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 (\(\mu\)) 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 \(\mu\), 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.