Performance analysis of the multiuser Shalvi-Weinstein algorithm

Autor(es)

Pavan, Flavio R. M. ^[1] ; Silva, Magno T. M. ^[1] ; Miranda, Maria D. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Escola Politecn, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Signal Processing; v. 163, p. 153-165, OCT 2019.
• Citações Web of Science: 0

Resumo

Performance analyses of blind adaptive equalizers provide valuable insights on their behavior, thus being fundamental in the design process. In the literature, interesting theoretical results to predict the steady-state performance of well-known constant-modulus-based algorithms were obtained under rather idealized equalization conditions. In practice, however, the communication channel is often noisy and perfect equalization can be unachievable. In this paper, we employ a linear regression model, commonly used in supervised adaptive filtering, to consider imperfect equalization conditions in the steady-state analysis of constant-modulus-based algorithms. Due to the nonlinear nature of blind algorithms, this analysis extension is a rather challenging, but rewarding, task. In particular, we apply the extended analysis model to the multiuser Shalvi-Weinstein algorithm, chosen due to its inherent advantages in adaptive blind equalization, namely high convergence rate and numerical robustness. As a result, we obtain a theoretical expression for its excess mean-square error (EMSE) at steady state. Interestingly enough, the resulting EMSE expression allows for the identification of distinct performance degradation causes, such as effects due to high-order statistics of the transmitted constellation, imperfect equalization, and multiuser cross-correlation penalty. In spite of the many assumptions considered throughout the EMSE expression derivation, simulation results validate it under realistic operating conditions. (C) 2019 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 17/20378-9 - Filtros adaptativos e aprendizagem de máquina: aplicações em imagens, comunicações e voz
• Beneficiário: Magno Teófilo Madeira da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Regular