Optimization Subcarrier Allocation and Genetic Algorithm for Resource Allocation in MIMO-OFDMA

Utami, Amalia Rizqi; Iskandar, Iskandar

doi:10.1109/isesd.2018.8605480

Cited by 8 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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%.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Further, Multiple-input-multiple-output (MIMO) OFDMA system helps to improve the system capacity, transmission range reliability and suppress interfering signals. The channel throughput increases linearly with [3] at the cost of hardware complexity. It demands careful design of the resource allocation algorithm in MIMO OFDMA systems when employed for symmetrical services.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation algorithm for symmetrical services in OFDMA systems

Swapna

Pillai

2020

IJEECS

View full text Add to dashboard Cite

<p><span>The widespread acceptance of symmetrical services has urged for performance betterment techniques in wireless communication systems. In this paper, we propose an algorithm for resource allocation in MIMO-OFDMA system for applications that demand similar quality in uplink and downlink direction. The problem is formulated as multiobjective optimization problem with objectives to maximize the bidirectional data rates for individual users and to minimize the difference between the uplink and downlink data rate for each user. Fairness has been considered as a constraint in the optimization problem. The power allocation for each subcarrier in the OFDMA system is carried out using Linear Programming (LP) techniques, while the subcarrier allocation problem has been undertaken using an innovative multiobjective optimization technique that employs the concept of non-dominance in evolutionary algorithms. The results are extremely encouraging as they outperform the algorithms reported in literature using linear programming techniques or evolutionary algorithms solely. </span></p>

show abstract

Low Complexity Resource Allocation Techniques for Symmetrical Services in OFDMA Systems

Swapna¹,

Pillai²

2020

Wireless Pers Commun

View full text Add to dashboard Cite

Optimization Subcarrier Allocation and Genetic Algorithm for Resource Allocation in MIMO-OFDMA

Cited by 8 publications

References 4 publications

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

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

Resource allocation algorithm for symmetrical services in OFDMA systems

Low Complexity Resource Allocation Techniques for Symmetrical Services in OFDMA Systems

Contact Info

Product

Resources

About