95 research outputs found
Mobile edge computing in wireless communication networks: design and optimization
This dissertation studies the design and optimization of applying mobile edge computing (MEC) in three kinds of advanced wireless networks, which is motivated by three non-trivial but not thoroughly studied topics in the existing MEC-related literature. First, we study the application of MEC in wireless powered cooperation-assisted systems. The technology of wireless power transfer (WPT) used at the access point (AP) is capable of providing sustainable energy supply for resource-limited user equipment (UEs) to support computation offloading, but also introduces the double-near-far effect into wireless powered communication networks (WPCNs). By leveraging cooperation among near-far users, the system performance can be highly improved through effectively suppressing the double-near-far effect in WPCNs. Then, we consider the application of MEC in the unmanned aerial vehicle (UAV)-assisted relaying systems to make better use of the flexible features of UAV as well as its computing resources. The adopted UAV not only acts as an MEC server to help compute UEs' offloaded tasks but also a relay to forward UEs' offloaded tasks to the AP, thus such kind of cooperation between the UAV and the AP can take the advantages of both sides so as to improve the system performance. Last, heterogeneous cellular networks (HetNets) with the coexistence of MEC and central cloud computing (CCC) are studied to show the complementary and promotional effects between MEC and CCC. The small base stations (SBSs) empowered by edge clouds offer limited edge computing services for UEs, whereas the macro base station (MBS) provides high-performance CCC services for UEs via restricted multiple-input multiple-output (MIMO) backhauls to their associated SBSs. With further considering the case with massive MIMO backhauls, the system performance can be further improved while significantly reducing the computational complexity. In the aforementioned three advanced MEC systems, we mainly focus on minimizing the energy consumption of the systems subject to proper latency constraints, due to the fact that energy consumption and latency are regarded as two important metrics for measuring the performance of MEC-related works. Effective optimization algorithms are proposed to solve the corresponding energy minimization problems, which are further validated by numerical results
Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges
The recent advances in information and communication technology (ICT) have
further extended Internet of Things (IoT) from the sole "things" aspect to the
omnipotent role of "intelligent connection of things". Meanwhile, the concept
of internet of everything (IoE) is presented as such an omnipotent extension of
IoT. However, the IoE realization meets critical challenges including the
restricted network coverage and the limited resource of existing network
technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted
significant attentions attributed to their high mobility, low cost, and
flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE.
This article presents a comprehensive survey on opportunities and challenges of
UAV-enabled IoE. We first present three critical expectations of IoE: 1)
scalability requiring a scalable network architecture with ubiquitous coverage,
2) intelligence requiring a global computing plane enabling intelligent things,
3) diversity requiring provisions of diverse applications. Thereafter, we
review the enabling technologies to achieve these expectations and discuss four
intrinsic constraints of IoE (i.e., coverage constraint, battery constraint,
computing constraint, and security issues). We then present an overview of
UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we
introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in
which we show that Ue-IoE can greatly enhance the scalability, intelligence and
diversity of IoE. Finally, we outline the future directions in Ue-IoE.Comment: 21 pages, 9 figure
Cloud-Edge Orchestration for the Internet-of-Things: Architecture and AI-Powered Data Processing
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe Internet-of-Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralised and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This paper first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.Engineering and Physical Sciences Research Council (EPSRC
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed
Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
Departing from the classic paradigm of data-centric designs, the 6G networks
for supporting edge AI features task-oriented techniques that focus on
effective and efficient execution of AI task. Targeting end-to-end system
performance, such techniques are sophisticated as they aim to seamlessly
integrate sensing (data acquisition), communication (data transmission), and
computation (data processing). Aligned with the paradigm shift, a task-oriented
over-the-air computation (AirComp) scheme is proposed in this paper for
multi-device split-inference system. In the considered system, local feature
vectors, which are extracted from the real-time noisy sensory data on devices,
are aggregated over-the-air by exploiting the waveform superposition in a
multiuser channel. Then the aggregated features as received at a server are fed
into an inference model with the result used for decision making or control of
actuators. To design inference-oriented AirComp, the transmit precoders at edge
devices and receive beamforming at edge server are jointly optimized to rein in
the aggregation error and maximize the inference accuracy. The problem is made
tractable by measuring the inference accuracy using a surrogate metric called
discriminant gain, which measures the discernibility of two object classes in
the application of object/event classification. It is discovered that the
conventional AirComp beamforming design for minimizing the mean square error in
generic AirComp with respect to the noiseless case may not lead to the optimal
classification accuracy. The reason is due to the overlooking of the fact that
feature dimensions have different sensitivity towards aggregation errors and
are thus of different importance levels for classification. This issue is
addressed in this work via a new task-oriented AirComp scheme designed by
directly maximizing the derived discriminant gain
6G wireless communications networks: a comprehensive survey
The commercial fifth-generation (5G) wireless communications networks have already been deployed with the aim of providing high data rates. However, the rapid growth in the number of smart devices and the emergence of the Internet of Everything (IoE) applications, which require an ultra-reliable and low-latency communication, will result in a substantial burden on the 5G wireless networks. As such, the data rate that could be supplied by 5G networks will unlikely sustain the enormous ongoing data traffic explosion. This has motivated research into continuing to advance the existing wireless networks toward the future generation of cellular systems, known as sixth generation (6G). Therefore, it is essential to provide a prospective vision of the 6G and the key enabling technologies for realizing future networks. To this end, this paper presents a comprehensive review/survey of the future evolution of 6G networks. Specifically, the objective of the paper is to provide a comprehensive review/survey about the key enabling technologies for 6G networks, which include a discussion about the main operation principles of each technology, envisioned potential applications, current state-of-the-art research, and the related technical challenges. Overall, this paper provides useful information for industries and academic researchers and discusses the potentials for opening up new research directions
20 Years of Evolution from Cognitive to Intelligent Communications
It has been 20 years since the concept of cognitive radio (CR) was proposed,
which is an efficient approach to provide more access opportunities to connect
massive wireless devices. To improve the spectrum efficiency, CR enables
unlicensed usage of licensed spectrum resources. It has been regarded as the
key enabler for intelligent communications. In this article, we will provide an
overview on the intelligent communication in the past two decades to illustrate
the revolution of its capability from cognition to artificial intelligence
(AI). Particularly, this article starts from a comprehensive review of typical
spectrum sensing and sharing, followed by the recent achievements on the
AI-enabled intelligent radio. Moreover, research challenges in the future
intelligent communications will be discussed to show a path to the real
deployment of intelligent radio. After witnessing the glorious developments of
CR in the past 20 years, we try to provide readers a clear picture on how
intelligent radio could be further developed to smartly utilize the limited
spectrum resources as well as to optimally configure wireless devices in the
future communication systems.Comment: The paper has been accepted by IEEE Transactions on Cognitive
Communications and Networkin
Joint UAV hovering altitude and power control for space-air-ground iot networks
Unmanned aerial vehicles (UAVs) have been widely
used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites
and base stations (BSs) constitute a space-air-ground three-tier
heterogeneous network, which are beneficial in terms of both
providing the seamless coverage as well as of improving the
capacity for increasingly prosperous Internet of Things (IoT)
networks. However, cross-tier interference may be inevitable
among these tightly embraced heterogeneous networks when
sharing the same spectrum. The power association problem
in satellite, UAV and macrocell three-tier networks becomes
a critical issue. In our paper, we propose a two-stage joint
hovering altitude and power control solution for the resource
allocation problem in UAV networks considering the inevitable
cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concaveconvex procedure (CCP) method are used to solve this problem,
followed by a low-complexity greedy search algorithm. Finally,
simulation results show the effectiveness of our proposed twostage joint optimization algorithm in terms of UAV network’s
total throughput
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