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Overview of evolutionary algorithms and neural networks for modern mobile communication

dc.contributor.authorKouhalvandi, Lida
dc.contributor.authorShayea, Ibraheem
dc.contributor.authorÖzoguz, Serdar
dc.contributor.authorMohamad, Hafizal
dc.date.accessioned2026-01-24T22:39:55Z
dc.date.issued2022-06-27
dc.description.abstractAbstractThe sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high‐performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks. Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra‐lower latency, ultra‐security, high speed, and reliable connectivity. Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi‐objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks.
dc.description.urihttps://doi.org/10.1002/ett.4579
dc.description.urihttps://hdl.handle.net/11376/4410
dc.description.urihttps://aperta.ulakbim.gov.tr/record/259063
dc.identifier.doi10.1002/ett.4579
dc.identifier.eissn2161-3915
dc.identifier.issn2161-3915
dc.identifier.openairedoi_dedup___::2f135e7ad0ff30d286800c07a6950b7b
dc.identifier.orcid0000-0003-0693-4114
dc.identifier.orcid0000-0003-0957-4468
dc.identifier.urihttps://hdl.handle.net/11527/38763
dc.identifier.volume33
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofTransactions on Emerging Telecommunications Technologies
dc.rightsOPEN
dc.subjectJoint Optimization
dc.subjectMassive Mimo Systems
dc.subjectResource-Allocation
dc.subjectHarmony Search Algorithm
dc.subjectParticle Swarm Optimization
dc.subjectClosed-Loop Optimization
dc.subjectg Networks
dc.subjectWireless Sensor Networks
dc.subjectArtificial Bee Colony
dc.subjectSelf-Optimization
dc.titleOverview of evolutionary algorithms and neural networks for modern mobile communication
dc.typeArticle
dspace.entity.typePublication

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