Resource Allocation Analysis with Genetic Algorithm in LTE MIMO-OFDMA Cellular System

Utami, Amalia Rizqi; Iskandar, Iskandar

doi:10.1109/tssa48701.2019.8985502

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Dimana: 𝒇 : Fairness 𝒏 : Jumlah user 𝒙 : Jumlah kapasitas Semakin besar nilai fairness maka semakin adil pembagian RB terhadap user, dengan nilai maksimum 1 yang berarti semua user mendapatkan pembagian nilai kapasitas yang sama [8]. Nilai fairness dibutuhkan untuk mengatur ulang subcarrier di tengah-tengah pengguna untuk mendapatkan keuntungan paling adil dan kehilangan kapasitas paling sedikit di bawah kriteria desain algoritma [9].…”

Section: Perhitungan Kapasitas Dan Fairnessunclassified

Algoritma Greedy dan Round Robin untuk Alokasi Resource terhadap Optimasi Kinerja Sistem 4G LTE

Fitriyani,

Hasanah,

Utami

2023

elkom. jur. tekn. elekt. komp.

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Salah satu teknologi komunikasi yang berkembang pesat saat ini adalah Long Term Evolution (LTE) yang memiliki efisiensi tinggi, kapasitas besar, dan kemampuan menyediakan layanan informasi dengan data rate yang lebih tinggi. Permasalahan yang kerap dihadapi masyarakat ialah banyaknya pengguna yang mengakses layanan komunikasi namun resource allocation dan kapasitas pengguna jaringan kurang diperhatikan. Sehingga, diperlukan skema untuk membuat sistem lebih efisien dalam penggunaan kapasitas yang sesuai kebutuhan. Dalam penelitian ini dilakukan proses simulasi alokasi resource block (RB) pada arah downlink menggunakan dua algoritma berbeda dengan menguji performa kapasitas dan fairness sebagai parameter. Hasil yang diperoleh pada penelitian ini adalah kinerja algoritma Round Robin lebih signifikan dibandingkan algoritma Greedy untuk perhitungan kedua parameter, karena alokasi RB pada algoritma Round Robin dilakukan secara merata sehingga setiap user mendapatkan RB. Namun, proses alokasi RB menggunakan algoritma Greedy lebih efisien dan cepat karena algoritma Greedy hanya melakukan alokasi terhadap user dengan kanal terbaik.

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Section: Perhitungan Kapasitas Dan Fairnessunclassified

Algoritma Greedy dan Round Robin untuk Alokasi Resource terhadap Optimasi Kinerja Sistem 4G LTE

Fitriyani,

Hasanah,

Utami

2023

elkom. jur. tekn. elekt. komp.

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“…In (Ye et al, 2018), Deep Learning is used for signal detection and channel estimation OFDM structures. And in (Utami & Iskandar, 2019a) using Genetic Algorithm for Resource Allocation in OFDMA and using Genetic Algorithm to resource allocation Analysis in LTE MIMO-OFDMA Cellular System (Utami & Iskandar, 2019b).…”

Section: Wi-fi Scheduling With ML Basedmentioning

confidence: 99%

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

MASRİ¹,

ERDAL²

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Wireless mobile communications have experienced tremendous growth in the number of users, data rate requirements, and coverage in recent years. As the data rate and system throughput requirements increase, researchers and system designers need to develop efficient methods to meet these requirements with reasonable effort and cost. In this paper, we discuss an efficient approach to deal with diminishing the overhead of Downlink (DL) and feedback using Machine Learning (ML) for 802.11ax. In particular, the antennas that are related to the router were divided into two gatherings. We used good samples of Channel State Information (CSI) that were taken from an open-access dataset and used it to train our linear regression model. The first group of antennas was used as input to our model and the second group was used as the output of the model. In the online mode, we need to estimate only one group of antennas and for the second group, we can predict it using the trained linear regression model by using the estimated CSI group as input to the model. Therefore, the output of the model using as the CSI for the second group. In this way, we can reduce the overhead of the DL in the router as shown in the result table so the router will work more efficiently compared to the existing systems. From the results table in the last section, the average sum rate has increased between 20% and 30%.

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Resource allocation trends for ultra dense networks in 5G and beyond networks: A classification and comprehensive survey

Sharma

Kumar

2021

Physical Communication

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Resource Allocation Analysis with Genetic Algorithm in LTE MIMO-OFDMA Cellular System

Cited by 3 publications

References 1 publication

Algoritma Greedy dan Round Robin untuk Alokasi Resource terhadap Optimasi Kinerja Sistem 4G LTE

Algoritma Greedy dan Round Robin untuk Alokasi Resource terhadap Optimasi Kinerja Sistem 4G LTE

Developing A New Scheduling Algorithm for Wi-Fi 6 Technology Based on Machine Learning

Resource allocation trends for ultra dense networks in 5G and beyond networks: A classification and comprehensive survey

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