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Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm

Muhammad AsadUllah1,2, Muhammad Adnan Khan1, Sagheer Abbas1

  • 1Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan.

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This study introduces a fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm to improve Multiple-Input Multiple-Output (MIMO) systems. The novel FL-OLMPSO enhances channel estimation and data detection, leading to better communication quality.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Multiple-Input Multiple-Output (MIMO) technology enhances communication system capacity and quality.
  • Maximum Likelihood (ML) principle is used for data and channel estimation in MIMO systems.
  • Rayleigh fading channels present challenges for reliable signal transmission.

Purpose of the Study:

  • To propose and evaluate novel fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithms for MIMO systems.
  • To enhance the convergence rate and performance of channel estimation and data detection.
  • To introduce two variants: FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO).

Main Methods:

  • A three-level FL-OLMPSO algorithm is developed for channel estimation and data detection.
  • Continuous FL-OLMPSO is used for channel parameter estimation.
  • Discrete FL-OLMPSO is employed for transmitted symbol evaluation.
  • Fuzzy logic is integrated to update the velocity factor, improving convergence.

Main Results:

  • The proposed FL-TOLMPSO and FL-POLMPSO techniques demonstrate superior performance.
  • Significant improvements observed in Mean Mutual Channel Error (MMCE), Minimum Mean Square Error (MMSE), and Bit Error Rate (BER).
  • Results are favorably compared against conventional Opposite Learning Mutant PSO (OLMPSO) variants.

Conclusions:

  • The FL-OLMPSO algorithm effectively improves channel estimation and data detection in MIMO systems.
  • The proposed FL-TOLMPSO and FL-POLMPSO variants offer enhanced performance over existing methods.
  • This research contributes to advancing the efficiency and reliability of wireless communication systems.