7,994 research outputs found
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Optimal Distributed Resource Allocation for Decode-and-Forward Relay Networks
This paper presents a distributed resource allocation algorithm to jointly
optimize the power allocation, channel allocation and relay selection for
decode-and-forward (DF) relay networks with a large number of sources, relays,
and destinations. The well-known dual decomposition technique cannot directly
be applied to resolve this problem, because the achievable data rate of DF
relaying is not strictly concave, and thus the local resource allocation
subproblem may have non-unique solutions. We resolve this non-strict concavity
problem by using the idea of the proximal point method, which adds quadratic
terms to make the objective function strictly concave. However, the proximal
solution adds an extra layer of iterations over typical duality based
approaches, which can significantly slow down the speed of convergence. To
address this key weakness, we devise a fast algorithm without the need for this
additional layer of iterations, which converges to the optimal solution. Our
algorithm only needs local information exchange, and can easily adapt to
variations of network size and topology. We prove that our distributed resource
allocation algorithm converges to the optimal solution. A channel resource
adjustment method is further developed to provide more channel resources to the
bottleneck links and realize traffic load balance. Numerical results are
provided to illustrate the benefits of our algorithm
Improvement of indoor VLC network downlink scheduling and resource allocation
Indoor visible light communications (VLC) combines illumination and communication by utilizing the high-modulation-speed of LEDs. VLC is anticipated to be complementary to radio frequency communications and an important part of next generation heterogeneous networks. In order to make the maximum use of VLC technology in a networking environment, we need to expand existing research from studies of traditional point-to-point links to encompass scheduling and resource allocation related to multi-user scenarios. This work aims to maximize the downlink throughput of an indoor VLC network, while taking both user fairness and time latency into consideration. Inter-user interference is eliminated by appropriately allocating LEDs to users with the aid of graph theory. A three-term priority factor model is derived and is shown to improve the throughput performance of the network scheduling scheme over those previously reported. Simulations of VLC downlink scheduling have been performed under proportional fairness scheduling principles where our newly formulated priority factor model has been applied. The downlink throughput is improved by 19.6% compared to previous two-term priority models, while achieving similar fairness and latency performance. When the number of users grows larger, the three-term priority model indicates an improvement in Fairness performance compared to two-term priority model scheduling
Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs
Radio waves carry both energy and information simultaneously. Nevertheless,
Radio-Frequency (RF) transmission of these quantities have traditionally been
treated separately. Currently, we are experiencing a paradigm shift in wireless
network design, namely unifying wireless transmission of information and power
so as to make the best use of the RF spectrum and radiations as well as the
network infrastructure for the dual purpose of communicating and energizing. In
this paper, we review and discuss recent progress on laying the foundations of
the envisioned dual purpose networks by establishing a signal theory and design
for Wireless Information and Power Transmission (WIPT) and identifying the
fundamental tradeoff between conveying information and power wirelessly. We
start with an overview of WIPT challenges and technologies, namely Simultaneous
Wireless Information and Power Transfer (SWIPT),Wirelessly Powered
Communication Network (WPCN), and Wirelessly Powered Backscatter Communication
(WPBC). We then characterize energy harvesters and show how WIPT signal and
system designs crucially revolve around the underlying energy harvester model.
To that end, we highlight three different energy harvester models, namely one
linear model and two nonlinear models, and show how WIPT designs differ for
each of them in single-user and multi-user deployments. Topics discussed
include rate-energy region characterization, transmitter and receiver
architecture, waveform design, modulation, beamforming and input distribution
optimizations, resource allocation, and RF spectrum use. We discuss and check
the validity of the different energy harvester models and the resulting signal
theory and design based on circuit simulations, prototyping and
experimentation. We also point out numerous directions that are promising for
future research.Comment: guest editor-authored tutorial paper submitted to IEEE JSAC special
issue on wireless transmission of information and powe
Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services
Metaverse encapsulates our expectations of the next-generation Internet,
while bringing new key performance indicators (KPIs). Although conventional
ultra-reliable and low-latency communications (URLLC) can satisfy objective
KPIs, it is difficult to provide a personalized immersive experience that is a
distinctive feature of the Metaverse. Since the quality of experience (QoE) can
be regarded as a comprehensive KPI, the URLLC is evolved towards the next
generation URLLC (xURLLC) with a personalized resource allocation scheme to
achieve higher QoE. To deploy Metaverse xURLLC services, we study the
interaction between the Metaverse service provider (MSP) and the network
infrastructure provider (InP), and provide an optimal contract design
framework. Specifically, the utility of the MSP, defined as a function of
Metaverse users' QoE, is to be maximized, while ensuring the incentives of the
InP. To model the QoE mathematically, we propose a novel metric named
Meta-Immersion that incorporates both the objective KPIs and subjective
feelings of Metaverse users. Furthermore, we develop an attention-aware
rendering capacity allocation scheme to improve QoE in xURLLC. Using a
user-object-attention level dataset, we validate that the xURLLC can achieve an
average of 20.1% QoE improvement compared to the conventional URLLC with a
uniform resource allocation scheme
Resource allocation and optimization techniques in wireless relay networks
Relay techniques have the potential to enhance capacity and coverage of a wireless network. Due to rapidly increasing number of smart phone subscribers and high demand for data intensive multimedia applications, the
useful radio spectrum is becoming a scarce resource. For this reason, two way relay network and cognitive radio technologies are required for better utilization of radio spectrum. Compared to the conventional one way relay
network, both the uplink and the downlink can be served simultaneously using a two way relay network. Hence the effective bandwidth efficiency is considered to be one time slot per transmission. Cognitive networks are wireless networks that consist of different types of users, a primary user (PU, the primary license holder of a spectrum band) and secondary users (SU, cognitive radios that opportunistically access the PU spectrum). The
secondary users can access the spectrum of the licensed user provided they do not harmfully affect to the primary user. In this thesis, various resource
allocation and optimization techniques have been investigated for wireless relay and cognitive radio networks
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