Particle Swarm Optimization-Based Parameter Estimation for the Compound Wireless THz Channel Model

The compound terahertz (THz) channel model has been widely adopted and recognized as a potent tool to capture the unique behaviors of wireless THz channels and analyze various performance of communication systems operating over THz spectra. However, accurate parameter estimation for the compound THz channel model still remains an open problem. In this letter, we propose to employ particle swarm optimization (PSO) to enable accurate yet efficient channel parameter estimation for the compound THz channel model. Based on the maximum likelihood (ML) estimation criterion, simulation results illustrate that the proposed PSO based parameter estimation technique is capable of achieving near-optimal estimation outcomes and possesses high robustness across multiple application scenarios. The proposed PSO based parameter estimation technique provides an easy-to-adapt mechanism for balancing the trade-off between estimation accuracy and computational efficiency.