Evalutation of Turbo Decoder Performance Through Software Reference Model

Turbo encoder and decoder are two important blocks of long-term evolution (LTE) systems, as they address the data encoding and decoding in a communication system. In recent years, the wireless communication has advanced to suit the user needs. The power optimization can be achieved by proposing early termination of decoding iteration where the number of iterations is made adjustable which stops the decoding as it finishes the process. Clock gating technique is used at the RTL level to avoid the unnecessary clock given to sequential circuits; here clock supplies are a major source of power dissipation. The performance of a system is affected due to the numbers of parameters, including channel noise, type of decoding and encoding techniques, type of interleaver, number of iterations, and frame length on the Matlab Simulink platform. A software reference model for turbo encoder and decoder are modeled using MATLAB Simulink. Performance of the proposed model is estimated and analyzed on various parameters like frame length, number of iterations, and channel noise.

Adaptive Frequency domain Affine Projection Equalizer of MIMO SC-FDMA Based Overlap-Save Method

Design adaptive equalizer using Affine projection algorithm for MIMO SC-FDMA and compare it with MMSE . Both equalizers are used within overlap-save method. Also a turbo decoder is used in conjunction with overlap-save method to enhance the BER performances in 8x8 and 16x16 MIMO SC-FDMA.

Adaptive Frequency domain Affine Projection Equalizer of MIMO SC-FDMA Based Overlap-Save Method

Design adaptive equalizer using Affine projection algorithm for MIMO SC-FDMA and compare it with MMSE . Both equalizers are used within overlap-save method. Also a turbo decoder is used in conjunction with overlap-save method to enhance the BER performances in 8x8 and 16x16 MIMO SC-FDMA.

A Parallel Turbo Decoder Based on Recurrent Neural Network

A neural network-based decoder, based on a long short-term memory (LSTM) network, is proposed to solve the problem of high decoding delay caused by the poor parallelism of existing decoding algorithms for turbo codes. The powerful parallel computing and feature learning ability of neural networks can reduce the decoding delay of turbo codes and bit error rates simultaneously. The proposed decoder refers to a unique component coding concept of turbo codes. First, each component decoder is designed based on an LSTM network. Next, each layer of the component decoder is trained, and the trained weights are loaded into the turbo code decoding neural network as initialization parameters. Then, the turbo code decoding network is trained end-to-end. Finally, a complete turbo decoder is realized. Simulation results show that the performance of the proposed decoder is improved by 0.5–1.5 dB compared with the traditional serial decoding algorithm in Gaussian white noise and t-distribution noise. Furthermore, the results demonstrate that the proposed decoder can be used in communication systems with various turbo codes and that it solves the problem of high delay in serial iterative decoding.