262 research outputs found
Cluster-Based Radio Resource Management for D2D-Supported Safety-Critical V2X Communications
Deploying direct device-to-device (D2D) links is a promising technology for vehicle-to-X (V2X) applications. However, intracell interference, along with stringent requirements on latency and reliability, are challenging issues. In this paper, we study the radio resource management problem for D2D-based safety-critical V2X communications. We first transform the V2X requirements into the constraints that are computable using slowly varying channel state information only. Secondly, we formulate an optimization problem, taking into account the requirements of both vehicular users (V-UEs) and cellular users (C-UEs), where resource sharing can take place not only between a V-UE and a C-UE but also among different V-UEs. The NP-hardness of the problem is rigorously proved. Moreover, a heuristic algorithm, called Cluster-based Resource block sharing and pOWer allocatioN (CROWN), is proposed to solve this problem. Finally, simulation results indicate promising performance of the CROWN scheme
5G NR-V2X: Towards Connected and Cooperative Autonomous Driving
This paper is concerned with the key features and fundamental technology
components for 5G New Radio (NR) for genuine realization of connected and
cooperative autonomous driving. We discuss the major functionalities of
physical layer, Sidelink features and its resource allocation, architecture
flexibility, security and privacy mechanisms, and precise positioning
techniques with an evolution path from existing cellular vehicle-to-everything
(V2X) technology towards NR-V2X. Moreover, we envisage and highlight the
potential of machine learning for further enhancement of various NR-V2X
services. Lastly, we show how 5G NR can be configured to support advanced V2X
use cases in autonomous driving
Resource Sharing and Power Allocation for D2D-based Safety-Critical V2X Communications
Deploying direct device-to-device (D2D) links is considered an enabler for V2X applications, with intra-cell interference and stringent latency and reliability requirements as challenging issues.We investigate the radio resource management problem for D2D-based safety-critical V2X communications. Firstly, we analyze and transform the V2X latency and reliability requirements into mathematical forms that are computable using only slowly varying channel information. Secondly, we propose a problem formulation fulfilling the requirements of V2X, where resource sharing can take place not only between vehicles and cellular users but also among different vehicles. Moreover, a Resource Block Sharing and Power Allocation (RBSPA) algorithm is proposed to solve this problem. Finally, simulations are presented that indicate promising performance of the proposed RBSPA scheme
Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving
This article studies the problem of image segmentation-based semantic
communication in autonomous driving. In real traffic scenes, detecting the key
objects (e.g., vehicles, pedestrians and obstacles) is more crucial than that
of other objects to guarantee driving safety. Therefore, we propose a vehicular
image segmentation-oriented semantic communication system, termed VIS-SemCom,
where image segmentation features of important objects are transmitted to
reduce transmission redundancy. First, to accurately extract image semantics,
we develop a semantic codec based on Swin Transformer architecture, which
expands the perceptual field thus improving the segmentation accuracy. Next, we
propose a multi-scale semantic extraction scheme via assigning the number of
Swin Transformer blocks for diverse resolution features, thus highlighting the
important objects' accuracy. Furthermore, the importance-aware loss is invoked
to emphasize the important objects, and an online hard sample mining (OHEM)
strategy is proposed to handle small sample issues in the dataset. Experimental
results demonstrate that the proposed VIS-SemCom can achieve a coding gain of
nearly 6 dB with a 60% mean intersection over union (mIoU), reduce the
transmitted data amount by up to 70% with a 60% mIoU, and improve the
segmentation intersection over union (IoU) of important objects by 4%, compared
to traditional transmission scheme.Comment: 10 pages, 8 figure
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
Efficient radio resource management for the fifth generation slice networks
It is predicted that the IMT-2020 (5G network) will meet increasing user demands and, hence, it is therefore, expected to be as flexible as possible. The relevant standardisation bodies and academia have accepted the critical role of network slicing in the implementation of the 5G network. The network slicing paradigm allows the physical infrastructure and resources of the mobile network to be “sliced” into logical networks, which are operated by different entities, and then engineered to address the specific requirements of different verticals, business models, and individual subscribers. Network slicing offers propitious solutions to the flexibility requirements of the 5G network. The attributes and characteristics of network slicing support the multi-tenancy paradigm, which is predicted to drastically reduce the operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile network operators. Furthermore, network slices enable mobile virtual network operators to compete with one another using the same physical networks but customising their slices and network operation according to their market segment's characteristics and requirements. However, owing to scarce radio resources, the dynamic characteristics of the wireless links, and its capacity, implementing network slicing at the base stations and the access network xix becomes an uphill task. Moreover, an unplanned 5G slice network deployment results in technical challenges such as unfairness in radio resource allocation, poor quality of service provisioning, network profit maximisation challenges, and rises in energy consumption in a bid to meet QoS specifications. Therefore, there is a need to develop efficient radio resource management algorithms that address the above mentioned technical challenges. The core aim of this research is to develop and evaluate efficient radio resource management algorithms and schemes that will be implemented in 5G slice networks to guarantee the QoS of users in terms of throughput and latency while ensuring that 5G slice networks are energy efficient and economically profitable. This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. It is observed that the proposed new radio swarm-intelligent profit-aware resource allocation (NR-SiRARE) scheme outperforms the LTE-OFDMA swarm-intelligent profit-aware resource (LO-SiRARE) scheme. However, the network profit for the NR-SiRARE is greatly affected by significant degradation of the path loss associated with millimetre waves. Second, this thesis examines the resource allocation challenge in a multi-tenant multi-slice multi-tier heterogeneous network. To maximise the total utility of a multi-tenant multislice multi-tier heterogeneous network, a latency-aware dynamic resource allocation problem is formulated as an optimisation problem. Via the hierarchical decomposition method for heterogeneous networks, the formulated optimisation problem is transformed to reduce the computational complexities of the proposed solutions. Furthermore, a genetic algorithmbased latency-aware resource allocation scheme is proposed to solve the maximum utility problem by considering related constraints. It is observed that GI-LARE scheme outperforms the static slicing (SS) and an optimal resource allocation (ORA) schemes. Moreover, the GI-LARE appears to be near optimal when compared with an exact solution based on spatial branch and bound. Third, this thesis addresses a distributed resource allocation problem in a multi-slice multitier multi-domain network with different players. A three-level hierarchical business model comprising InPs, MVNOs, and service providers (SP) is examined. The radio resource allocation problem is formulated as a maximum utility optimisation problem. A multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multidomain games schemes are proposed to solve the maximum utility optimisation problem. The distributed backtracking scheme is based on the Fisher Market and Auction theory principles. The proposed multi-tier multi-domain scheme outperforms the GI-LARE and the SS schemes. This is attributed to the availability of resources from other InPs and MVNOs; and the flexibility associated with a multi-domain network. Lastly, an energy-efficient resource allocation problem for 5G slice networks in a highly dense heterogeneous environment is investigated. A mathematical formulation of energy-efficient resource allocation in 5G slice networks is developed as a mixed-integer linear fractional optimisation problem (MILFP). The method adopts hierarchical decomposition techniques to reduce complexities. Furthermore, the slice user association, QoS for different slice use cases, an adapted water filling algorithm, and stochastic geometry tools are employed to xxi model the global energy efficiency (GEE) of the 5G slice network. Besides, neither stochastic geometry nor a three-level hierarchical business model schemes have been employed to model the global energy efficiency of the 5G slice network in the literature, making it the first time such method will be applied to 5G slice network. With rigorous numerical simulations based on Monte-Carlo numerical simulation technique, the performance of the proposed algorithms and schemes was evaluated to show their adaptability, efficiency and robustness for a 5G slice network
tinyLTE: Lightweight, Ad-Hoc Deployable Cellular Network for Vehicular Communication
The application of LTE technology has evolved from infrastructure-based
deployments in licensed bands to new use cases covering ad hoc,
device-to-device communications and unlicensed band operation. Vehicular
communication is an emerging field of particular interest for LTE, covering in
our understanding both automotive (cars) as well as unmanned aerial vehicles.
Existing commercial equipment is designed for infrastructure making it
unsuitable for vehicular applications requiring low weight and unlicensed band
support (e.g. 5.9 GHz ITS-band). In this work, we present tinyLTE, a system
design which provides fully autonomous, multi-purpose and ultra-compact LTE
cells by utilizing existing open source eNB and EPC implementations. Due to its
small form factor and low weight, the tinyLTE system enables mobile deployment
on board of cars and drones as well as smooth integration with existing
roadside infrastructure. Additionally, the standalone design allows for systems
to be chained in a multi-hop configuration. The paper describes the lean and
low-cost design concept and implementation followed by a performance evaluation
for single and two-hop configurations at 5.9 GHz. The results from both lab and
field experiments validate the feasibility of the tinyLTE approach and
demonstrate its potential to even support real-time vehicular applications
(e.g. with a lowest average end-to-end latency of around 7 ms in the lab
experiment)
Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications
© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/TITS.2024.3351488Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.Peer reviewe
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
Milestones in Autonomous Driving and Intelligent Vehicles Part \uppercase\expandafter{\romannumeral1}: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing
at a rapid pace due to the convenience, safety, and economic benefits. Although
a number of surveys have reviewed research achievements in this field, they are
still limited in specific tasks and lack systematic summaries and research
directions in the future. Our work is divided into 3 independent articles and
the first part is a Survey of Surveys (SoS) for total technologies of AD and
IVs that involves the history, summarizes the milestones, and provides the
perspectives, ethics, and future research directions. This is the second part
(Part \uppercase\expandafter{\romannumeral1} for this technical survey) to
review the development of control, computing system design, communication, High
Definition map (HD map), testing, and human behaviors in IVs. In addition, the
third part (Part \uppercase\expandafter{\romannumeral2} for this technical
survey) is to review the perception and planning sections. The objective of
this paper is to involve all the sections of AD, summarize the latest technical
milestones, and guide abecedarians to quickly understand the development of AD
and IVs. Combining the SoS and Part \uppercase\expandafter{\romannumeral2}, we
anticipate that this work will bring novel and diverse insights to researchers
and abecedarians, and serve as a bridge between past and future.Comment: 18 pages, 4 figures, 3 table
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