5,196 research outputs found
Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks
Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be
promising in the fifth generation (5G) wireless networks. H-CRANs enable users
to enjoy diverse services with high energy efficiency, high spectral
efficiency, and low-cost operation, which are achieved by using cloud computing
and virtualization techniques. However, H-CRANs face many technical challenges
due to massive user connectivity, increasingly severe spectrum scarcity and
energy-constrained devices. These challenges may significantly decrease the
quality of service of users if not properly tackled. Non-orthogonal multiple
access (NOMA) schemes exploit non-orthogonal resources to provide services for
multiple users and are receiving increasing attention for their potential of
improving spectral and energy efficiency in 5G networks. In this article a
framework for energy-efficient NOMA H-CRANs is presented. The enabling
technologies for NOMA H-CRANs are surveyed. Challenges to implement these
technologies and open issues are discussed. This article also presents the
performance evaluation on energy efficiency of H-CRANs with NOMA.Comment: This work has been accepted by IEEE Network. Pages 18, Figure
Maximizing System Throughput Using Cooperative Sensing in Multi-Channel Cognitive Radio Networks
In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access
the licensed spectrum when it is not currently being used by primary users
(PUs). In this paper, we study the throughput maximization problem for a
multi-channel CRN where each SU can only sense a limited number of channels. We
show that this problem is strongly NP-hard, and propose an approximation
algorithm with a factor at least where is a system
parameter reflecting the sensing capability of SUs across channels and their
sensing budgets. This performance guarantee is achieved by exploiting a nice
structural property of the objective function and constructing a particular
matching. Our numerical results demonstrate the advantage of our algorithm
compared with both a random and a greedy sensing assignment algorithm
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
Five Facets of 6G: Research Challenges and Opportunities
Whilst the fifth-generation (5G) systems are being rolled out across the
globe, researchers have turned their attention to the exploration of radical
next-generation solutions. At this early evolutionary stage we survey five main
research facets of this field, namely {\em Facet~1: next-generation
architectures, spectrum and services, Facet~2: next-generation networking,
Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing,
as well as Facet~5: applications of deep learning in 6G networks.} In this
paper, we have provided a critical appraisal of the literature of promising
techniques ranging from the associated architectures, networking, applications
as well as designs. We have portrayed a plethora of heterogeneous architectures
relying on cooperative hybrid networks supported by diverse access and
transmission mechanisms. The vulnerabilities of these techniques are also
addressed and carefully considered for highlighting the most of promising
future research directions. Additionally, we have listed a rich suite of
learning-driven optimization techniques. We conclude by observing the
evolutionary paradigm-shift that has taken place from pure single-component
bandwidth-efficiency, power-efficiency or delay-optimization towards
multi-component designs, as exemplified by the twin-component ultra-reliable
low-latency mode of the 5G system. We advocate a further evolutionary step
towards multi-component Pareto optimization, which requires the exploration of
the entire Pareto front of all optiomal solutions, where none of the components
of the objective function may be improved without degrading at least one of the
other components
Insights on Significant Implication on Research Approach for Enhancing 5G Network System
With the exponential growth of mobile users, there is a massive growth of data as well as novel services to support such data management. However, the existing 4G network is absolutely not meant for catering up such higher demands of bandwidth utilization as well as servicing massive users with similar Quality of service. Such problems are claimed to be effectively addressed by the adoption of 5G networking system. Although the characteristics of 5G networking are theoretically sound, still it is under the roof of the research. Therefore, this paper presents a discussion about the conventional approach as well as an approach using cognitive radio network towards addressing the frequently identified problems of energy, resource allocation, and spectral efficiency. The study collects the existing, recent researches in the domain of 5G communications from various publications. Different from existing review work, the paper also contributes towards identifying the core research findings as well as a significant research gap towards improving the communication in the 5G network system
6G Cellular Networks and Connected Autonomous Vehicles
With 5G mobile communication systems been commercially rolled out, research discussions on next generation mobile systems, i.e., 6G, have started. On the other hand, vehicular technologies are also evolving rapidly, from connected vehicles as coined by V2X (vehicle to everything) to autonomous vehicles to the combination of the two, i.e., the networks of connected autonomous vehicles (CAV). How fast the evolution of these two areas will go head-in-head is of great importance, which is the focus of this paper. After a brief overview on technological evolution of V2X to CAV and 6G key technologies, this paper explores two complementary research directions, namely, 6G for CAVs versus CAVs for 6G. The former investigates how various 6G key enablers, such as THz, cell free communication and artificial intelligence (AI), can be utilized to provide CAV mission-critical services. The latter discusses how CAVs can facilitate effective deployment and operation of 6G systems. This paper attempts to investigate the interactions between the two technologies to spark more research efforts in these areas
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