235 research outputs found
Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs
Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be
integrated into future cellular networks as new aerial mobile users. Providing
cellular connectivity to UAVs will enable a myriad of applications ranging from
online video streaming to medical delivery. However, to enable a reliable
wireless connectivity for the UAVs as well as a secure operation, various
challenges need to be addressed such as interference management, mobility
management and handover, cyber-physical attacks, and authentication. In this
paper, the goal is to expose the wireless and security challenges that arise in
the context of UAV-based delivery systems, UAV-based real-time multimedia
streaming, and UAV-enabled intelligent transportation systems. To address such
challenges, artificial neural network (ANN) based solution schemes are
introduced. The introduced approaches enable the UAVs to adaptively exploit the
wireless system resources while guaranteeing a secure operation, in real-time.
Preliminary simulation results show the benefits of the introduced solutions
for each of the aforementioned cellular-connected UAV application use case.Comment: This manuscript has been accepted for publication in IEEE Wireless
Communication
5G-PPP Technology Board:Delivery of 5G Services Indoors - the wireless wire challenge and solutions
The 5G Public Private Partnership (5G PPP) has focused its research and innovation activities mainly on outdoor use cases and supporting the user and its applications while on the move. However, many use cases inherently apply in indoor environments whereas their requirements are not always properly reflected by the requirements eminent for outdoor applications. The best example for indoor applications can be found is the Industry 4.0 vertical, in which most described use cases are occurring in a manufacturing hall. Other environments exhibit similar characteristics such as commercial spaces in offices, shopping malls and commercial buildings. We can find further similar environments in the media & entertainment sector, culture sector with museums and the transportation sector with metro tunnels. Finally in the residential space we can observe a strong trend for wireless connectivity of appliances and devices in the home. Some of these spaces are exhibiting very high requirements among others in terms of device density, high-accuracy localisation, reliability, latency, time sensitivity, coverage and service continuity. The delivery of 5G services to these spaces has to consider the specificities of the indoor environments, in which the radio propagation characteristics are different and in the case of deep indoor scenarios, external radio signals cannot penetrate building construction materials. Furthermore, these spaces are usually “polluted” by existing wireless technologies, causing a multitude of interreference issues with 5G radio technologies. Nevertheless, there exist cases in which the co-existence of 5G new radio and other radio technologies may be sensible, such as for offloading local traffic. In any case the deployment of networks indoors is advised to consider and be planned along existing infrastructure, like powerlines and available shafts for other utilities. Finally indoor environments expose administrative cross-domain issues, and in some cases so called non-public networks, foreseen by 3GPP, could be an attractive deployment model for the owner/tenant of a private space and for the mobile network operators serving the area. Technology-wise there exist a number of solutions for indoor RAN deployment, ranging from small cell architectures, optical wireless/visual light communication, and THz communication utilising reconfigurable intelligent surfaces. For service delivery the concept of multi-access edge computing is well tailored to host virtual network functions needed in the indoor environment, including but not limited to functions supporting localisation, security, load balancing, video optimisation and multi-source streaming. Measurements of key performance indicators in indoor environments indicate that with proper planning and consideration of the environment characteristics, available solutions can deliver on the expectations. Measurements have been conducted regarding throughput and reliability in the mmWave and optical wireless communication cases, electric and magnetic field measurements, round trip latency measurements, as well as high-accuracy positioning in laboratory environment. Overall, the results so far are encouraging and indicate that 5G and beyond networks must advance further in order to meet the demands of future emerging intelligent automation systems in the next 10 years. Highly advanced industrial environments present challenges for 5G specifications, spanning congestion, interference, security and safety concerns, high power consumption, restricted propagation and poor location accuracy within the radio and core backbone communication networks for the massive IoT use cases, especially inside buildings. 6G and beyond 5G deployments for industrial networks will be increasingly denser, heterogeneous and dynamic, posing stricter performance requirements on the network. The large volume of data generated by future connected devices will put a strain on networks. It is therefore fundamental to discriminate the value of information to maximize the utility for the end users with limited network resources
Mobile Edge Computing
This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists
An overview of VANET vehicular networks
Today, with the development of intercity and metropolitan roadways and with
various cars moving in various directions, there is a greater need than ever
for a network to coordinate commutes. Nowadays, people spend a lot of time in
their vehicles. Smart automobiles have developed to make that time safer, more
effective, more fun, pollution-free, and affordable. However, maintaining the
optimum use of resources and addressing rising needs continues to be a
challenge given the popularity of vehicle users and the growing diversity of
requests for various services. As a result, VANET will require modernized
working practices in the future. Modern intelligent transportation management
and driver assistance systems are created using cutting-edge communication
technology. Vehicular Ad-hoc networks promise to increase transportation
effectiveness, accident prevention, and pedestrian comfort by allowing
automobiles and road infrastructure to communicate entertainment and traffic
information. By constructing thorough frameworks, workflow patterns, and update
procedures, including block-chain, artificial intelligence, and SDN (Software
Defined Networking), this paper addresses VANET-related technologies, future
advances, and related challenges. An overview of the VANET upgrade solution is
given in this document in order to handle potential future problems
A Survey on UAV-enabled Edge Computing: Resource Management Perspective
Edge computing facilitates low-latency services at the network's edge by
distributing computation, communication, and storage resources within the
geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent
advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new
opportunities for edge computing in military operations, disaster response, or
remote areas where traditional terrestrial networks are limited or unavailable.
In such environments, UAVs can be deployed as aerial edge servers or relays to
facilitate edge computing services. This form of computing is also known as
UAV-enabled Edge Computing (UEC), which offers several unique benefits such as
mobility, line-of-sight, flexibility, computational capability, and
cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices
are typically very limited in the context of UEC. Efficient resource management
is, therefore, a critical research challenge in UEC. In this article, we
present a survey on the existing research in UEC from the resource management
perspective. We identify a conceptual architecture, different types of
collaborations, wireless communication models, research directions, key
techniques and performance indicators for resource management in UEC. We also
present a taxonomy of resource management in UEC. Finally, we identify and
discuss some open research challenges that can stimulate future research
directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU
A Dual Sampling Communication Method in Wireless Networks.
PhD ThesisAs mobile wireless data traffic is increasing significantly, the development direction
for wireless networks is focusing on very high data rates, extremely low latency, with
a large number of connected devices and a reduction in energy usage. To satisfy the
rapid rise in user and traffic capacity, raises challenges given the limited bandwidth
resource. The main purpose for this research is to find ways to improve spectral efficiency,
data transmission rate, and reduce latency. Simultaneous wireless transmissions
happening in the same frequency band can help alleviate demand on transmission
slots, with methods like network coding to support decoding at the end terminals.
However, in general, signal asynchrony harms the transmission performance significantly.
The main contribution of this research is the proposal of a Dual Sampling (DS)
method, which aims to relieve the impact of signal asynchrony on simultaneous transmissions.
The key concept behind the DS method is sampling twice within each symbol
period to handle overlapping signals for successful decoding. Simulation results confirm
that it manages to support simultaneous transmissions. Moreover, the DS method
is implemented in both Information-Centric Networks (ICN) and Unmanned Aerial
Vehicles (UAVs) aided wireless networks. Additionally, for ICN, a Cache Migration
Protocol (CMP) is proposed to support simultaneous transmissions which reduces the
transmission latency. While for UAV-aided wireless networks, by exploiting the DS
method, simultaneous transmissions are supported resulting in better optimal max-min
throughput along supported by suitableUAV flight trajectory planning. By demonstrating
the performance gain in the application scenarios of ICN and UAV-aided wireless
networks, the DS method can be regarded as an optional promising transmission mechanism
when communicating with multiple users simultaneously
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