A new method for searching the optimal step size of NLMS algorithm in intelligent antennas arrays based on fuzzy logic and artificial neural networks

Orozco-Tupacyupanqui, W.; Pérez-Meana, Héctor; Nakano-Miyatake, Mariko

doi:10.1109/concapan.2014.7000421

2014 IEEE Central America and Panama Convention (CONCAPAN XXXIV) 2014

DOI: 10.1109/concapan.2014.7000421

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A new method for searching the optimal step size of NLMS algorithm in intelligent antennas arrays based on fuzzy logic and artificial neural networks

W. Orozco-Tupacyupanqui

Héctor Pérez-Meana

Mariko Nakano-Miyatake

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2024

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A Fuzzy Based High-Performance DecisionMaking Model for Signal Detection in Smart Antenna Through Preference Leveled Evaluation Functions

Khanum,

Gunasekaran,

et al. 2024

Intelligent Technologies for Research and Engineering

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In a densely populated area with many users, adding a new wireless access point may not necessarily improve Wi-Fi performance. There are times when students must deal with poor download rates even with Access Points (AP) in every classroom. Cochannel interference is the root cause of several typical Wi-Fi issues. A discussion may be compared to Wi-Fi communication. The capacity to communicate and listen properly are both essential for effective communication. When two speakers are speaking in a similar tone, the conversational uncertainty is exacerbated. Wi-Fi broadcasts are the same way. The interference and drag performance might be worsened by two or more nearby APs using the same channel. This study suggests a smart antenna technology. When a smart antenna AP finds a nearby AP signal, it will automatically alter its pattern to minimise interference and provide quick and reliable transmission. The same principle applies when we cup our hands over our lips or ears to enable us to yell or listen more clearly. There are a lot of false positives in the typical approaches for WLAN node signal recognition. The optimal signal for a WLAN node is therefore identified using this study's proposed BPNN model, which uses the PFMDMM system for signal classification. This Decision-Making Model Using Parameterized Fuzzy Measures has been shown via experiments. A WLAN node's optimal signal may be more accurately predicted using a decision-making model based on preference-leveled evaluation functions. The precision of the signal identification and the anticipated findings were found to be almost identical to those obtained from real ground measurements. The test team mimicked cochannel interference, which would occur in a setting with plenty of APs, such as a workplace, hotel, or airport. The suggested smart antenna AP regularly outperformed other apps by an average of 75% greater coverage and unmatched performance.

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A Fuzzy Based High-Performance DecisionMaking Model for Signal Detection in Smart Antenna Through Preference Leveled Evaluation Functions

Khanum,

Gunasekaran,

et al. 2024

Intelligent Technologies for Research and Engineering

View full text Add to dashboard Cite

show abstract

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A new method for searching the optimal step size of NLMS algorithm in intelligent antennas arrays based on fuzzy logic and artificial neural networks

Cited by 1 publication

References 9 publications

A Fuzzy Based High-Performance DecisionMaking Model for Signal Detection in Smart Antenna Through Preference Leveled Evaluation Functions

A Fuzzy Based High-Performance DecisionMaking Model for Signal Detection in Smart Antenna Through Preference Leveled Evaluation Functions

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