EconPapers    
Economics at your fingertips  
 

Link-level performance abstraction for mimo receivers using artificial neural network

Asif Khan (), Alam Zaib (), Hazrat Ali () and Shahid Khattak ()
Additional contact information
Asif Khan: COMSATS University Islamabad
Alam Zaib: COMSATS University Islamabad
Hazrat Ali: COMSATS University Islamabad
Shahid Khattak: COMSATS University Islamabad

Telecommunication Systems: Modelling, Analysis, Design and Management, 2022, vol. 80, issue 4, No 7, 559-572

Abstract: Abstract This paper presents a novel framework for link-level performance abstraction for multiple input multiple output (MIMO) receivers using a neural network model. The link-level performance abstraction is widely used to predict the receiver performances through a lookup table (LUT). As opposed to the classical LUT-based techniques, in the proposed neural network-based approach, the dataset for different channels and receivers is generated from the link-level simulations in order to train the neural network. The output performance values for MIMO wireless system are defined in terms of various features extracted from the input received codewords, derived primarily from the received post-detection signal to noise ratio (SNR) values. The redundant features are removed before training the neural network. Finally, the neural network model is incorporated into the link-level simulation chain, replacing the receiver. The performance of the proposed framework is then evaluated for different channel conditions. Experimental results provide a good close link-level approximation for different receivers subjected to various modulation and coding schemes. We show that the neural network-based link-level performance abstraction outperforms the classical LUT-based link-level abstraction technique with exponential mapping function under various modulation and coding schemes.

Keywords: Multiple input multiple output; Iterative receiver; Artificial neural network; Mutual information; Minimum mean square error filter; Maximum likelihood receiver (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11235-022-00925-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:80:y:2022:i:4:d:10.1007_s11235-022-00925-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-022-00925-y

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:telsys:v:80:y:2022:i:4:d:10.1007_s11235-022-00925-y