Optimization of Access Threshold for Cognitive Radio Networks with Prioritized Secondary Users

We propose an access control scheme in cognitive radio networks with prioritized Secondary Users (SUs). Considering the different types of data in the networks, the SU packets in the system are divided into SU1 packets with higher priority and SU2 packets with lower priority. In order to control the access of the SU2 packets (including the new arrival SU2 packets and the interrupted SU2 packets), a dynamic access threshold is set. By building a discrete-time queueing model and constructing a three-dimensional Markov chain with the number of the three types of packets in the system, we derive some performance measures of the two types of the SU packets. Then, with numerical results, we show the change trends for the different performance measures. At last, considering the tradeoff between the throughput and the average delay of the SU2 packets, we build a net benefit function to make optimization for the access threshold

Performance Analysis and Optimization for Cognitive Radio Networks with Classified Secondary Users and Impatient Packets

A cognitive radio network with classified Secondary Users (SUs) is considered. There are two types of SU packets, namely, SU1 packets and SU2 packets, in the system. The SU1 packets have higher priority than the SU2 packets. Considering the diversity of the SU packets and the real-time need of the interrupted SU packets, a novel spectrum allocation strategy with classified SUs and impatient packets is proposed. Based on the number of PU packets, SU1 packets, and SU2 packets in the system, by modeling the queue dynamics of the networks users as a three-dimensional discrete-time Markov chain, the transition probability matrix of the Markov chain is given. Then with the steady-state analysis, some important performance measures of the SU2 packets are derived to show the system performance with numerical results. Specially, in order to optimize the system actions of the SU2 packets, the individually optimal strategy and the socially optimal strategy for the SU2 packets are demonstrated. Finally, a pricing mechanism is provided to oblige the SU2 packets to follow the socially optimal strategy

The Influence of Network Public Opinion on Audit Credibility: A Dynamic Rumor Propagation Model Based on User Weight

Network public opinion is one of the factors that affects the credibility of audits, especially falsified network public opinion, which can easily result in the public losing trust in audits and may even impact the financial market. As users of social networks are not online 24 h a day, and their network behaviors are dynamic, in this study, we constructed a dynamic rumor-spreading model. Because the influence and authority of different user nodes in the network are different, we added user weights to the rumor propagation model, and finally, we established a dynamic rumor propagation model based on user weights. The experimental results showed that the rumor propagation model had a good monitoring effect, so it could help with managing the public opinion of audit institutions, maintaining the image of audit fairness and justice, and maintaining the stability of the capital market

Interpolation and Prediction of Spatiotemporal Data Based on XML Integrated with Grey Dynamic Model

Interpolation and prediction of spatiotemporal data are integral components of many real-world applications. Thus, approaches of interpolating and predicting spatiotemporal data have been extensively investigated. Currently, the grey dynamic model has been used to enhance the performance of interpolating and predicting spatiotemporal data. Meanwhile, the Extensible Markup Language (XML) has unique characteristics of information representation and exchange. In this paper, we first couple the grey dynamic model with the spatiotemporal XML model. Based on a definition of the position part of the spatiotemporal XML model, we extract the corresponding position information of each time interval and propose an algorithm for constructing an AVL tree to store them. Then, we present the architecture of an interpolating and predicting process and investigate change operations in positions. On this basis, we present an algorithm for interpolation and prediction of spatiotemporal data based on XML integrated with the grey dynamic model. Experimental results demonstrate the performance advantages of the proposed approach

Prediction of Enterprise Free Cash Flow Based on a Backpropagation Neural Network Model of the Improved Genetic Algorithm

Enterprises with good long-term free cash flow data often have better prospects than enterprises with good net profit but unstable free cash flow for a long time, and free cash flow prediction is an important part of evaluating the enterprise value of an enterprise. By determining the fitness function, algorithm formula, population, and Backpropagation (BP) neural network design, a BP neural network model based on the improved genetic algorithm is proposed to predict the free cash flow of enterprises. Taking the free cash flow data of G Company from 1 January 2019 to 30 June 2019 as an example, after evaluating the most neurons and the best population, analyzing the relative errors and comparing the average relative errors of different prediction models, the results show that the model has better prediction accuracy. Cash flow forecasting can effectively improve decision making on productions and operations and the investment financing of enterprises, and has important practical significance for studying enterprise fund management