270 research outputs found
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements
This paper considers one-hop device-to-device (D2D)-assisted wireless caching
networks that cache video files of varying quality levels, with the assumption
that the base station can control the video quality but cache-enabled devices
cannot. Two problems arise in such a caching network: file placement problem
and node association problem. This paper suggests a method to cache videos of
different qualities, and thus of varying file sizes, by maximizing the sum of
video quality measures that users can enjoy. There exists an interesting
trade-off between video quality and video diversity, i.e., the ability to
provision diverse video files. By caching high-quality files, the cache-enabled
devices can provide high-quality video, but cannot cache a variety of files.
Conversely, when the device caches various files, it cannot provide a good
quality for file-requesting users. In addition, when multiple devices cache the
same file but their qualities are different, advanced node association is
required for file delivery. This paper proposes a node association algorithm
that maximizes time-averaged video quality for multiple users under a playback
delay constraint. In this algorithm, we also consider request collision, the
situation where several users request files from the same device at the same
time, and we propose two ways to cope with the collision: scheduling of one
user and non-orthogonal multiple access. Simulation results verify that the
proposed caching method and the node association algorithm work reliably.Comment: 13 pages, 11 figures, accepted for publication in IEEE Journal on
Selected Areas in Communication
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
Analysis and Optimization of Service Delay for Multi-quality Videos in Multi-tier Heterogeneous Network with Random Caching
Aiming to minimize service delay, we propose a new random caching scheme in
device-to-device (D2D)-assisted heterogeneous network. To support diversified
viewing qualities of multimedia video services, each video file is encoded into
a base layer (BL) and multiple enhancement layers (ELs) by scalable video
coding (SVC). A super layer, including the BL and several ELs, is transmitted
to every user. We define and quantify the service delay of multi-quality videos
by deriving successful transmission probabilities when a user is served by a
D2D helper, a small-cell base station (SBS) and a macro-cell base station
(MBS). We formulate a delay minimization problem subject to the limited cache
sizes of D2D helpers and SBSs. The structure of the optimal solutions to the
problem is revealed, and then an improved standard gradient projection method
is designed to effectively obtain the solutions. Both theoretical analysis and
Monte-Carlo simulations validate the successful transmission probabilities.
Compared with three benchmark caching policies, the proposed SVC-based random
caching scheme is superior in terms of reducing the service delay.Comment: 13 pages, 8 figures, IEEE Systems Journal, Accepte
Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System
The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation
Energy Prioritized Caching for Cellular D2D Networks
Multimedia content transmission heavily taxes network resources and puts a
significant burden on wireless systems in terms of capacity and energy
consumption. In this context, device-to-device (D2D) paradigm has a utilitarian
value to alleviate the network burden by utilizing short-range transmissions
with less energy cost. For the realization of proper D2D networking, caching in
devices is essential. Additionally, caching needs to operate efficiently with
the aim of realizing network-wide energy improvements. To this end, we study
multimedia caching in cellular D2D networks and propose an energy prioritized
D2D caching (EPDC) algorithm in this work. We also investigate the optimal
caching policy in that system setting. The impact of device capacity and D2D
transmission range on energy consumption is studied by focusing on different
operation modes such as D2D and base station transmissions. According to the
simulation results, EPDC algorithm has substantial energy-efficiency gains over
the commonly utilized Least Recently Used (LRU) algorithm and content-based
caching algorithms PDC and SXO. For larger D2D transmission ranges, this
improvement becomes more evident
Machine Learning for Multimedia Communications
Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise
Content Caching and Delivery in Heterogeneous Vehicular Networks
Connected and automated vehicles (CAVs), which enable information exchange and content delivery in real time, are expected to revolutionize current transportation systems for better driving safety, traffic efficiency, and environmental sustainability. However, the emerging CAV applications such as content delivery pose stringent requirements on latency, throughput, reliability, and global connectivity. The current wireless networks face significant challenges to satisfy the requirements due to scarce radio spectrum resources, inflexibility to dynamic traffic demands, and geographic-constrained fixed infrastructure deployment. To empower multifarious CAV content delivery, heterogeneous vehicular networks (HetVNets), which integrate the terrestrial networks with aerial networks formed by unmanned aerial vehicles (UAVs) and space networks constituting of low Earth orbit (LEO) satellites, can guarantee reliable, flexible, cost-effective, and globally seamless service provisioning. In addition, edge caching is a promising solution to facilitate content delivery by caching popular files in the HetVNet access points (APs) to relieve the backhaul traffic with a lower delivery delay. The main technical issues are: 1) to fully reveal the potential of HetVNets for content delivery performance enhancement, content caching scheme design in HetVNets should jointly consider network characteristics, vehicle mobility patterns, content popularity, and APs’ caching capacities; 2) to fully exploit the controllable mobility and agility of UAVs to support dynamic vehicular content demands, the caching scheme and trajectory design for UAVs should be jointly optimized, which has not been well addressed due to their intricate inter-coupling relationships; and 3) for caching-based content delivery in HetVNets, a cooperative content delivery scheme should be designed to enable the cooperation among different network segments with ingenious utilization of heterogeneous network resources.
In this thesis, we design the content caching and delivery schemes in the caching-enabled HetVNet to address the three technical issues. First, we study the content caching in HetVNets with fixed terrestrial APs including cellular base stations (CBSs), Wi-Fi roadside units (RSUs), and TV white space (TVWS) stations. To characterize the intermittent network connection caused by limited network coverage and high vehicle mobility, we establish an on-off model with service interruptions to describe the vehicular content delivery process. Content coding then is leveraged to resist the impact of unstable network connections and enhance caching efficiency. By jointly considering file characteristics and network conditions, the content placement is formulated as an integer linear programming (ILP) problem. Adopting the idea of the student admission model, the ILP problem is then transformed into a many-to-one matching problem between content files and HetVNet APs and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with a low complexity. Second, UAV-aided caching is considered to assist vehicular content delivery in aerial-ground vehicular networks (AGVN) and a joint caching and trajectory optimization (JCTO) problem is investigated to jointly optimize content caching, content delivery, and UAV trajectory. To enable real-time decision-making in highly dynamic vehicular networks, we propose a deep supervised learning scheme to solve the JCTO problem. Specifically, we first devise a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content caching policy, we design a time-based graph decomposition method to jointly optimize content delivery and UAV trajectory, with which we then leverage the particle swarm optimization algorithm to optimize the content caching. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make online decisions. With the CNN-based model, a function mapping the input network information to output decisions can be intelligently learnt to make timely inferences. Extensive trace-driven experiments are conducted to demonstrate the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model. Third, we investigate caching-assisted cooperative content delivery in space-air-ground integrated vehicular networks (SAGVNs), where vehicular content requests can be cooperatively served by multiple APs in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular quality-of-service (QoS) requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay compared to the benchmark schemes.
In summary, we have investigated the content caching in terrestrial networks with fixed APs, joint caching and trajectory optimization in the AGVN, and caching-assisted cooperative content delivery in the SAGVN. The proposed schemes and theoretical results should provide useful guidelines for future research in the caching scheme design and efficient utilization of network resources in caching-enabled heterogeneous wireless networks
Recommended from our members
Multimedia delivery in the future internet
The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio
and video are produced, distributed, shared, managed and consumed on-line through various networks,
like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white
paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked
challenges of the Networked Media in the transition to the Future of the Internet.
Internet has evolved and changed the way we work and live. End users of the Internet have been confronted
with a bewildering range of media, services and applications and of technological innovations concerning
media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace
of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more
than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so
regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected
to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising
to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged
that in a near- to mid-term future, the Internet will provide the means to share and distribute (new)
multimedia content and services with superior quality and striking flexibility, in a trusted and personalized
way, improving citizens’ quality of life, working conditions, edutainment and safety.
In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe
network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as
community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of
interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and
innovative applications “on the move”, like virtual collaboration environments, personalised services/
media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content
combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P
networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to
contribute towards such a vision.
Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6)
and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily
contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way
ahead in the area of Content Aware media delivery platforms
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