness of drones and our developed system exploiting the 400 MHz frequency band. Then, experimental results validate the performance of the developed system in terms of radio propagation and delay performance, and we further confirm the effectiveness of the system in practical settings. [ABSTRACT FROM AUTHOR]

IEEE 802.15.4 protocol defines multiple security specifications to provide different levels of safety protection in the link layer. How to choose the security level for harvesting enabled Internet of Things (IoT) nodes is an important problem as it decides the security protection and also impacts on network performance. Considering the dynamics of network threats and harvesting performance, in this letter, a harvesting and threat aware security configuration strategy is proposed. Simulation results illustrate that the proposal can choose the suitable security configuration according to the network security threats, service requirements, and the variable harvested energy. Comparison with conventional methods, the proposed strategy can extend the working time, resulting in improved network performance. Moreover, the proposed security configuration method can also meet the diversified service requirements. [ABSTRACT FROM AUTHOR]

In the context of the IoT, mMTC technology in 5G enables a range of new applications. The 3GPP NB-IoT introduced in Release 13 is a major enhancement toward mMTC to support devices requiring low data rate, low cost, and long battery life. Previous works suggested that NB-IoT physical channel characteristics have potential to fulfill design requirements. From the system point of view, the interaction among UEs and application data transmission procedures have to be further evaluated considering radio resource scheduling. In this article, we identify radio resource scheduling issues for NB-IoT systems and provide a comprehensive performance evaluation. We consider control plane optimization procedures with a massive number of UEs in mixed CE levels. Using a scheduling architecture verified with derived effective data rates, we show the effects of essential parameters on control channel allocation and data scheduling. Given our scheduling settings in anchor carriers, the number of transmitted data packets achieves about 65,000 per cell per hour with mixed CE levels. The results satisfy the design goal of NB-IoT data transmission in 3GPP TR 45.820. [ABSTRACT FROM AUTHOR]

Recently, WLANs using multiple frequency bands (e.g., 5 GHz, 2.4 GHz, and 920 MHz) have become popular as a means to increase spectral efficiency. However, during multi-hop transmissions in such multi-band WLANs, the relay node experiences a significant increase in delay and loss of data frames. This happens because of the difference of link rates between the receiving and sending sides of the relay node. In order to address this problem, we consider the channel conditions of each band on either side of the relay. Accordingly, the receiving and sending rates at the relay node are measured and calculated, respectively. Based on the measured receiving rate and calculated sending rate, we propose an algorithm for the relay node to determine the optimal coding rate and modulation method so as to dynamically control the best band and channel selection for the sending side. Simulation results demonstrate the effectiveness of our proposed algorithm in terms of improvement of delay and transmission failure probability. [ABSTRACT FROM AUTHOR]

Unmanned aircraft systems have recently been considered as effective solutions to establish communication network infrastructures in isolated regions, and perform environment-related tasks such as crop monitoring and topographic surveying. In a UAV network, the data from onboard video cameras, sensors, and communication devices are retrieved by a ground station. The performance of this type of network depends on efficient resource allocation to achieve a high throughput under the usually limited frequency bands. Thus, we introduce an architecture and a resource allocation system for UAV networks. In addition, we present the results from different field experiments and discuss future research directions based on these results. We expect that this study will foster the development of UAV networks for the service of society. [ABSTRACT FROM AUTHOR]

Unmanned aircraft systems (UASs) have attracted worldwide attention in recent years because of their potential applicability in a multitude of fields such as environmental monitoring and the communication provisioning environment. These applications enable data to be remotely collected from distant places through unmanned aerial vehicles (UAVs) equipped with communication devices. However, the communication range of a single UAV is limited because of the restrictions of the wireless transceiver as a result of the payload limitation. Therefore, UASs consisting of relay-based UAVs in which data are transferred via multiple UAVs have been studied to achieve long-distance communication with a high throughput. The time division long-term evolution advanced (TD-LTE-A) relaying technique may be applicable to UASs as it offers a reasonable solution. However, this technique is currently ineffective for UASs due to the limitation caused by its frame configuration. To alleviate this issue, in this paper, we propose a method to optimize radio resources based on the TD-LTE-A frame structure, which is capable of realizing efficient relay communication. Our proposed method considers a flexible frame configuration and updates cycle of control information to overcome the shortcoming of the conventional scheme. The effectiveness of the proposed radio resource optimization is demonstrated through simulation under varying environments. [ABSTRACT FROM AUTHOR]

Recently, unmanned aircraft systems (UASs) have attracted attention as a new avenue for commercial services. Using the flexible mobility of unmanned aircrafts (UAs), commercial services can be operated in wide areas. However, there is a problem in the wireless communication between the UA and its ground station. When several UASs are operated within the neighboring airspace, wireless-communication conflicts occur. One of the most effective solutions for this issue is to decide the communication schedule using a time-division multiple access (TDMA) scheme. Furthermore, by spatially reusing the time slot, numerous UAs can be operated within the neighboring airspace, in a limited frequency band. In this paper, we propose an efficient time-slot allocation for enhancing the frequency resource utilization. Our proposed scheme determines the time-slot allocation considering the time-slot spatial reuse, using a virtual cell based space partitioning method. In addition, we consider the influence of the UA mobility on the network to decide the parameters for the proposed resource allocation system. The effectiveness of our proposed resource allocation is evaluated through computer-based simulation. [ABSTRACT FROM PUBLISHER]

With the increasing popularity of augmented reality (AR) services, providing seamless human-computer interactions in the AR setting has received notable attention in the industry. Gesture control devices have recently emerged to be the next great gadgets for AR due to their unique ability to enable computer interaction with day-to-day gestures. While these AR devices are bringing revolutions to our interaction with the cyber world, it is also important to consider potential privacy leakages from these always-on wearable devices. Specifically, the coarse access control on current AR systems could lead to possible abuse of sensor data. Although the always-on gesture sensors are frequently quoted as a privacy concern, there has not been any study on information leakage of these devices. In this article, we present our study on side-channel information leakage of the most popular gesture control device, Myo. Using signals recorded from the electromyography (EMG) sensor and accelerometers on Myo, we can recover sensitive information such as passwords typed on a keyboard and PIN sequence entered through a touchscreen. EMG signal records subtle electric currents of muscle contractions. We design novel algorithms based on dynamic cumulative sum and wavelet transform to determine the exact time of finger movements. Furthermore, we adopt the Hudgins feature set in a support vector machine to classify recorded signal segments into individual fingers or numbers. We also apply coordinate transformation techniques to recover fine-grained spatial information with low-fidelity outputs from the sensor in keystroke recovery. We evaluated the information leakage using data collected from a group of volunteers. Our results show that there is severe privacy leakage from these commodity wearable sensors. Our system recovers complex passwords constructed with lowercase letters, uppercase letters, numbers, and symbols with a mean success rate of 91%. [ABSTRACT FROM AUTHOR]

The concept of Internet of things (IoT) has attracted attention as a key technology for realizing future industrial society. In the future society, numerous “things” with sensors are deployed and connected to networks, and data collected from these devices are used for a wide variety of industrial applications. In this paper, we focus on data collection for location-based authentication system as an application of industrial IoT. The authentication system uses ambient information, which is collected from the devices as unique information at a certain place and a certain time. However, since the ambient information changes continuously, it is required to collect it in real time from multipoint. Thus, we propose an efficient data collection method considering the requirements from the authentication system. The key point is to regulate the network performance for data collection by considering the application requirements. Since the location-based authentication system can be used in many situations and has large expansivity, the proposed work is considered to significantly contribute to the future industrial IoT society. In addition, we demonstrate how to optimize the operation of our proposal by using mathematical analysis. Moreover, the efficiency of our proposed method is validated through numerical results. [ABSTRACT FROM PUBLISHER]

The rapid proliferation of personal wearable as well as embedded devices point to the emergence of networks of unprecedented size and complexity in the near future. Unfortunately, traditional network security solutions fall short of addressing the unique security requirements of the emerging environment given their general emphasis on administratively managed, preconfigured security context and strong physical security mechanisms. To cope with the security challenges of this emerging environment, novel cognitive-inspired security architectures have been proposed that emphasize dynamic, autonomous trust management. Cognitive security systems take advantage of sensing and computing capabilities of smart devices to analyze raw sensor data and apply machine learning techniques to make security decisions. In this article, we present a canonical representation of cognitive security architectures and examine the practicality of using these architectures to address the security challenges of rapidly growing networks of mobile/embedded autonomous devices including the ability to identify threats simply based on symptoms, without necessarily understanding attack methods. Using authentication as the main focus, we introduce our canonical representation and define various categories of contextual information commonly used by cognitive security architectures to handle authentication requirements, and highlight key advantages and disadvantages of each category. We then examine three grand challenges facing the cognitive security research including the tension between automation and security, the unintended consequences of using machine learning techniques as a basis for making security decisions, and the revocation problem in the context of cognitive security. We conclude by offering some insight into solution approaches to these challenges. [ABSTRACT FROM PUBLISHER]

Fiber-Wireless (FiWi) network is a classification of network that combines the massive bandwidth of the optical network and the reach of the wireless network. FiWi networks are usually composed of an optical and a wireless component. Since both components are designed to work independently, some mechanisms, such as the different power saving methods in both components, may not cooperate with each other and this may result in an undesirable performance. In this paper, we identify that the conflicting power saving mechanisms cause unnecessary energy consumption and introduce additional delay to the overall FiWi network. To cope with this problem, we propose a novel ONU sleep method, which dynamically control the ONU sleep period based on the STAs energy control mechanism. Finally, we demonstrate that our proposed method has shorter latency and is more efficient in term of energy consumption than the existing method. [ABSTRACT FROM AUTHOR]

Cooperative satellite cells and terrestrial wireless communication networks (comprising macrocells, picocells, and the Internet) are much anticipated access technologies to enable users to seamlessly access rich multimedia content (e.g., TV broadcasting, VoD streaming, and other services) on their devices. Furthermore, context awareness has become popular to accompany these services not only to enhance the users' perceived service quality but also to improve the overall utilization of such cooperative networks. However, delivering context-aware multimedia content through these cooperative networks is associated with a significant research challenge due to the inherently different satellites and terrestrial networks technologies. For example, to provide context-specific TV programs to a user, a satellite needs to adopt unicast-style delivery. This may be an expensive operation since satellites are intrinsically useful for broadcasting or multicasting services to a wide audience. In contrast, terrestrial communication networks can be better suited to perform unicast-based context-aware content delivery. In this article, we address these challenges, and propose a dynamic bandwidth allocation method to effectively utilize the satellite and terrestrial networks for providing context-aware contents to many users. Computer-based simulation results are presented to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM PUBLISHER]

In LTE-Advanced networks, besides the overall coverage provided by traditional macrocells, various classes of low-power nodes (e.g., pico eNBs, femto eNBs, and relays) can be distributed throughout the macrocells as a more targeted underlay to further enhance the area?s spectral efficiency, alleviate traffic hot zones, and thus improve the end-user experience. Considering the limited backhaul connections within lowpower nodes and the imbalanced traffic distribution among different cells, it is highly possible that some cells are severely congested while adjacent cells are very lightly loaded. Therefore, it is of critical importance to achieve efficient load balancing among multi-tier cells in LTEAdvanced networks. However, available techniques such as smart cell and biasing, although able to alleviate congestion or distribute traffic to some extent, cannot respond or adapt flexibly to the real-time traffic distributions among multi-tier cells. Toward this end, we propose in this article a device-to-device communicationbased load balancing algorithm, which utilizes D2D communications as bridges to flexibly offload traffic among different tier cells and achieve efficient load balancing according to their real-time traffic distributions. Besides identifying the research issues that deserve further study, we also present numerical results to show the performance gains that can be achieved by the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Recently, the concept of Internet of Things, referred to as IoT, has drawn a great deal of research attention for realizing an intelligent society. The IoT is expected to comprise millions of heterogeneous smart “things” having sensor terminals, which may collect various types of information. By sending these collected information via IoT, it is possible to construct many smart systems, e.g., automatic prevention of traffic jam and so on. However, the coverage of IoT and the capacity of its ground networks are, still, not capable enough to connect the numerous devices and terminals deployed all over the world. Therefore, there is an urgent need for effective data collection in such systems. In this paper, we focus on effective data collection by satellite-routed sensor system, which makes it possible to gather data from wide areas arbitrarily yet efficiently. However, multiple accesses from the numerous things to a satellite result in data collisions and increase the delay. For effectively resolving the problem of data collisions, we envision a new method, which utilizes a “divide and conquer” approach to collect data from the numerous things based upon demand. Also, we mathematically demonstrate how to optimize the operating time of our proposed. The effectiveness of our proposal is evaluated through numerical results. [ABSTRACT FROM PUBLISHER]

Multi-Layered Satellite Networks (MLSNs) have enormous potential to provide a ubiquitous wireless environment due to their advantages, such as extensive coverage, high network capacity, and lower delay performance. Since MLSNs are flexible and can be expanded easily to construct useful communication networks, researchers have paid a great deal of attention to find out how to use them efficiently. However, traffic congestion may occur in such networks since the distribution of MLSN users is heavily influenced by geographical restrictions, and they may often lead to severe communication delay and throughput degradation. Traditional research has proposed a counter-measure for avoiding traffic congestion caused by traffic flow on each layer. However, they do not consider congestion due to the inter-layer traffic that may, indeed, occur in MLSNs. Therefore, to effectively resolve the problem of traffic congestion, we propose a new MLSN model by envisioning a method to distribute the flow of packets between the two layers of the considered MLSNs for minimizing the packet delivery delay of the network. Moreover, we analyze the effect of the method on the packet delivery delay by considering propagation and queuing latencies. The analysis clearly shows the advantage of our proposed model. Furthermore, computer-based simulation results validate our analysis and demonstrate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]