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Innovative resource allocation mechanism for optimizing 5G multi user‐massive multiple input multiple output system 优化 5G 多用户-海量多输入多输出系统的创新资源分配机制

Internet Technology Letters Pub Date : 2024-08-10 DOI: 10.1002/itl2.569

P. Leela Rani, N. Devi, A. Guru Gokul

{"title":"Innovative resource allocation mechanism for optimizing 5G multi user‐massive multiple input multiple output system","authors":"P. Leela Rani, N. Devi, A. Guru Gokul","doi":"10.1002/itl2.569","DOIUrl":"https://doi.org/10.1002/itl2.569","url":null,"abstract":"5G networks are essential in all locations owing to the multitude of advantages they provide. As a result, the number of users has increased dramatically. Nevertheless, these users require a variety of resources in order to function efficiently. Deep learning techniques have been created to improve the precision and dependability of resource allocation in the context of 5G networks. This research utilizes an efficient recurrent neural network (ERNN) to handle resource allocation for 5G multiuser (MU)‐massive multiple input multiple output (MIMO). In order to optimize the objective functions, the first application of the multi‐objective differential evaluation algorithm (MODEA) is used. The neural network is provided with these updated goal functions in order to allocate resources. ERNN evaluates the level of need for each individual user. By partitioning the resource at this level, it maintains a high throughput while distributing it to each user. In addition, the fairness index of the resource distribution system based on neural networks is established. The suggested method achieves a data transfer rate of 290 bits per second (bps) and a fairness index of 0.97% when used by 50 users. The findings of the proposed method exhibit superior performance compared to other existing methods in the field of 5G massive MIMO.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"2 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Internet of Things security‐based E parking framework for smart city application using Lora 使用 Lora 为智慧城市应用开发基于安全的物联网电子停车框架

Internet Technology Letters Pub Date : 2024-08-08 DOI: 10.1002/itl2.566

S. K. Tripathy, G. Palai

{"title":"An Internet of Things security‐based E parking framework for smart city application using Lora","authors":"S. K. Tripathy, G. Palai","doi":"10.1002/itl2.566","DOIUrl":"https://doi.org/10.1002/itl2.566","url":null,"abstract":"Finding an accessible parking spot using 5G technology can be considered as time and fuel expenses. In this manner, it might make drivers disappointed in the parking zone. This will prompt awful traffic around the parking spot and may likewise prompt a mishap. That is the reason this task proposes a Smart Parking framework that utilizes cameras which will be associated with a Raspberry Pi and it will likewise have an Android application as an interface to help book or view accessible spaces. E Parking framework for security empowerment in 5G can be characterized as the utilization of trend setting innovations for the effective activity, checking, and the board of parking inside an urban versatility technique. This task will help tackle issues referenced by permitting clients to see and select accessible space in the parking, which will keep clients from driving around. You Only Look Once (YOLO) algorithm, Adaptive Background Learning and also pre‐trained Mask‐RCNN are used for finding the nearest free parking slot. Currently, Raspberry Pi will be utilized as the connection between the Cameras and the Server, by moving information gathered from the Raspberry Pi to an online server in order to process the information and empower the Android application to get outcome. In an end, this venture will help in decreasing the measure of time a driver needs to spend around the parking just to locate an accessible spot, lessening the measure of traffic, diminishing contamination, expanding the security using 5G technologies and furthermore better monetizing the parking spot. The proposed system detects vehicles in indoor as well as outdoor parking fields accurately.","PeriodicalId":509592,"journal":{"name":"Internet Technology Letters","volume":"6 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of data analysis models for low‐resource Eurasian languages using machine translation 利用机器翻译优化低资源欧亚语言的数据分析模型

Internet Technology Letters Pub Date : 2024-04-18 DOI: 10.1002/itl2.528

HongYan Chen, Kim Kyung Yee

引用次数: 0