5,285 research outputs found
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks
Effective resource allocation plays a pivotal role for performance
optimization in wireless networks. Unfortunately, typical resource allocation
problems are mixed-integer nonlinear programming (MINLP) problems, which are
NP-hard. Machine learning based methods recently emerge as a disruptive way to
obtain near-optimal performance for MINLP problems with affordable
computational complexity. However, they suffer from severe performance
deterioration when the network parameters change, which commonly happens in
practice and can be characterized as the task mismatch issue. In this paper, we
propose a transfer learning method via self-imitation, to address this issue
for effective resource allocation in wireless networks. It is based on a
general "learning to optimize" framework for solving MINLP problems. A unique
advantage of the proposed method is that it can tackle the task mismatch issue
with a few additional unlabeled training samples, which is especially important
when transferring to large-size problems. Numerical experiments demonstrate
that with much less training time, the proposed method achieves comparable
performance with the model trained from scratch with sufficient amount of
labeled samples. To the best of our knowledge, this is the first work that
applies transfer learning for resource allocation in wireless networks
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples
Effective resource management plays a pivotal role in wireless networks,
which, unfortunately, results in challenging mixed-integer nonlinear
programming (MINLP) problems in most cases. Machine learning-based methods have
recently emerged as a disruptive way to obtain near-optimal performance for
MINLPs with affordable computational complexity. There have been some attempts
in applying such methods to resource management in wireless networks, but these
attempts require huge amounts of training samples and lack the capability to
handle constrained problems. Furthermore, they suffer from severe performance
deterioration when the network parameters change, which commonly happens and is
referred to as the task mismatch problem. In this paper, to reduce the sample
complexity and address the feasibility issue, we propose a framework of
Learning to Optimize for Resource Management (LORM). Instead of the end-to-end
learning approach adopted in previous studies, LORM learns the optimal pruning
policy in the branch-and-bound algorithm for MINLPs via a sample-efficient
method, namely, imitation learning. To further address the task mismatch
problem, we develop a transfer learning method via self-imitation in LORM,
named LORM-TL, which can quickly adapt a pre-trained machine learning model to
the new task with only a few additional unlabeled training samples. Numerical
simulations will demonstrate that LORM outperforms specialized state-of-the-art
algorithms and achieves near-optimal performance, while achieving significant
speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by
relying on a few unlabeled samples, achieves comparable performance with the
model trained from scratch with sufficient labeled samples.Comment: arXiv admin note: text overlap with arXiv:1811.0710
Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems
Mobile edge computing (MEC) and wireless power transfer (WPT) are two
promising techniques to enhance the computation capability and to prolong the
operational time of low-power wireless devices that are ubiquitous in Internet
of Things. However, the computation performance and the harvested energy are
significantly impacted by the severe propagation loss. In order to address this
issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is
studied in this paper. The computation rate maximization problems in a
UAV-enabled MEC wireless powered system are investigated under both partial and
binary computation offloading modes, subject to the energy harvesting causal
constraint and the UAV's speed constraint. These problems are non-convex and
challenging to solve. A two-stage algorithm and a three-stage alternative
algorithm are respectively proposed for solving the formulated problems. The
closed-form expressions for the optimal central processing unit frequencies,
user offloading time, and user transmit power are derived. The optimal
selection scheme on whether users choose to locally compute or offload
computation tasks is proposed for the binary computation offloading mode.
Simulation results show that our proposed resource allocation schemes
outperforms other benchmark schemes. The results also demonstrate that the
proposed schemes converge fast and have low computational complexity.Comment: This paper has been accepted by IEEE JSA
Spatial Domain Simultaneous Information and Power Transfer for MIMO Channels
In this paper, we theoretically investigate a new technique for simultaneous
information and power transfer (SWIPT) in multiple-input multiple-output (MIMO)
point-to-point with radio frequency energy harvesting capabilities. The
proposed technique exploits the spatial decomposition of the MIMO channel and
uses the eigenchannels either to convey information or to transfer energy. In
order to generalize our study, we consider channel estimation error in the
decomposition process and the interference between the eigenchannels. An
optimization problem that minimizes the total transmitted power subject to
maximum power per eigenchannel, information and energy constraints is
formulated as a mixed-integer nonlinear program and solved to optimality using
mixed-integer second-order cone programming. A near-optimal mixed-integer
linear programming solution is also developed with robust computational
performance. A polynomial complexity algorithm is further proposed for the
optimal solution of the problem when no maximum power per eigenchannel
constraints are imposed. In addition, a low polynomial complexity algorithm is
developed for the power allocation problem with a given eigenchannel
assignment, as well as a low-complexity heuristic for solving the eigenchannel
assignment problem.Comment: 14 pages, 5 figures, Accepted for publication in IEEE Trans. on
Wireless Communication
Adaptive Task Allocation for Mobile Edge Learning
This paper aims to establish a new optimization paradigm for implementing
realistic distributed learning algorithms, with performance guarantees, on
wireless edge nodes with heterogeneous computing and communication capacities.
We will refer to this new paradigm as `Mobile Edge Learning (MEL)'. The problem
of dynamic task allocation for MEL is considered in this paper with the aim to
maximize the learning accuracy, while guaranteeing that the total times of data
distribution/aggregation over heterogeneous channels, and local computing
iterations at the heterogeneous nodes, are bounded by a preset duration. The
problem is first formulated as a quadratically-constrained integer linear
problem. Being an NP-hard problem, the paper relaxes it into a non-convex
problem over real variables. We thus proposed two solutions based on deriving
analytical upper bounds of the optimal solution of this relaxed problem using
Lagrangian analysis and KKT conditions, and the use of suggest-and-improve
starting from equal batch allocation, respectively. The merits of these
proposed solutions are exhibited by comparing their performances to both
numerical approaches and the equal task allocation approach.Comment: 8 pages, 2 figures, submitted to IEEE WCNC Workshop 2019, Morocc
A Survey on Device-to-Device Communication in Cellular Networks
Device-to-Device (D2D) communication was initially proposed in cellular
networks as a new paradigm to enhance network performance. The emergence of new
applications such as content distribution and location-aware advertisement
introduced new use-cases for D2D communications in cellular networks. The
initial studies showed that D2D communication has advantages such as increased
spectral efficiency and reduced communication delay. However, this
communication mode introduces complications in terms of interference control
overhead and protocols that are still open research problems. The feasibility
of D2D communications in LTE-A is being studied by academia, industry, and the
standardization bodies. To date, there are more than 100 papers available on
D2D communications in cellular networks and, there is no survey on this field.
In this article, we provide a taxonomy based on the D2D communicating spectrum
and review the available literature extensively under the proposed taxonomy.
Moreover, we provide new insights to the over-explored and under-explored areas
which lead us to identify open research problems of D2D communication in
cellular networks.Comment: 18 pages; 8 figures; Accepted for publication in IEEE Communications
Surveys and Tutorial
Large-Scale Convex Optimization for Ultra-Dense Cloud-RAN
The heterogeneous cloud radio access network (Cloud-RAN) provides a
revolutionary way to densify radio access networks. It enables centralized
coordination and signal processing for efficient interference management and
flexible network adaptation. Thus, it can resolve the main challenges for
next-generation wireless networks, including higher energy efficiency and
spectral efficiency, higher cost efficiency, scalable connectivity, and low
latency. In this article, we shall provide an algorithmic thinking on the new
design challenges for the dense heterogeneous Cloud-RAN based on convex
optimization. As problem sizes scale up with the network size, we will
demonstrate that it is critical to take unique structures of design problems
and inherent characteristics of wireless channels into consideration, while
convex optimization will serve as a powerful tool for such purposes. Network
power minimization and channel state information acquisition will be used as
two typical examples to demonstrate the effectiveness of convex optimization
methods. We will then present a two-stage framework to solve general
large-scale convex optimization problems, which is amenable to parallel
implementation in the cloud data center.Comment: to appear in IEEE Wireless Commun. Mag., June 201
Joint Spectrum Allocation and Structure Optimization in Green Powered Heterogeneous Cognitive Radio Networks
We aim at maximizing the sum rate of secondary users (SUs) in OFDM-based
Heterogeneous Cognitive Radio (CR) Networks using RF energy harvesting.
Assuming SUs operate in a time switching fashion, each time slot is partitioned
into three non-overlapping parts devoted for energy harvesting, spectrum
sensing and data transmission. The general problem of joint resource allocation
and structure optimization is formulated as a Mixed Integer Nonlinear
Programming task which is NP-hard and intractable. Thus, we propose to tackle
it by decomposing it into two subproblems. We first propose a sub-channel
allocation scheme to approximately satisfy SUs' rate requirements and remove
the integer constraints. For the second step, we prove that the general
optimization problem is reduced to a convex optimization task. Considering the
trade-off among fractions of each time slot, we focus on optimizing the time
slot structures of SUs that maximize the total throughput while guaranteeing
the rate requirements of both real-time and non-real-time SUs. Since the
reduced optimization problem does not have a simple closed-form solution, we
thus propose a near optimal closed-form solution by utilizing Lambert-W
function. We also exploit iterative gradient method based on Lagrangian dual
decomposition to achieve near optimal solutions. Simulation results are
presented to validate the optimality of the proposed schemes
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption
A promising technique to provide mobile applications with high computation
resources is to offload the processing task to the cloud. Utilizing the
abundant processing capabilities of the clouds, mobile edge computing enables
mobile devices with limited batteries to run resource hungry applications and
to save power. However, it is not always true that edge computing consumes less
power compared to device computing. It may take more power for the mobile
device to transmit a file to the cloud than running the task itself. This paper
investigates the power minimization problem for the mobile devices by data
offloading in multi-cell multi-user OFDMA mobile edge computing networks. We
consider the maximum acceptable delay as QoS metric to be satisfied in our
network. We formulate the problem as a mixed integer nonlinear problem which is
converted into a convex form using D.C. approximation. To solve the converted
optimization problem, we have proposed centralized and distributed algorithms
for joint power allocation and channel assignment together with
decision-making. Simulation results illustrate that by utilizing the proposed
algorithms, considerable power savings can be achieved, e.g., about 60 % for
large bit stream size compared to local computing baseline
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