scholarly journals Error Detection in Turbo Decoding using Neural Network

2019 ◽  
Vol 9 (1S) ◽  
pp. 218-221
Keyword(s):  
Neural Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Error Detection ◽  
Turbo Codes ◽  
Iterative Decoding ◽  
Turbo Decoding ◽  
Turbo Decoder ◽  
The Neural Network ◽  
Turbo Encoder

In this paper reduction of errors in turbo decoding is done using neural network. Turbo codes was one of the first thriving attempt for obtaining error correcting performance in the vicinity of the theoretical Shannon bound of –1.6 db. Parallel concatenated encoding and iterative decoding are the two techniques available for constructing turbo codes. Decrease in Eb/No necessary to get a desired bit-error rate (BER) is achieved for every iteration in turbo decoding. But the improvement in Eb/No decreases for each iteration. From the turbo encoder, the output is taken and this is added with noise, when transmitting through the channel. The noisy data is fed as an input to the neural network. The neural network is trained for getting the desired target. The desired target is the encoded data. The turbo decoder decodes the output of neural network. The neural network help to reduce the number of errors. Bit error rate of turbo decoder trained using neural network is less than the bit error rate of turbo decoder without training.

scholarly journals A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Johnny W. H. Kao ◽  
Stevan M. Berber ◽  
Abbas Bigdeli
Keyword(s):  
Neural Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Iterative Decoding ◽  
Viterbi Algorithm ◽  
Convolutional Code ◽  
Rate Transmission ◽  
Neural Network Structure ◽  
Data Rate Transmission ◽  
Novel Algorithm

A novel algorithm for decoding a general rate K/N convolutional code based on recurrent neural network (RNN) is described and analysed. The algorithm is introduced by outlining the mathematical models of the encoder and decoder. A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA). The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower decoding complexity. Most importantly this algorithm allows parallel signal processing, which increases the decoding speed and accommodates higher data rate transmission. These characteristics are inherited from a neural network structure of the decoder and the iterative nature of the algorithm, that outperform the conventional VA algorithm.

scholarly journals Threshold-Based Bit Error Rate for Stopping Iterative Turbo Decoding in a Varying SNR Environment

Frequenz ◽  
2017 ◽  
Vol 71 (1-2) ◽  
pp. 83-94 ◽  
Author(s):  
Roslina Mohamad ◽  
Harlisya Harun ◽  
Makhfudzah Mokhtar ◽  
Wan Azizun Wan Adnan ◽  
Kaharudin Dimyati
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Iterative Decoding ◽  
High Signal ◽  
Optimum Number ◽  
Turbo Decoding ◽  
Stopping Criterion ◽  
Stopping Criteria ◽  
Iteration Number ◽  
Number Of Iterations

Abstract Online bit error rate (BER) estimation (OBE) has been used as a stopping iterative turbo decoding criterion. However, the stopping criteria only work at high signal-to-noise ratios (SNRs), and fail to have early termination at low SNRs, which contributes to an additional iteration number and an increase in computational complexity. The failure of the stopping criteria is caused by the unsuitable BER threshold, which is obtained by estimating the expected BER performance at high SNRs, and this threshold does not indicate the correct termination according to convergence and non-convergence outputs (CNCO). Hence, in this paper, the threshold computation based on the BER of CNCO is proposed for an OBE stopping criterion (OBEsc). From the results, OBEsc is capable of terminating early in a varying SNR environment. The optimum number of iterations achieved by the OBEsc allows huge savings in decoding iteration number and decreasing the delay of turbo iterative decoding.

scholarly journals Performances of BICM-ID system using CRSC code in optical transmissions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hocine Fekih ◽  
Boubakar Seddik Bouazza ◽  
Keltoum Nouri
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Iterative Decoding ◽  
Optical Transmission ◽  
Coded Modulation ◽  
Modulation Scheme ◽  
Coding Gain ◽  
Decoding Algorithms ◽  
Qam Modulation ◽  
Short Code

AbstractRecently, using iterative decoding algorithms to achieve an interesting bit error rate for spectrally efficient modulation become a necessity for optical transmission, in this paper, we propose a coded modulation scheme based on bit interleaving circular recursive systematic convolutional (CRSC) code and 16-QAM modulation. The proposal system considered as a serial concatenation of a channel encoder, a bit interleaver and M-ary modulator can be flexible easy to implement using a short code length. For a spectral efficiency $\eta =3\text{bit}/s/Hz$, the coding gain at a bit error rate of 10−6 is about 8 dB.

scholarly journals Técnicas para la implementación de turbo decodificadores con baja latencia

2019 ◽  
pp. 29-41
Author(s):  
Lennin Conrado Yllescas-Calderon ◽  
Ramón Parra-Michel ◽  
Luis F Gonzalez-Pérez
Keyword(s):  
Channel Coding ◽  
Iterative Decoding ◽  
Turbo Coding ◽  
Processing Capacity ◽  
Turbo Decoding ◽  
Turbo Decoder ◽  
Transmission Rates ◽  
Communications Systems ◽  
Lte Advanced ◽  
Turbo Decoders

Turbo coding is a channel coding technique that increases the capacity of communications systems, especially wireless and mobile. Due to its high correction capability, this technique is used in modern wireless communication standards such as 3GPP and LTE/LTE-Advanced. One of the features of these systems is the increase in data processing capacity, where transmission rates of up to 1 Gbps are specified. However, the turbo coding technique inherently presents a limited performance as a consequence of the turbo decoding process at the reception stage. The turbo decoder presents a high operation latency mainly caused by the iterative decoding process, the interleaver and deinterleaver stage and the estimation process of the information bits. In this work, we show the techniques used to implement modern low-latency turbo decoders suitable for 3G and 4G standards.

Study of SEU effects in a Turbo Decoder Bit Error Rate

2009 ◽  
Author(s):  
M. Portela-Garcia ◽  
M. Garcia-Valderas ◽  
C. Lopez-Ongil ◽  
L. Entrena ◽  
B. Lestriez ◽  
...  
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Turbo Decoder

scholarly journals Fuzzy Based Adaptive Controller for on - Chip Communication

2018 ◽  
Vol 7 (3.27) ◽  
pp. 362
Author(s):  
M Jasmin ◽  
T Vigneswaran
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Error Detection ◽  
Input Data ◽  
Fuzzy Controller ◽  
Proper Time ◽  
System On Chip ◽  
Coding Scheme ◽  
Error Detection And Correction ◽  
On Chip

Occurrence of bit error is more when communication takes place in System on chip environment. By employing proper error detection and correction codes the bit error rate can be considerably reduced in On-chip communication. As System on chip involves heterogeneous system the efficiency of communication is improved when reconfigurable multiple coding schemes are preferred. Depending upon the requirements for various subsystem the correct code has to be selected. Due to the variations in input demands based on various subsystems the proper selection of codes become fuzzy in nature. In this paper Fuzzy Controller is designed to select the correct coding scheme. Inputs are given to the fuzzy controller based on the application demand of the user. The input parameters are minimum bit error rate, computational complexity and correlation level of the input data. Fuzzy Controller employs three membership functions and 27 rules to select the appropriate coding scheme. The selected coding scheme should be communicated at the proper time to the decoder. To enable the decoding process selected coding scheme is communicated effectively by using less overhead frame format. To verify the functionality of fuzzy controller random input data sets are used for testing.  

scholarly journals An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 31
Author(s):  
Qianwu Zhang ◽  
Zicong Wang ◽  
Shuaihang Duan ◽  
Bingyao Cao ◽  
Yating Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Decision Tree ◽  
Bit Error Rate ◽  
Error Rate ◽  
Forward Error Correction ◽  
Adaboost Algorithm ◽  
End To End ◽  
Learning By Using ◽  
Optical Transceivers

In this paper, an improved end-to-end autoencoder based on reinforcement learning by using Decision Tree for optical transceivers is proposed and experimentally demonstrated. Transmitters and receivers are considered as an asymmetrical autoencoder combining a deep neural network and the Adaboost algorithm. Experimental results show that 48 Gb/s with 7% hard-decision forward error correction (HD-FEC) threshold under 65 km standard single mode fiber (SSMF) is achieved with proposed scheme. Moreover, we further experimentally study the Tree depth and the number of Decision Tree, which are the two main factors affecting the bit error rate performance. Experimental research afterwards showed that the effect from the number of Decision Tree as 30 on bit error rate (BER) flattens out under 48 Gb/s for the fiber range from 25 km and 75 km SSMF, and the influence of Tree depth on BER appears to be a gentle point when Tree Depth is 5, which is defined as the optimal depth point for aforementioned fiber range. Compared to the autoencoder based on a Fully-Connected Neural Network, our algorithm uses addition operations instead of multiplication operations, which can reduce computational complexity from 108 to 107 in multiplication and 106 to 108 in addition on the training phase.

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