| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Escola Politecn, Sao Paulo - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | Signal Processing; v. 163, p. 153-165, OCT 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
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) | |
| FAPESP's process: | 17/20378-9 - Adaptive filters and machine learning: applications on image, communications, and speech |
| Grantee: | Magno Teófilo Madeira da Silva |
| Support Opportunities: | Regular Research Grants |