820 research outputs found
Effective Capacity in Wireless Networks: A Comprehensive Survey
Low latency applications, such as multimedia communications, autonomous
vehicles, and Tactile Internet are the emerging applications for
next-generation wireless networks, such as 5th generation (5G) mobile networks.
Existing physical-layer channel models, however, do not explicitly consider
quality-of-service (QoS) aware related parameters under specific delay
constraints. To investigate the performance of low-latency applications in
future networks, a new mathematical framework is needed. Effective capacity
(EC), which is a link-layer channel model with QoS-awareness, can be used to
investigate the performance of wireless networks under certain statistical
delay constraints. In this paper, we provide a comprehensive survey on existing
works, that use the EC model in various wireless networks. We summarize the
work related to EC for different networks such as cognitive radio networks
(CRNs), cellular networks, relay networks, adhoc networks, and mesh networks.
We explore five case studies encompassing EC operation with different design
and architectural requirements. We survey various delay-sensitive applications
such as voice and video with their EC analysis under certain delay constraints.
We finally present the future research directions with open issues covering EC
maximization
Radio Resource Allocation Algorithms for Multi-Service OFDMA Networks: The Uniform Power Loading Scenario
Adaptive Radio Resource Allocation is essential for guaranteeing high
bandwidth and power utilization as well as satisfying heterogeneous
Quality-of-Service requests regarding next generation broadband multicarrier
wireless access networks like LTE and Mobile WiMAX. A downlink OFDMA
single-cell scenario is considered where heterogeneous Constant-Bit-Rate and
Best-Effort QoS profiles coexist and the power is uniformly spread over the
system bandwidth utilizing a Uniform Power Loading (UPL) scenario. We express
this particular QoS provision scenario in mathematical terms, as a variation of
the well-known generalized assignment problem answered in the combinatorial
optimization field. Based on this concept, we propose two heuristic search
algorithms for dynamically allocating subchannels to the competing QoS classes
and users which are executed under polynomially-bounded cost. We also propose
an Integer Linear Programming model for optimally solving and acquiring a
performance upper bound for the same problem at reasonable yet high execution
times. Through extensive simulation results we show that the proposed
algorithms exhibit high close-to-optimal performance, thus comprising
attractive candidates for implementation in modern OFDMA-based systems.Comment: accepted for publication at the Springer Telecommunication Systems
Journal (TSMJ
Leveraging Synergy of 5G SDWN and Multi-Layer Resource Management for Network Optimization
Fifth-generation (5G) cellular wireless networks are envisioned to predispose
service-oriented, flexible, and spectrum/energy-efficient edge-to-core
infrastructure, aiming to offer diverse applications. Convergence of
software-defined networking (SDN), software-defined radio (SDR) compatible with
multiple radio access technologies (RATs), and virtualization on the concept of
5G software-defined wireless networking (5G-SDWN) is a promising approach to
provide such a dynamic network. The principal technique behind the 5G-SDWN
framework is the separation of the control and data planes, from the deep core
entities to edge wireless access points (APs). This separation allows the
abstraction of resources as transmission parameters of each user over the
5G-SDWN. In this user-centric and service-oriented environment, resource
management plays a critical role to achieve efficiency and reliability.
However, it is natural to wonder if 5G-SDWN can be leveraged to enable
converged multi-layer resource management over the portfolio of resources, and
reciprocally, if CML resource management can effectively provide performance
enhancement and reliability for 5G-SDWN. We believe that replying to these
questions and investigating this mutual synergy are not trivial, but
multidimensional and complex for 5G-SDWN, which consists of different
technologies and also inherits legacy generations of wireless networks. In this
paper, we propose a flexible protocol structure based on three mentioned
pillars for 5G-SDWN, which can handle all the required functionalities in a
more crosslayer manner. Based on this, we demonstrate how the general framework
of CML resource management can control the end user quality of experience. For
two scenarios of 5G-SDWN, we investigate the effects of joint user-association
and resource allocation via CML resource management to improve performance in a
virtualized network
A Survey on High-Speed Railway Communications: A Radio Resource Management Perspective
High-speed railway (HSR) communications will become a key feature supported
by intelligent transportation communication systems. The increasing demand for
HSR communications leads to significant attention on the study of radio
resource management (RRM), which enables efficient resource utilization and
improved system performance. RRM design is a challenging problem due to
heterogenous quality of service (QoS) requirements and dynamic characteristics
of HSR wireless communications. The objective of this paper is to provide an
overview on the key issues that arise in the RRM design for HSR wireless
communications. A detailed description of HSR communication systems is first
presented, followed by an introduction on HSR channel models and
characteristics, which are vital to the cross-layer RRM design. Then we provide
a literature survey on state-of-the-art RRM schemes for HSR wireless
communications, with an in-depth discussion on various RRM aspects including
admission control, mobility management, power control and resource allocation.
Finally, this paper outlines the current challenges and open issues in the area
of RRM design for HSR wireless communications.Comment: 40 pages, 10 figures. Submitted to Computer Communication
QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks
In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the
optimal routing by satisfying the Quality of Service (QoS) constraints is an
ambitious task. Multiple paths are available from the source node to the
gateway for reliability, and sometimes it is necessary to deal with failures of
the link in WMN. A major challenge in a MCMR-WMN is finding the routing with
QoS satisfied and an interference free path from the redundant paths, in order
to transmit the packets through this path. The Particle Swarm Optimization
(PSO) is an optimization technique to find the candidate solution in the search
space optimally, and it applies artificial intelligence to solve the routing
problem. On the other hand, the Genetic Algorithm (GA) is a population based
meta-heuristic optimization algorithm inspired by the natural evolution, such
as selection,mutation and crossover. PSO can easily fall into a local optimal
solution, at the same time GA is not suitable for dynamic data due to the
underlying dynamic network. In this paper we propose an optimal intelligent
routing, using a Hybrid PSO-GA, which also meets the QoS constraints. Moreover,
it integrates the strength of PSO and GA. The QoS constraints, such as
bandwidth, delay, jitter and interference are transformed into penalty
functions. The simulation results show that the hybrid approach outperforms PSO
and GA individually, and it takes less convergence time comparatively, keeping
away from converging prematurely.
Keywords: Wireless mesh networks, Multi-radio, Multi-channel, Particle swarm
optimization, Genetic algorithm, Quality of service.Comment: 15 pages in Cybernetics and Information Technologies,Volume 15, No 1,
201
Efficient ZF-WF Strategy for Sum-Rate Maximization of MU-MISO Cognitive Radio Networks
This article presents an efficient quasi-optimal sum rate (SR) maximization
technique based on zero-forcing water-filling (ZFWF) algorithm directly applied
to cognitive radio networks (CRNs). We have defined the non-convexity nature of
the optimization problem in the context of CRNs while we have offered all
necessary conditions to solve the related SR maximization problem, which
considers power limit at cognitive transmitter and interference levels at
primary users (PUs) and secondary users (SUs). A general expression capable to
determine the optimal number of users as a function of the main system
parameters, namely the signal-to-interference-plus-noise ratio (SINR) and the
number of BS antennas is proposed. Our numerical results for the CRN
performance are analyzed in terms of both BER and sum-capacity for the proposed
ZF-WF precoding technique, and compared to the classical minimum mean square
error (MMSE), corroborating the effectiveness of the proposed technique
operating in multi user multiple input single output (MU-MISO) CRNsComment: 23 pages, 9 figures, 2 table
Survey on QoE\QoS Correlation Models For Multimedia Services
This paper presents a brief review of some existing correlation models which
attempt to map Quality of Service (QoS) to Quality of Experience (QoE) for
multimedia services. The term QoS refers to deterministic network behaviour, so
that data can be transported with a minimum of packet loss, delay and maximum
bandwidth. QoE is a subjective measure that involves human dimensions; it ties
together user perception, expectations, and experience of the application and
network performance. The Holy Grail of subjective measurement is to predict it
from the objective measurements; in other words predict QoE from a given set of
QoS parameters or vice versa. Whilst there are many quality models for
multimedia, most of them are only partial solutions to predicting QoE from a
given QoS. This contribution analyses a number of previous attempts and
optimisation techniquesthat can reliably compute the weighting coefficients for
the QoS/QoE mapping.Comment: 20 pages, International Journal of Distributed and Parallel Systems
(IJDPS
A Journey from Improper Gaussian Signaling to Asymmetric Signaling
The deviation of continuous and discrete complex random variables from the
traditional proper and symmetric assumption to a generalized improper and
asymmetric characterization (accounting correlation between a random entity and
its complex conjugate), respectively, introduces new design freedom and various
potential merits. As such, the theory of impropriety has vast applications in
medicine, geology, acoustics, optics, image and pattern recognition, computer
vision, and other numerous research fields with our main focus on the
communication systems. The journey begins from the design of improper Gaussian
signaling in the interference-limited communications and leads to a more
elaborate and practically feasible asymmetric discrete modulation design. Such
asymmetric shaping bridges the gap between theoretically and practically
achievable limits with sophisticated transceiver and detection schemes in both
coded/uncoded wireless/optical communication systems. Interestingly,
introducing asymmetry and adjusting the transmission parameters according to
some design criterion render optimal performance without affecting the
bandwidth or power requirements of the systems. This dual-flavored article
initially presents the tutorial base content covering the interplay of
reality/complexity, propriety/impropriety and circularity/noncircularity and
then surveys majority of the contributions in this enormous journey.Comment: IEEE COMST (Early Access
From 4G to 5G: Self-organized Network Management meets Machine Learning
In this paper, we provide an analysis of self-organized network management,
with an end-to-end perspective of the network. Self-organization as applied to
cellular networks is usually referred to Self-organizing Networks (SONs), and
it is a key driver for improving Operations, Administration, and Maintenance
(OAM) activities. SON aims at reducing the cost of installation and management
of 4G and future 5G networks, by simplifying operational tasks through the
capability to configure, optimize and heal itself. To satisfy 5G network
management requirements, this autonomous management vision has to be extended
to the end to end network. In literature and also in some instances of products
available in the market, Machine Learning (ML) has been identified as the key
tool to implement autonomous adaptability and take advantage of experience when
making decisions. In this paper, we survey how network management can
significantly benefit from ML solutions. We review and provide the basic
concepts and taxonomy for SON, network management and ML. We analyse the
available state of the art in the literature, standardization, and in the
market. We pay special attention to 3rd Generation Partnership Project (3GPP)
evolution in the area of network management and to the data that can be
extracted from 3GPP networks, in order to gain knowledge and experience in how
the network is working, and improve network performance in a proactive way.
Finally, we go through the main challenges associated with this line of
research, in both 4G and in what 5G is getting designed, while identifying new
directions for research.Comment: 23 pages, 3 figures, Surve
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and
efficient wireless communications than ever before. For this reason, techniques
such as spectrum sharing, dynamic spectrum access, extraction of signal
intelligence and optimized routing will soon become essential components of the
IoT wireless communication paradigm. Given that the majority of the IoT will be
composed of tiny, mobile, and energy-constrained devices, traditional
techniques based on a priori network optimization may not be suitable, since
(i) an accurate model of the environment may not be readily available in
practical scenarios; (ii) the computational requirements of traditional
optimization techniques may prove unbearable for IoT devices. To address the
above challenges, much research has been devoted to exploring the use of
machine learning to address problems in the IoT wireless communications domain.
This work provides a comprehensive survey of the state of the art in the
application of machine learning techniques to address key problems in IoT
wireless communications with an emphasis on its ad hoc networking aspect.
First, we present extensive background notions of machine learning techniques.
Then, by adopting a bottom-up approach, we examine existing work on machine
learning for the IoT at the physical, data-link and network layer of the
protocol stack. Thereafter, we discuss directions taken by the community
towards hardware implementation to ensure the feasibility of these techniques.
Additionally, before concluding, we also provide a brief discussion of the
application of machine learning in IoT beyond wireless communication. Finally,
each of these discussions is accompanied by a detailed analysis of the related
open problems and challenges.Comment: Ad Hoc Networks Journa
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