Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications

TL;DR

This paper introduces a novel widely linear augmented extreme learning machine method for impairments compensation in satellite communications, significantly improving robustness and efficiency over traditional techniques.

Contribution

It develops a new CELM-based post-compensation scheme utilizing widely linear processing, offering enhanced performance and reduced computational complexity for satellite channel impairments.

Findings

CELM-WLLS achieves about 0.8 dB BER gain over traditional methods.

The proposed method reduces computational complexity by two-thirds.

Enhanced robustness in dynamic satellite channel conditions.

Abstract

Satellite communications are crucial for the evolution beyond fifth-generation networks. However, the dynamic nature of satellite channels and their inherent impairments present significant challenges. In this paper, a novel post-compensation scheme that combines the complex-valued extreme learning machine with augmented hidden layer (CELMAH) architecture and widely linear processing (WLP) is developed to address these issues by exploiting signal impropriety in satellite communications. Although CELMAH shares structural similarities with WLP, it employs a different core algorithm and does not fully exploit the signal impropriety. By incorporating WLP principles, we derive a tailored formulation suited to the network structure and propose the CELM augmented by widely linear least squares (CELM-WLLS) for post-distortion. The proposed approach offers enhanced communication robustness and is highly effective for satellite communication scenarios characterized by dynamic channel conditions and non-linear impairments. CELM-WLLS is designed to improve signal recovery performance and outperform traditional methods such as least square (LS) and minimum mean square error (MMSE). Compared to CELMAH, CELM-WLLS demonstrates approximately 0.8 dB gain in BER performance, and also achieves a two-thirds reduction in computational complexity, making it a more efficient solution.