2,481 research outputs found
Energy Efficient User Association and Power Allocation in Millimeter Wave Based Ultra Dense Networks with Energy Harvesting Base Stations
Millimeter wave (mmWave) communication technologies have recently emerged as
an attractive solution to meet the exponentially increasing demand on mobile
data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave
technology are expected to increase both energy efficiency and spectral
efficiency. In this paper, user association and power allocation in mmWave
based UDNs is considered with attention to load balance constraints, energy
harvesting by base stations, user quality of service requirements, energy
efficiency, and cross-tier interference limits. The joint user association and
power optimization problem is modeled as a mixed-integer programming problem,
which is then transformed into a convex optimization problem by relaxing the
user association indicator and solved by Lagrangian dual decomposition. An
iterative gradient user association and power allocation algorithm is proposed
and shown to converge rapidly to an optimal point. The complexity of the
proposed algorithm is analyzed and the effectiveness of the proposed scheme
compared with existing methods is verified by simulations.Comment: to appear, IEEE Journal on Selected Areas in Communications, 201
Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks
Taking full advantages of both heterogeneous networks (HetNets) and cloud
access radio access networks (CRANs), heterogeneous cloud radio access networks
(H-CRANs) are presented to enhance both the spectral and energy efficiencies,
where remote radio heads (RRHs) are mainly used to provide high data rates for
users with high quality of service (QoS) requirements, while the high power
node (HPN) is deployed to guarantee the seamless coverage and serve users with
low QoS requirements. To mitigate the inter-tier interference and improve EE
performances in H-CRANs, characterizing user association with RRH/HPN is
considered in this paper, and the traditional soft fractional frequency reuse
(S-FFR) is enhanced. Based on the RRH/HPN association constraint and the
enhanced S-FFR, an energy-efficient optimization problem with the resource
assignment and power allocation for the orthogonal frequency division multiple
access (OFDMA) based H-CRANs is formulated as a non-convex objective function.
To deal with the non-convexity, an equivalent convex feasibility problem is
reformulated, and closedform expressions for the energy-efficient resource
allocation solution to jointly allocate the resource block and transmit power
are derived by the Lagrange dual decomposition method. Simulation results
confirm that the H-CRAN architecture and the corresponding resource allocation
solution can enhance the energy efficiency significantly.Comment: 13 pages, 7 figures, accepted by IEEE TV
User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets
The popularity of cellular internet of things (IoT) is increasing day by day
and billions of IoT devices will be connected to the internet. Many of these
devices have limited battery life with constraints on transmit power. High user
power consumption in cellular networks restricts the deployment of many IoT
devices in 5G. To enable the inclusion of these devices, 5G should be
supplemented with strategies and schemes to reduce user power consumption.
Therefore, we present a novel joint uplink user association and resource
allocation scheme for minimizing user transmit power while meeting the quality
of service. We analyze our scheme for two-tier heterogeneous network (HetNet)
and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms
compared to 20 dBm in state-of-the-art Max reference signal received power
(RSRP) and channel individual offset (CIO) based association schemes
Non-Orthogonal Multiple Access for Air-to-Ground Communication
This paper investigates ground-aerial uplink non-orthogonal multiple access
(NOMA) cellular networks. A rotary-wing unmanned aerial vehicle (UAV) user and
multiple ground users (GUEs) are served by ground base stations (GBSs) by
utilizing the uplink NOMA protocol. The UAV is dispatched to upload specific
information bits to each target GBSs. Specifically, our goal is to minimize the
UAV mission completion time by jointly optimizing the UAV trajectory and
UAV-GBS association order while taking into account the UAV's interference to
non-associated GBSs. The formulated problem is a mixed integer non-convex
problem and involves infinite variables. To tackle this problem, we efficiently
check the feasibility of the formulated problem by utilizing graph theory and
topology theory. Next, we prove that the optimal UAV trajectory needs to
satisfy the \emph{fly-hover-fly} structure. With this insight, we first design
an efficient solution with predefined hovering locations by leveraging graph
theory techniques. Furthermore, we propose an iterative UAV trajectory design
by applying successive convex approximation (SCA) technique, which is
guaranteed to coverage to a locally optimal solution. We demonstrate that the
two proposed designs exhibit polynomial time complexity. Finally, numerical
results show that: 1) the SCA based design outperforms the fly-hover-fly based
design; 2) the UAV mission completion time is significantly minimized with
proposed NOMA schemes compared with the orthogonal multiple access (OMA)
scheme; 3) the increase of GUEs' quality of service (QoS) requirements will
increase the UAV mission completion time
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
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
Dynamic Joint Uplink and Downlink Optimization for Uplink and Downlink Decoupling-Enabled 5G Heterogeneous Networks
The concept of user-centric and personalized service in the fifth generation
(5G) mobile networks encourages technical solutions such as dynamic asymmetric
uplink/downlink resource allocation and elastic association of cells to users
with decoupled uplink and downlink (DeUD) access. In this paper we develop a
joint uplink and downlink optimization algorithm for DeUD-enabled wireless
networks for adaptive joint uplink and downlink bandwidth allocation and power
control, under different link association policies. Based on a general model of
inter-cell interference, we propose a three-step optimization algorithm to
jointly optimize the uplink and downlink bandwidth allocation and power
control, using the fixed point approach for nonlinear operators with or without
monotonicity, to maximize the minimum level of quality of service satisfaction
per link, subjected to a general class of resource (power and bandwidth)
constraints. We present numerical results illustrating the theoretical findings
for network simulator in a real-world setting, and show the advantage of our
solution compared to the conventional proportional fairness resource allocation
schemes in both the coupled uplink and downlink (CoUD) access and the novel
link association schemes in DeUD.Comment: 17 pages, 8 figure
Downlink Energy Efficiency of Power Allocation and Wireless Backhaul Bandwidth Allocation in Heterogeneous Small Cell Networks
The widespread application of wireless services and dense devices access have
triggered huge energy consumption. Because of the environmental and financial
considerations, energy-efficient design in wireless networks becomes an
inevitable trend. To the best of the authors' knowledge, energy-efficient
orthogonal frequency division multiple access heterogeneous small cell
optimization comprehensively considering energy efficiency maximization, power
allocation, wireless backhaul bandwidth allocation, and user Quality of Service
is a novel approach and research direction, and it has not been investigated.
In this paper, we study the energy-efficient power allocation and wireless
backhaul bandwidth allocation in orthogonal frequency division multiple access
heterogeneous small cell networks. Different from the existing resource
allocation schemes that maximize the throughput, the studied scheme maximizes
energy efficiency by allocating both transmit power of each small cell base
station to users and bandwidth for backhauling, according to the channel state
information and the circuit power consumption. The problem is first formulated
as a non-convex nonlinear programming problem and then it is decomposed into
two convex subproblems. A near optimal iterative resource allocation algorithm
is designed to solve the resource allocation problem. A suboptimal
low-complexity approach is also developed by exploring the inherent structure
and property of the energy-efficient design. Simulation results demonstrate the
effectiveness of the proposed algorithms by comparing with the existing
schemes.Comment: to appear in IEEE Transactions on Communication
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
Energy Efficient Resource Allocation for Hybrid Services with Future Channel Gains
In this paper, we propose a framework to maximize energy efficiency (EE) of a
system supporting real-time (RT) and non-real-time services by exploiting
future average channel gains of mobile users, which change in the timescale of
seconds and are reported predictable within a minute-long time window. To
demonstrate the potential of improving EE by jointly optimizing resource
allocation for both services by harnessing both future average channel gains
and current instantaneous channel gains, we optimize a two-timescale policy
with perfect prediction, by taking orthogonal frequency division multiple
access system serving RT and video-on-demand (VoD) users as an example.
Considering that fine-grained prediction for every user is with high cost, we
propose a heuristic policy that only needs to predict the median of average
channel gains of VoD users. Simulation results show that the optimal policy
outperforms relevant counterparts, indicating the necessity of the joint
optimization for both services and for two timescales. Besides, the heuristic
policy performs closely to the optimal policy with perfect prediction while
becomes superior with large prediction errors. This suggests that the EE gain
over non-predictive policies can be captured with coarse-grained prediction.Comment: The manuscript has been submitted to IEEE Transactions on Green
Communications and Networks. It is in the third round of revie
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