{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:59:17Z","timestamp":1740142757662,"version":"3.37.3"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. Wireless heterogeneous networks are created by combining several radio access technologies, each with its own potentials, capabilities and limitations. In these networks, providing real-time services with quality assurance is essential. For effective use of radio resources, the Radio Resource Management method was introduced which its performance and efficiency is better than the control of independent radio resources in any radio access technology. In this paper, we introduced a novel approach to select the most effective radio access technologies by taking into account some performance parameters like the type of service, users\u2019 distribution pattern and the cost of the services. It also optimizes the handover relations between macrolayer and small cells. The proposed approach is a self-optimizing model can be employed to control resources and improve performance indices associated with mobile networks without human interference by only relying on network intelligence. In order to maximize the network performance, we applied the dynamic backhauling technique to analyze the uplink signaling data which increased the validity level of the decision-making process. Based on the extracted semantic information, the network decision-making engine is able to adjust the network parameters and efficiently allocate the resources. The numerical results exhibit considerable power saving for different traffic models in addition to reduce the rate of vertical handovers. The results also show increase the network throughput by up to 30%.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab149","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T03:11:46Z","timestamp":1631848306000},"page":"128-143","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Approach to Adaptive Resource Allocation for Energy Saving in Reconfigurable Heterogeneous Networks"],"prefix":"10.1093","volume":"66","author":[{"given":"Mohammad","family":"Nabipour","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Islamic Azad University , Shahr-E-Ray Branch, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4048-0833","authenticated-orcid":false,"given":"Amir Reza","family":"Momen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Islamic Azad University , Shahr-E-Ray Branch, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"issue":"1","key":"2023011814190717400_ref1","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1109\/MWC.2019.1900027","article-title":"Deep learning for physical-layer 5G wireless techniques: Opportunities, challenges and solutions","volume":"27","author":"Huang","year":"2019","journal-title":"IEEE Wireless Commun."},{"issue":"6","key":"2023011814190717400_ref2","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1093\/comjnl\/bxaa192","article-title":"A proposed framework for cloud-aware multimodal multimedia big data analysis toward optimal resource allocation","volume":"64","author":"Sasikala","year":"2021","journal-title":"Comp. 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