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Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex

Maha Abdelkhalek1, Souheib Ben Amor1,2, Sofiène Affes1

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Summary

A new data-aided joint angle and delay (JADE) estimator, GWOEIS, improves Gray Wolf Optimization (GWO) using importance sampling (IS) for better accuracy and speed in wireless channel estimation.

Keywords:
data-aided (DA)gray wolf optimization (GWO)importance sampling (IS)joint angle and delay estimation (JADE)maximum likelihood (ML)multi-carriermulti-pathorthogonal frequency division multiplex (OFDM)single-input multiple-output (SIMO)

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Area of Science:

  • Signal Processing
  • Wireless Communications
  • Optimization Algorithms

Background:

  • Accurate estimation of angles of arrival (AoAs) and time delays (TDs) is crucial for wireless communication systems.
  • Traditional methods like Gray Wolf Optimization (GWO) can be inefficient due to random initialization and slow convergence.
  • Orthogonal Frequency Division Multiplex (OFDM) systems with single-input multiple-output (SIMO) channels face challenges in multi-path environments.

Purpose of the Study:

  • To propose a novel data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator.
  • To enhance the Gray Wolf Optimization (GWO) algorithm by integrating the importance sampling (IS) concept, creating the GWOEIS approach.
  • To improve estimation accuracy, resolution capabilities, and convergence speed in OFDM-SIMO channels.

Main Methods:

  • Developed GWOEIS by embedding importance sampling (IS) into the Gray Wolf Optimization (GWO) algorithm.
  • Modified and dynamically updated the GWO convergence factor using cumulative distribution functions (CDFs) derived from IS.
  • Utilized a simplified importance function for reliable initial estimates, enhancing search efficiency.

Main Results:

  • GWOEIS demonstrates global optimality and superior resolution capabilities compared to traditional GWO.
  • The proposed method achieves faster convergence by providing reliable initial estimates.
  • Simulations confirm significant improvements in accuracy and speed, with GWOEIS reaching the Cramér-Rao lower bound (CRLB) even at low signal-to-noise ratios (SNRs).

Conclusions:

  • GWOEIS offers a substantial improvement over conventional GWO for JADE estimation.
  • The integration of IS significantly enhances the efficiency and performance of the GWO algorithm.
  • The new estimator provides near-optimal performance in challenging wireless channel conditions.