1,614 research outputs found
Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks (C-RANs)
Over the last few years, Cloud Radio Access Network (C-RAN) has arisen as a
transformative architecture for 5G cellular networks that brings the
flexibility and agility of cloud computing to wireless communications. At the
same time, content caching in wireless networks has become an essential
solution to lower the content-access latency and backhaul traffic loading,
which translate into user Quality of Experience (QoE) improvement and network
cost reduction. In this article, a novel Cooperative Hierarchical Caching (CHC)
framework in C-RAN is introduced where contents are jointly cached at the
BaseBand Unit (BBU) and at the Radio Remote Heads (RRHs). Unlike in traditional
approaches, the cache at the BBU, cloud cache, presents a new layer in the
cache hierarchy, bridging the latency/capacity gap between the traditional
edge-based and core-based caching schemes. Trace-driven simulations reveal that
CHC yields up to 80% improvement in cache hit ratio, 21% decrease in average
content-access latency, and 20% reduction in backhaul traffic load compared to
the edge-only caching scheme with the same total cache capacity. Before closing
the article, several challenges and promising opportunities for deploying
content caching in C-RAN are highlighted towards a content-centric mobile
wireless network.Comment: to appear on IEEE Network, July 201
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
The fifth generation (5G) wireless network technology is to be standardized
by 2020, where main goals are to improve capacity, reliability, and energy
efficiency, while reducing latency and massively increasing connection density.
An integral part of 5G is the capability to transmit touch perception type
real-time communication empowered by applicable robotics and haptics equipment
at the network edge. In this regard, we need drastic changes in network
architecture including core and radio access network (RAN) for achieving
end-to-end latency on the order of 1 ms. In this paper, we present a detailed
survey on the emerging technologies to achieve low latency communications
considering three different solution domains: RAN, core network, and caching.
We also present a general overview of 5G cellular networks composed of software
defined network (SDN), network function virtualization (NFV), caching, and
mobile edge computing (MEC) capable of meeting latency and other 5G
requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial
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
Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey
This paper presents a comprehensive literature review on applications of
economic and pricing theory for resource management in the evolving fifth
generation (5G) wireless networks. The 5G wireless networks are envisioned to
overcome existing limitations of cellular networks in terms of data rate,
capacity, latency, energy efficiency, spectrum efficiency, coverage,
reliability, and cost per information transfer. To achieve the goals, the 5G
systems will adopt emerging technologies such as massive Multiple-Input
Multiple-Output (MIMO), mmWave communications, and dense Heterogeneous Networks
(HetNets). However, 5G involves multiple entities and stakeholders that may
have different objectives, e.g., high data rate, low latency, utility
maximization, and revenue/profit maximization. This poses a number of
challenges to resource management designs of 5G. While the traditional
solutions may neither efficient nor applicable, economic and pricing models
have been recently developed and adopted as useful tools to achieve the
objectives. In this paper, we review economic and pricing approaches proposed
to address resource management issues in the 5G wireless networks including
user association, spectrum allocation, and interference and power management.
Furthermore, we present applications of economic and pricing models for
wireless caching and mobile data offloading. Finally, we highlight important
challenges, open issues and future research directions of applying economic and
pricing models to the 5G wireless networks
Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
The emerging Internet of Things (IoT)-driven ultra-dense small cell networks
(UD-SCNs) will need to combat a variety of challenges. On one hand, massive
number of devices sharing the limited wireless resources will render
centralized control mechanisms infeasible due to the excessive cost of
information acquisition and computations. On the other hand, to reduce energy
consumption from fixed power grid and/or battery, many IoT devices may need to
depend on the energy harvested from the ambient environment (e.g., from RF
transmissions, environmental sources). However, due to the opportunistic nature
of energy harvesting, this will introduce uncertainty in the network operation.
In this article, we study the distributed cell association problem for energy
harvesting IoT devices in UD-SCNs. After reviewing the state-of-the-art
research on the cell association problem in small cell networks, we outline the
major challenges for distributed cell association in IoT-driven UD-SCNs where
the IoT devices will need to perform cell association in a distributed manner
in presence of uncertainty (e.g., limited knowledge on channel/network) and
limited computational capabilities. To this end, we propose an approach based
on mean-field multi-armed bandit games to solve the uplink cell association
problem for energy harvesting IoT devices in a UD-SCN. This approach is
particularly suitable to analyze large multi-agent systems under uncertainty
and lack of information. We provide some theoretical results as well as
preliminary performance evaluation results for the proposed approach
Power Control in UAV-Supported Ultra Dense Networks: Communications, Caching, and Energy Transfer
By means of network densification, ultra dense networks (UDNs) can
efficiently broaden the network coverage and enhance the system throughput. In
parallel, unmanned aerial vehicles (UAVs) communications and networking have
attracted increasing attention recently due to their high agility and numerous
applications. In this article, we present a vision of UAV-supported UDNs.
Firstly, we present four representative scenarios to show the broad
applications of UAV-supported UDNs in communications, caching and energy
transfer. Then, we highlight the efficient power control in UAV-supported UDNs
by discussing the main design considerations and methods in a comprehensive
manner. Furthermore, we demonstrate the performance superiority of
UAV-supported UDNs via case study simulations, compared to traditional fixed
infrastructure based networks. In addition, we discuss the dominating technical
challenges and open issues ahead
Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach
Millimeter Wave (MmWave) communication is one of the key technology of the
fifth generation (5G) wireless systems to achieve the expected 1000x data rate.
With large bandwidth at mmWave band, the link capacity between users and base
stations (BS) can be much higher compared to sub-6GHz wireless systems.
Meanwhile, due to the high cost of infrastructure upgrade, it would be
difficult for operators to drastically enhance the capacity of backhaul links
between mmWave BSs and the core network. As a result, the data rate provided by
backhaul may not be sufficient to support all mmWave links, the backhaul
connection becomes the new bottleneck that limits the system performance. On
the other hand, as mmWave channels are subject to random blockage, the data
rates of mmWave users significantly vary over time. With limited backhaul
capacity and highly dynamic data rates of users, how to allocate backhaul
resource to each user remains a challenge for mmWave systems. In this article,
we present a deep reinforcement learning (DRL) approach to address this
challenge. By learning the blockage pattern, the system dynamics can be
captured and predicted, resulting in efficient utilization of backhaul
resource. We begin with a discussion on DRL and its application in wireless
systems. We then investigate the problem backhaul resource allocation and
present the DRL based solution. Finally, we discuss open problems for future
research and conclude this article.Comment: Appear to IEEE Communications Magazine. Version with math contents
and equation
Stochastic Design and Analysis of Wireless Cloud Caching Networks
This paper develops a stochastic geometry-based approach for the modeling,
analysis, and optimization of wireless cloud caching networks comprised of
multiple-antenna radio units (RUs) inside clouds. We consider the Matern
cluster process to model RUs and the probabilistic content placement to cache
files in RUs. Accordingly, we study the exact hit probability for a user of
interest for two strategies; closest selection, where the user is served by the
closest RU that has its requested file, and best selection, where the serving
RU having the requested file provides the maximum instantaneous received power
at the user. As key steps for the analyses, the Laplace transform of out of
cloud interference, the desired link distance distribution in the closest
selection, and the desired link received power distribution in the best
selection are derived. Also, we approximate the derived exact hit probabilities
for both the closest and the best selections in such a way that the related
objective functions for the content caching design of the network can lead to
tractable concave optimization problems. Solving the optimization problems, we
propose algorithms to efficiently find their optimal content placements.
Finally, we investigate the impact of different parameters such as the number
of antennas and the cache memory size on the caching performance
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