# Adaptive Optimization of Control Parameters for Feed-Forward Software Defined Equalization

Adaptive Optimization of Control Parameters for Feed-Forward Software Defined Equalization In this paper we briefly describe the design, implementation, and evaluation of a novel adaptive optimization approach for the feed-forward software defined equalization (FFSDE) method using the least mean squared (LMS) algorithm. In our design, we adaptively change the filter length (N) and step size ( $$\mu$$ μ ) to achieve the optimal bit error rate value. We used a vector signal generator RF PXI-5670 and a vector signal analyzer (VSA) RF PXI-5660 to test the validity of our approach. We implemented our method for the M-ary quadrature amplitude modulation (M-QAM) scheme in the VSA (which served as a receiver). The experimental results showed that we achieved high convergence speed and accuracy for rapidly changing transmitter channel characteristics. The automatic optimal setting feature of the LMS Algorithm parameters N and $$\mu$$ μ , enabled us to solve the hardware configuration problem for the FFSDE method. Determination of the LMS Algorithm training sequence size for the particular M-QAM allowed us to eliminate redundant data of the training sequence and increase the throughput. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

# Adaptive Optimization of Control Parameters for Feed-Forward Software Defined Equalization

, Volume 95 (4) – Feb 2, 2017
11 pages

Publisher
Springer US
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4036-3
Publisher site
See Article on Publisher Site

### Abstract

In this paper we briefly describe the design, implementation, and evaluation of a novel adaptive optimization approach for the feed-forward software defined equalization (FFSDE) method using the least mean squared (LMS) algorithm. In our design, we adaptively change the filter length (N) and step size ( $$\mu$$ μ ) to achieve the optimal bit error rate value. We used a vector signal generator RF PXI-5670 and a vector signal analyzer (VSA) RF PXI-5660 to test the validity of our approach. We implemented our method for the M-ary quadrature amplitude modulation (M-QAM) scheme in the VSA (which served as a receiver). The experimental results showed that we achieved high convergence speed and accuracy for rapidly changing transmitter channel characteristics. The automatic optimal setting feature of the LMS Algorithm parameters N and $$\mu$$ μ , enabled us to solve the hardware configuration problem for the FFSDE method. Determination of the LMS Algorithm training sequence size for the particular M-QAM allowed us to eliminate redundant data of the training sequence and increase the throughput.

### Journal

Wireless Personal CommunicationsSpringer Journals

Published: Feb 2, 2017

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