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Jointly optimal rate control and total transmission power for cooperative cognitive radio system

IET COMMUNICATIONS 2017年第11期11卷 1679-1688页

作者： Soleimanpour-moghadam, Mohadese Talebi, Siamak Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

In this study, the authors apply the multi-objective optimisation (MOO) methods to the challenge posed by joint rate maximisation and total transmission power (TTP) minimisation in cooperative cognitive radio networks. The proposed MOO methods which are based on amplify and forward relaying strategy optimise the two conflicting objectives and, at same time, they maximise the rate quality and minimise the TTP allocated to the network relays simultaneously. The MOO problem under investigation is a non-convex non-linear combinatorial optimisation one that three MOO methods are presented for a desired solution. The explored methods are: weighted sum algorithm (which is based on a simplistic model), MOO fractional programming method (which has much in common with their MOO problem) and lexicographic algorithm (which is suitably adapted to complex combinatorial optimisation environments due to its robustness in avoiding trapping in local optima). Their simulation results confirm the proposed methods' effectiveness for simultaneous rate maximisation and TTP minimisation as well as their superiority over counterpart algorithms.

关键词： cooperative communication cognitive radio minimisation optimal control amplify and forward communication relay networks (telecommunication) concave programming nonlinear programming optimal rate control total transmission power multiobjective optimisation methods joint rate maximisation and total transmission power minimisation TTP minimisation cooperative cognitive radio networks amplify and forward relaying strategy rate quality maximisation nonconvex nonlinear combinatorial optimisation weighted sum algorithm MO fractional programming method lexicographic algorithm MOO problem

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System Reliability Estimation of Divert Attitude Control System of a Launch Vehicle using Bayesian Networks

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DEFENCE SCIENCE JOURNAL 2020年第1期70卷 90-94页

作者： Muthukumar, S. Bose, P. Subhash Chandra Srivardhan, R. A. DRDO Res Ctr Imarat Hyderabad 500069 India Natl Inst Technol Mech Engn Dept Warangal 506004 Andhra Pradesh India DRDO Def Res & Dev Lab Hyderabad 500058 India

Divert attitude and control system (DACS) is a one-shot system and provides attitude correction and translation of the Launch vehicle. DACS consists of many flight critical sub systems which are arranged in a series configuration. The traditional Reliability block diagram and Fault tree diagram methods are unsuitable for reliability modelling, when considering uncertainty among the components and system. Bayesian network is the natural choice to model dependencies among the components and system. DACS being one shot system, it is very expensive and time consuming to test more number of systems during the design and development. Hence the data is drawn from component level, subsystem level and expert opinion is used for reliability estimation. In this paper, Bayesian network modelling of DAC system was carried out for estimating the reliability using multi-level data. An algorithm is developed for computation of Conditional probabilities in Bayesian network. Posterior probability distribution of components is calculated using Markov Chain Monte Carlo (MCMC) simulations and results are compared with Junction tree based exact inference algorithm. MATLAB code is developed to estimate the reliability of DAC system.

关键词： Propulsion system Reliability Bayesian networks MCMC weighted sum algorithm Conditional probability

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Gradient-based multi-objective optimization with applications to waterflooding optimization

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COMPUTATIONAL GEOSCIENCES 2016年第3期20卷 677-693页

作者： Liu, Xin Reynolds, Albert C. Univ Tulsa Tulsa OK 74104 USA

We consider problems where it is desirable to maximize multiple objective functions, but it is impossible to find a single design vector (vector of optimization variables) which maximizes all objective functions. In this case, the solution of the multi-objective optimization problem is defined as the Pareto front. The defining characteristic of the Pareto front is that, given any specific point on the Pareto front, it is impossible to find another point on the Pareto front or another feasible point which yields a greater value of all objective functions. The focus of this work is on the generation of the Pareto front for bi-objective optimization problems with specific applications to waterflooding optimization. The most straightforward way to obtain the Pareto front is by application of the weighted sum method. We provide a procedure for scaling the optimization problem which makes it more straightforward to obtain points which are approximately uniformly distributed on the Pareto front when applying the weighted sum method. We also compare the performance of implementations of the weighted sum and normal boundary intersection (NBI) procedures where, with both methodologies, a gradient-based algorithm is used for optimization. The vector of objective functions maps the set of feasible design vectors onto a set Z, and it is well known that all points on the Pareto front are on the boundary of Z. The weighted sum method cannot find points which are on the concave part of the boundary of Z, whereas the NBI method can be used to find all points on the boundary of Z, even though all points on this boundary may not correspond to Pareto optimal points. We develop and implement an NBI algorithm based on the augmented Lagrange method where the maximization of the augumented Lagrangian in the inner loop of the augmented Lagrange method is accomplished by a gradient-based optimization algorithm with the necessary gradients computed by the adjoint method. Two waterflooding opt

关键词： Multi-objective optimization Life-cycle production optimization Waterflooding optimization weighted sum algorithm Normal boundary intersection algorithm Robust optimization Optimization under geological uncertainty

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Energy-Aware Enterprise Femtocell Deployment

Energy-Aware Enterprise Femtocell Deployment

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Lin, Michael La Porta, Thomas Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA

ISBN: (纸本)9781479930838

Large-scale enterprise femtocell deployments can significantly impact the performance and energy consumption of the underlying wireless networks that support them. Naive femtocell deployments can lead to higher overall network energy usage, while optimized femtocell deployments can increase total network connectivity and reduce macrocell energy consumption. This paper approaches femtocell deployment as a combinatorial optimization problem. We first consider accelerated greedy algorithms using one of two metrics: femtocell coverage and area spectral efficiency. Then, motivated by an analysis of the strengths and weaknesses of each metric, we introduce an algorithm that takes the weighted sum of both metrics. We evaluate our algorithms using extensive simulations, and find that our weighted sum algorithm decreases outage probability by up to 30% relative to existing greedy approaches. Furthermore, our algorithm can lead to a reduction in total network energy usage by up to 14%.

关键词： combinatorial mathematics femtocellular radio greedy algorithms optimisation probability radio networks spectral analysis telecommunication network reliability telecommunication power management area spectral efficiency combinatorial optimization problem energy-aware enterprise Naive femtocell deployment extensive simulation femtocell coverage greedy algorithm macrocell energy consumption. reduce network connectivity network energy usage reduction outage probability decrement weighted sum algorithm wireless network energy consumption Acceleration Base stations Greedy algorithms Interference Macrocell networks Power demand Signal to noise ratio

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Voltage Security-Constrained Optimal Generation Rescheduling for Available Transfer Capacity Enhancement in Deregulated Electricity Markets

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ENERGIES 2019年第22期12卷 4371-4371页

作者： Adewuyi, Oludamilare Bode Ahmadi, Mikaeel Olaniyi, Isaiah Opeyemi Senjyu, Tomonobu Olowu, Temitayo Olayemi Mandal, Paras Univ Ryukyus Grad Sch Sci & Engn Nishihara Okinawa 9030213 Japan Ladoke Akintola Univ Technol Fac Engn PMB 4000 Ogbomosho 210214 Nigeria Obafemi Awolowo Univ Dept Elect & Elect Engn Ife 220282 Nigeria Univ Texas El Paso Dept Elect & Comp Engn El Paso TX 79968 USA

Modern utilities are forced to operate very close to their loadable limits (maximum capacity) due to geographical, economical and some technical reasons. The deregulation of the power industry, the competitive nature of modern electricity markets and the continuous quest for modernization of cities and hamlets all over the world has also led to fast increase in the load demand. The stability of power systems all over the world are threatened with recurrent occurrences of voltage stability issues. Hence, Inter-zonal energy transactions between willing supplier and buyers need to be done with adequate consideration for power system security. In this work, a voltage security-constrained optimal generator active and reactive power rescheduling is carried out using the IEEE 30 and IEEE 57 bus systems. The simultaneous maximization of available transfer capacity (ATC) and voltage stability margin (VSM), using the weighted sum approach, is the objective function. Credible optimal power flow and power system security constraints are considered. Three variants of particle swarm optimization in MATLAB (R) are used in this work for analyzing the results for objectivity. The technical and economic benefits of the optimal generator rescheduling on the system's ATC, VSM, line losses, line flow and voltage profile are adequately analyzed.

关键词： deregulated electricity market available transfer capacity critical boundary index voltage security-constrained OPF weighted sum algorithm particle swarm optimization

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