Abstract
Adaptive equalization algorithms play a pivotal role in suppressing inter-symbol interference in wireless channels. Contemporarily, with the rapid development of science and technology, there is still a lack of unified cognition for adaptive equalization algorithms. Therefore, this study systematically discusses the research status and development process of adaptive equalization algorithms, focusing on the least mean square algorithm (LMS), constant modulus blind equalization algorithm (CMA) and neural network algorithm. Subsequently, based on Matlab simulation, their performance is analyzed visually. Finally, a table is listed to compare the three commonly used algorithms. From the aspects of practicability and application environment, it deeply analyzes the limitations of traditional adaptive equalization algorithms such as LMS and CMA in the current era, and demonstrates the superior performance of neural networks. On this basis, this paper emphasizes the powerful learning ability of neural networks and the opportunities for future research, which will lay the foundation for the development of next-generation communication networks.
Publisher
Darcy & Roy Press Co. Ltd.
Reference20 articles.
1. Pandey A, Malviya L D, Sharma V. Comparative study of LMS and NLMS algorithms in adaptive equalizer. International Journal of Engineering Research and Applications (IJERA), 2012, 2(3): 1584-1587.
2. Malik G, Sappal A S. Adaptive equalization algorithms: an overview. International journal of advanced computer science and applications, 2011, 2(3).
3. Razavi B. The decision-feedback equalizer [A Circuit for All Seasons]. IEEE Solid-State Circuits Magazine, 2017, 9(4): 13-132.
4. Wang B, Qiu X. Adaptive Equalizer and Its Development Trends. Journal of Instruments and Meters, 2005 (z2): 426-428.
5. Falconer D, Ljung L. Application of fast Kalman estimation to adaptive equalization. IEEE Transactions on Communications, 1978, 26(10): 1439-1446.