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

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.