The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction. INDEX TERMS Mobile multiuser communication, outage probability, performance prediction, GWO-BP neural network.

The medium access control (MAC) protocol design of underwater acoustic sensor networks (UWASNs) faces many challenges: the power limitation at nodes, long propagation delay, low data rates, etc. These challenges of underwater acoustic channels result in the unsuitable usage of terrestrial networks MAC protocol in UWASNs. Moreover, the long propagation delay causes a serious problem for the MAC protocol. In this paper, we propose a new MACA-based MAC protocol with delay tolerant (MACA-DT). It is shown that by using adaptive silent time and simultaneous handshake technique, MACA-DT protocol can improve the channel utilization and alleviate the long end-to-end delay. Simulation results show that our protocol can significantly improves the network throughput and decreases the end-to-end delay when compared with traditional MACA protocols

Underwater acoustic sensor networks (UWASNs) are effective tools for exploring and observing the ocean. Due to the nonnegligible physical restrictions of the underwater acoustic communication, most MAC protocols applied in the existing terrestrial wireless networks become inapplicable. In this paper, we propose a multiple handshaking MAC protocol for UWASNs called multihandshaking MAC (MHM). Using the method of multiple handshaking and competitive mechanism of control packets, our protocol is proposed to make the contending nodes share the underwater acoustic channel much more fairly and more efficiently. The main idea of MHM is to allow multiple nodes to transmit and receive data packets at the same time without packet collisions. We also propose a competitive mechanism of control packets, which can guarantee that there will not be data collisions in the process of multiple packet transmissions. Simulation results show that our protocol can achieve better performance, including throughput, delay, and fairness.

This paper starts from the research of computational advertising and scene theory, based on the background of artificial intelligence technology application, and studies the communication strategy of the computational advertising scene. It studies the three major strategies of computing advertising scene communication and deeply analyzes the principles and applications of scene insight strategy, content selection strategy, and community operation strategy. Results show the following: (1) under the background that the era of artificial intelligence has arrived, computing advertising uses artificial intelligence algorithms to complete breakthrough upgrades. Through the combination of algorithms and data, its intelligent upgrades are mainly manifested in three aspects: First, to achieve higher matching accuracy communication, the second is efficient customized communication, and the third is to realize the contextual interaction between advertisements and users; (2) the “intelligent” performance characteristics of intelligent scene dissemination of computing advertising are mainly in the application of scene intelligence technology, the intelligence of the data platform, and the intelligent construction of user portraits; (3) in the era of artificial intelligence, the communication strategies for computing advertising scenarios mainly include intelligent scenario insight strategies, content selection strategies, and community operation strategies. First, the intelligent scene insight strategy is mainly analyzed from two levels. On the one hand, it is a scene mining based on intelligent data and an in-depth analysis of the user tag system centered on intelligent algorithms. On the other hand, through the research on keyword search, the impact of intelligent upgrade on scenario insight is analyzed. Secondly, in terms of content selection strategy, the application of artificial intelligence technology has brought a new upgrade to the creation and recommendation mechanism of computational advertisements.

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