637 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
Low-complexity antenna selection techniques for massive MIMO systems
PhD ThesisMassive Multiple-Input Multiple-Output (M-MIMO) is a state of the art technology
in wireless communications, where hundreds of antennas are exploited at the base
station (BS) to serve a much smaller number of users. Employing large antenna
arrays can improve the performance dramatically in terms of the achievable rates
and radiated energy, however, it comes at the price of increased cost, complexity,
and power consumption.
To reduce the hardware complexity and cost, while maintaining the advantages of
M-MIMO, antenna selection (AS) techniques can be applied where only a subset of
the available antennas at the BS are selected. Optimal AS can be obtained through
exhaustive search, which is suitable for conventional MIMO systems, but is prohibited
for systems with hundreds of antennas due to its enormous computational
complexity. Therefore, this thesis address the problem of designing low complexity
AS algorithms for multi-user (MU) M-MIMO systems.
In chapter 3, different evolutionary algorithms including bio-inspired, quantuminspired,
and heuristic methods are applied for AS in uplink MU M-MIMO systems.
It was demonstrated that quantum-inspired and heuristic methods outperform
the bio-inspired techniques in terms of both complexity and performance.
In chapter 4, a downlink MU M-MIMO scenario is considered with Matched Filter
(MF) precoding. Two novel AS algorithms are proposed where the antennas are
selected without any vector multiplications, which resulted in a dramatic complexity
reduction. The proposed algorithms outperform the case where all antennas are
activated, in terms of both energy and spectral efficiencies.
In chapter 5, three AS algorithms are designed and utilized to enhance the performance
of cell-edge users, alongside Max-Min power allocation control. The
algorithms aim to either maximize the channel gain, or minimize the interference
for the worst-case user only.
The proposed methods in this thesis are compared with other low complexity AS
schemes and showed a great performance-complexity trade-off
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
Cellular Underwater Wireless Optical CDMA Network: Potentials and Challenges
Underwater wireless optical communications is an emerging solution to the
expanding demand for broadband links in oceans and seas. In this paper, a
cellular underwater wireless optical code division multiple-access (UW-OCDMA)
network is proposed to provide broadband links for commercial and military
applications. The optical orthogonal codes (OOC) are employed as signature
codes of underwater mobile users. Fundamental key aspects of the network such
as its backhaul architecture, its potential applications and its design
challenges are presented. In particular, the proposed network is used as
infrastructure of centralized, decentralized and relay-assisted underwater
sensor networks for high-speed real-time monitoring. Furthermore, a promising
underwater localization and positioning scheme based on this cellular network
is presented. Finally, probable design challenges such as cell edge coverage,
blockage avoidance, power control and increasing the network capacity are
addressed.Comment: 11 pages, 10 figure
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological
behaviors of fish schooling in nature, viz., the preying, swarming, following
and random behaviors. Owing to a number of salient properties, which include
flexibility, fast convergence, and insensitivity to the initial parameter
settings, the family of AFSA has emerged as an effective Swarm Intelligence
(SI) methodology that has been widely applied to solve real-world optimization
problems. Since its introduction in 2002, many improved and hybrid AFSA models
have been developed to tackle continuous, binary, and combinatorial
optimization problems. This paper aims to present a concise review of the
family of AFSA, encompassing the original ASFA and its improvements,
continuous, binary, discrete, and hybrid models, as well as the associated
applications. A comprehensive survey on the AFSA from its introduction to 2012
can be found in [1]. As such, we focus on a total of {\color{blue}123} articles
published in high-quality journals since 2013. We also discuss possible AFSA
enhancements and highlight future research directions for the family of
AFSA-based models.Comment: 37 pages, 3 figure
A Review of the Applications of Quantum Machine Learning in Optical Communication Systems
In the context of optical signal processing, quantum and quantum-inspired
machine learning algorithms have massive potential for deployment. One of the
applications is in error correction protocols for the received noisy signals.
In some scenarios, non-linear and unknown errors can lead to noise that
bypasses linear error correction protocols that optical receivers generally
implement. In those cases, machine learning techniques are used to recover the
transmitted signal from the received signal through various estimation
procedures. Since quantum machine learning algorithms promise advantage over
classical algorithms, we expect that optical signal processing can benefit from
these advantages. In this review, we survey several proposed quantum and
quantum-inspired machine learning algorithms and their applicability with
current technology to optical signal processing.Comment: European Wireless Conference (EW) 2023 - 6G Driving a Sustainable
Growt
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
The upcoming 5th Generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated Artificial Intelligence (AI) operations. However, fully-intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the 6th Generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performanceandservicetypes.Theincreasinglystringentperformancerequirementsofemergingnetworks may finally trigger the deployment of some interesting new technologies such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications and cell-free communications – tonameafew.Ourvisionfor6Gis–amassivelyconnectedcomplexnetworkcapableofrapidlyresponding to the users’ service calls through real-time learning of the network state as described by the network-edge (e.g., base-station locations, cache contents, etc.), air interface (e.g., radio spectrum, propagation channel, etc.), and the user-side (e.g., battery-life, locations, etc.). The multi-state, multi-dimensional nature of the network state, requiring real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of Machine Learning (ML), Quantum Computing (QC), and Quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensivereviewoftherelatedstate-of-the-artinthedomainsofML(includingdeeplearning),QCand QML, and identify their potential benefits, issues and use cases for their applications in the B5G networks. Subsequently,weproposeanovelQC-assistedandQML-basedframeworkfor6Gcommunicationnetworks whilearticulatingitschallengesandpotentialenablingtechnologiesatthenetwork-infrastructure,networkedge, air interface and user-end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed
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