Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms

Hasan Raza, Ishtiaq Ahmad, Noor M. Khan, Waseem Abbasi (), Muhammad Shahid Anwar (), Sadique Ahmad and Mohammed A. El-Affendi
Additional contact information
Hasan Raza: Department of Electrical Engineering, Hamdard University, Islamabad 44000, Pakistan
Ishtiaq Ahmad: Department of Electrical Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan
Noor M. Khan: Department of Electrical Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan
Waseem Abbasi: Department of Electrical Engineering, MY University, Islamabad 46000, Pakistan
Muhammad Shahid Anwar: Department of AI Software, Gachon University, Seongnam-si 13120, Republic of Korea
Sadique Ahmad: EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Mohammed A. El-Affendi: EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

Mathematics, 2022, vol. 10, issue 23, 1-11

Abstract: The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed framework used to efficiently run the adaptive filtering algorithms having high computational cost. In this paper, a communication load-balancing procedure is introduced to validate the PDASP architecture using low-cost wireless sensor nodes. The PDASP architecture with the implementation of a multiple-input multiple-output (MIMO) based Recursive Least Square (RLS) algorithm is deployed on the processing-inefficient low-cost wireless sensor nodes to validate the performance of the PDASP architecture in terms of computational cost, processing time, and memory utilization. Furthermore, the processing time and memory utilization provided by the PDASP architecture are compared with sequentially operated RLS-based MIMO channel estimator on 2 × 2 , 3 × 3 , and 4 × 4 MIMO communication systems. The measurement results show that the sequentially operated MIMO RLS algorithm based on 3 × 3 and 4 × 4 MIMO communication systems is unable to work on a single unit; however, these MIMO systems can efficiently be run on the PDASP architecture with reduced memory utilization and processing time.

Keywords: distributed MIMO channel estimation; low computational complexity; parallel processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/23/4600/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/23/4600/ (text/html)

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:gam:jmathe:v:10:y:2022:i:23:p:4600-:d:993835

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().