4,901 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Network coding via evolutionary algorithms
Network coding (NC) is a relatively recent novel technique that generalises
network operation beyond traditional store-and-forward routing, allowing
intermediate nodes to combine independent data streams linearly. The rapid
integration of bandwidth-hungry applications such as video conferencing and HDTV
means that NC is a decisive future network technology.
NC is gaining popularity since it offers significant benefits, such as throughput
gain, robustness, adaptability and resilience. However, it does this at a potential
complexity cost in terms of both operational complexity and set-up complexity. This
is particularly true of network code construction.
Most NC problems related to these complexities are classified as non
deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to
solve them in polynomial time. This research concentrates on the multicast scenario,
particularly: (a) network code construction with optimum network and coding
resources; (b) optimising network coding resources; (c) optimising network security
with a cost criterion (to combat the unintentionally introduced Byzantine
modification security issue).
The proposed solution identifies minimal configurations for the source to deliver
its multicast traffic whilst allowing intermediate nodes only to perform forwarding
and coding. In the method, a preliminary process first provides unevaluated
individuals to a search space that it creates using two generic algorithms (augmenting
path and linear disjoint path. An initial population is then formed by randomly
picking individuals in the search space. Finally, the Multi-objective Genetic
algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches
search the population to identify minimal configurations. Genetic operators
(crossover, mutation) contribute to include optimum features (e.g. lower cost, lower
coding resources) into feasible minimal configurations. A fitness assignment and
individual evaluation process is performed to identify the feasible minimal
configurations. Simulations performed on randomly generated acyclic networks are used to
quantify the performance of MOGA and VEGA
PERFORMANCE OF OPTIMIZATION METHODS FOR ENERGY EFFICIENCY IN COOPERATIVE COMMUNICATION
In cooperative communication the effect of channel fading can be improved by cooperation between the user terminals and the relay nodes in wireless networks. In a Wireless Sensor Network (WSN), cooperative relaying improves the link quality with a relatively high Energy Efficiency Gain (EEG). In this paper, optimized parameters are used in WSN to enhance the EEG using particle swarm optimization (PSO) and Real-Coded Genetic Algorithm (RGA). Maximum enhancements of EEG obtained using RGA for M-ary Quadrature Amplitude Modulation (M-QAM) is 64% for M=16, 87% for M=32, and 97% for M=64 compared to EEG obtained without optimization. The superiority proposed optimization methods are verified by comparing with results without optimization and by comparing with the published results for Energy Efficiency (EE)
Device-to-device communications: a performance analysis in the context of social comparison-based relaying
Device-to-device (D2D) communications are recognized as a key enabler of future cellular networks which will help to drive improvements in spectral efficiency and assist with the offload of network traffic. Among the transmission modes of D2D communications are single-hop and relay assisted multi-hop transmission. Relay-assisted D2D communications will be essential when there is an extended distance between the source and destination or when the transmit power of D2D user equipments (UEs) is constrained below a certain level. Although a number of works on relay-assisted D2D communications have been presented in the literature, most of those assume that relay nodes cooperate unequivocally. In reality, this cannot be assumed since there is little incentive to cooperate without a guarantee of future reciprocal behavior. Cooperation is a social behavior that depends on various factors, such as peer comparison, incentives, the cost to the donor and the benefit to the recipient. To incorporate the social behavior of D2D relay nodes, we consider the decision to relay using the donation game based on social comparison and characterize the probability of cooperation in an evolutionary context. We then apply this within a stochastic geometric framework to evaluate the outage probability and transmission capacity of relay assisted D2D communications. Through numerical evaluations, we investigate the performance gap between the ideal case of 100% cooperation and practical scenarios with a lower cooperation probability. It shows that practical scenarios achieve lower transmission capacity and higher outage probability than idealistic network views which assume full cooperation. After a sufficient number of generations, however, the cooperation probability follows the natural rules of evolution and the transmission performance of practical scenarios approach that of the full cooperation case, indicating that all D2D relay nodes adopt the same dominant cooperative strategy based on social comparison, without the need for enforcement by an external authority
A unified design space of synthetic stripe-forming networks
Synthetic biology is a promising tool to study the function and properties of gene regulatory networks. Gene circuits with predefined behaviours have been successfully built and modelled, but largely on a case-by-case basis. Here we go beyond individual networks and explore both computationally and synthetically the design space of possible dynamical mechanisms for 3-node stripe-forming networks. First, we computationally test every possible 3-node network for stripe formation in a morphogen gradient. We discover four different dynamical mechanisms to form a stripe and identify the minimal network of each group. Next, with the help of newly established engineering criteria we build these four networks synthetically and show that they indeed operate with four fundamentally distinct mechanisms. Finally, this close match between theory and experiment allows us to infer and subsequently build a 2-node network that represents the archetype of the explored design space
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
Architecture of a cognitive non-line-of-sight backhaul for 5G outdoor urban small cells
Densely deployed small cell networks will address the growing demand for broadband mobile connectivity, by increasing access network capacity and coverage. However, most potential small cell base station (SCBS) locations do not have existing telecommunication infrastructure. Providing backhaul connectivity to core networks is therefore a challenge. Millimeter wave (mmW) technologies operated at 30-90GHz are currently being considered to provide low-cost, flexible, high-capacity and reliable backhaul solutions using existing roof-mounted backhaul aggregation sites. Using intelligent mmW radio devices and massive multiple-input multiple-output (MIMO), for enabling point-to-multipoint (PtMP) operation, is considered in this research. The core aim of this research is to develop an architecture of an intelligent non-line-sight (NLOS) small cell backhaul (SCB) system based on mmW and massive MIMO technologies, and supporting intelligent algorithms to facilitate reliable NLOS street-to-rooftop NLOS SCB connectivity. In the proposed architecture, diffraction points are used as signal anchor points between backhaul radio devices. In the new architecture the integration of these technologies is considered. This involves the design of efficient artificial intelligence algorithms to enable backhaul radio devices to autonomously select suitable NLOS propagation paths, find an optimal number of links that meet the backhaul performance requirements and determine an optimal number of diffractions points capable of covering predetermined SCB locations. Throughout the thesis, a number of algorithms are developed and simulated using the MATLAB application. This thesis mainly investigates three key issues: First, a novel intelligent NLOS SCB architecture, termed the cognitive NLOS SCB (CNSCB) system is proposed to enable street-to-rooftop NLOS connectivity using predetermined diffraction points located on roof edges. Second, an algorithm to enable the autonomous creation of multiple-paths, evaluate the performance of each link and determine an optimal number of possible paths per backhaul link is developed. Third, an algorithm to determine the optimal number of diffraction points that can cover an identified SCBS location is also developed. Also, another investigated issue related to the operation of the proposed architecture is its energy efficiency, and its performance is compared to that of a point-to-point (PtP) architecture. The proposed solutions were examined using analytical models, simulations and experimental work to determine the strength of the street-to-rooftop backhaul links and their ability to meet current and future SCB requirements. The results obtained showed that reliable multiple NLOS links can be achieved using device intelligence to guide radio signals along the propagation path. Furthermore, the PtMP architecture is found to be more energy efficient than the PtP architecture. The proposed architecture and algorithms offer a novel backhaul solution for outdoor urban small cells. Finally, this research shows that traditional techniques of addressing the demand for connectivity, which consisted of improving or evolving existing solutions, may nolonger be applicable in emerging communication technologies. There is therefore need to consider new ways of solving the emerging challenges
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