Evalutation of Turbo Decoder Performance Through Software Reference Model

Evalutation of Turbo Decoder Performance Through Software Reference Model

Manjunatha K. N., Raghu N., Kiran B.
Copyright: © 2022 |Pages: 19
DOI: 10.4018/978-1-7998-6988-7.ch011
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Abstract

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.
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Turbo Codes

There is a trade-off between energy and bandwidth efficiency (Saito, 2016) while designing a channel code. The codes with bigger redundancy or lower rate can usually rectify maximum number of errors. The communication system is expected to communicate long distance with minimum transmit power. It uses the smaller antennas to withstand more interference to communicate at higher data rates. This leads to correct more number of errors. The code energy becomes more efficient from these properties. The low rate codes consume maximum bandwidth with high overheads. The high computational requirements develop the decoding complexities as the code length increases. In channel coding always process of encoding is easy and decoding in difficult.

For every received noise power (N), bandwidth (W), channel type and signal power (S), the theoretical upper bound on the data rate is represented by R and only at this limit the transmitted data is error free.This limit is refers to Shannon capacity or channel capacity.

The mathematical model for the additive white Gaussian noise (AWGN) channels is

978-1-7998-6988-7.ch011.m01
(1)

An ideal error-free channel is very difficult to find in real time. The transmission of error-free data is estimated through probability of bit error to an arbitrary small constant. The Bit Error Rate (BER) or bit error probability (Vargas et al., 2015) used as standard of measure and is often chosen to be 10-5 or 10-6. Based on the Convolutional encoding, first turbo code was presented in 1993 by Berrou et al. To cover the block codes and convolutional (Shrestha & Paily, 2014) codes later the term “turbo codes” has been coined. Turbo codes are simply parallel concatenation of two codes isolated by an interleaver.

The below fig 1. explains the structure of a turbo encoder.

Figure 1.

General representation of turbo encoder

978-1-7998-6988-7.ch011.f01

The turbo encoder design concept follows the choice of the encoders and interleaver (Kim & Kim, 2013), and most of the designs obey the ideas represented below:

  • •

    Normally two identical encoders are used.

  • •

    The input bits are also appeared across the output also called as systematic form.

  • •

    The pseudo-random order of the bit is read through interleaver.

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