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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hasan Raza & Ishtiaq Ahmad & Noor M. Khan & Waseem Abbasi & Muhammad Shahid Anwar & Sadique Ahmad & Mohammed A. El-Affendi, 2022. "Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms," Mathematics, MDPI, vol. 10(23), pages 1-11, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4600-:d:993835
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    References listed on IDEAS

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    1. Huafeng Xia & Feiyan Chen, 2020. "Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
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