{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:21:22Z","timestamp":1753881682769,"version":"3.41.2"},"reference-count":25,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,3,15]]},"abstract":"<jats:p> Emergency communication is a key link to ensure the stable operation of communication networks. And large-scale multiple-input multiple-output (MIMO) technology can significantly improve coverage of communication systems. This work aims to optimize the backhaul process through deep neural network to improve the response speed and data transmission efficiency of base stations. Specifically, the proposed method uses deep neural network to represent the backhaul process of base stations, and explores data transmission under high efficiency and low delay. Simulation experiments are designed to verify performance advantages of the proposed method in large-scale MIMO wireless ad hoc networking scenarios, by using three comparison metrics. The experimental results show that the proposed method has a significant performance improvement in data transmission rate, system stability and resource utilization compared with traditional methods. Besides, the feasibility and effectiveness of the algorithm in practical application are verified by simulation and actual environment test. To sum up, the proposed algorithm can provide a new direction for the technical research in the field of emergency communication. <\/jats:p>","DOI":"10.1142\/s021812662550104x","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T12:20:39Z","timestamp":1727266839000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Neural Network-Based Backhaul Algorithm in Emergency Communication Base Stations Under Large-Scale MIMO Ad hoc Network Scenarios"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0343-8328","authenticated-orcid":false,"given":"Xiaoqing","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu 610065, P. R. 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