Cognitive Communications for Commercial Networked Earth Observing Fractionated Small Satellites

Periola, A. A.; Falowo, Olabisi E.

doi:10.1007/s11277-017-4513-8

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…The hidden layer encodes the unknown mathematical expression that links the input and output parameters in a problem under consideration. The unknown mathematical expression is derived using a training procedure (Periola and Falowo, 2017). During training, the input layer is assigned an input layer activation function, and it accepts pre-processed input parameters.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

Aikhuele¹,

Periola²,

Ighravwe³

2019

10.5267/j.ijdns

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The increased role of wind turbine systems makes it important for its operational states to be continuously monitored and optimized. This goal can be achieved using existing methods, which relies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10-3 , 4.2 ×10-3 , and 4.0 × 10-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

Aikhuele¹,

Periola²,

Ighravwe³

2019

10.5267/j.ijdns

View full text Add to dashboard Cite

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“…Besides, working mainly in time division mode with flexible network schemes, such satellite systems manage their wireless resources by rationally allocating time slots, transmit power as well as spectrum band to address the aforementioned concerns. Various efforts have been reported in the literature to improve both energy and spectrum efficiency [20] [21]. Resource allocation approaches that support dynamic spectrum and power allocation are likely to allow multibeam satellite systems to be more resource-efficient in complex network circumstances.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Trading for Satellite Communication Systems with Dynamic Bargaining

Lam

Zhao

et al. 2018

IEEE Trans. Commun.

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With the rapid development of modern satellite communications, broadband satellite services are experiencing a period of remarkable growth in both the number of users and the available bandwidth. More efficient spectrum management schemes require deeper investigation in order to meet the everincreasing demand for broadband spectrum. In this paper, we propose a band allocation method for multibeam satellite systems by introducing a market-driven pricing mechanism. Instead of adopting static and fixed band selling, we consider a satellite network operator that utilizes the mode of price bargaining to trade the unused band with terrestrial network operators. By applying market-based mechanism to support satellite spectrum allocation, higher spectrum efficiency can be attained in order for satellite systems to meet the increasing demands for satellite bandwidth at an affordable cost. Besides, for the one-tomany bargaining case without terrestrial operator involved in, a differential spectrum pricing solution is devised to address heterogeneous users' spectrum preferences. In a typical price bargaining model, market participants (i.e. terrestrial network operators) are assumed to know exactly their needs dynamically, which is hard to achieve in near real-time; thus, our approach approximates it with a sub-optimal estimation on the network operators' benefit threshold. To be specific, we obtain the optimal pricing at every round of bargaining by predicting the overall benefits of terrestrial network operators and reaching the Nash equilibrium. Essential discussions and proofs for the pricing rationality are provided. Numerical results are given to evaluate the impact of the pricing scheme on the profits of satellite systems.

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Hybrid wireless aided volunteer computing paradigm

Periola

Falowo

2020

Wireless Netw

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Cognitive Communications for Commercial Networked Earth Observing Fractionated Small Satellites

Cited by 4 publications

References 22 publications

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

Wind turbine systems operational state and reliability evaluation: An artificial neural network approach

Spectrum Trading for Satellite Communication Systems with Dynamic Bargaining

Hybrid wireless aided volunteer computing paradigm

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