An efficient sliding window algorithm using adaptive-length guard window for turbo decoders

Lim, Hyuntack; Kim, Yongsang; Cheun, Kyungwhoon

doi:10.1109/jcn.2012.6253068

Cited by 9 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decoding algorithm is based on log-MAP algorithm and the algorithm is implemented using single-precision floating-point arithmetic on the NOC. The sliding-window algorithm described in [2] is improved in order to reduce the overhead communication between the micro-processors on the NOC. The architecture of the log-MAP decoder is shown as In Fig 2, D1 and D2 stand for the two soft-input softoutput decoders.…”

Section: Turbo Decodermentioning

confidence: 99%

“…Recently, the research has been focusing on how to reduce the complexity and latency resulted from MAP decoding (also known as the BCJR algorithm). In order to relax the high demanding on memory during decoding, sliding window algorithm is invented to separate the long data block into short sub-blocks [2]. By adopting appropriate early stopping criteria [3], the number of iteration is reduced, so is the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Parallelized Implementation of Turbo Decoding Based on Network on Chip Multi - core Processor

ZHANG¹,

HU²,

Chen³

2014

JESTR

View full text Add to dashboard Cite

With the evolution of wireless communication systems, it is increasingly difficult for Application Specific Integrated Circuit (ASIC) solutions to meet the daily changing requirements. A network on chip (NOC) multi-core processor based on message-passing programming model is designed to implement the LTE-A turbo decoder in a parallel mode using pure Software Defined Radio (SDR) approach. The NOC is well balanced between the hardware and software design with a high degree of programmability and re-configurability. According to the features of the NOC multi-core processor, the implementation of turbo decoder is optimized to reduce the computational complexity and to increase the parallelization. Several aspects of turbo decoder are investigated in software radio approach rather than hardware. Compared with the results of the software simulation and the Field Programmable Gate Array (FPGA) demonstration, the NOC multicore processor is flexible to realize the proposed turbo decoding algorithm. In addition, our solution has comparable performance with other published ones.

show abstract

Section: Turbo Decodermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Parallelized Implementation of Turbo Decoding Based on Network on Chip Multi - core Processor

ZHANG¹,

HU²,

Chen³

2014

JESTR

View full text Add to dashboard Cite

show abstract

“…But at this time there is a new method, namely the Deep Learning method, which can process training quickly, at this time there are also many researchers conducting research on predictions using the method as reflected by Kim et al regarding the prediction of household electricity consumption using CNN-LSTM Hybrid Network [12], the proposed method can be quickly and accurately in predicting irregular energy consumption trends in the dataset of household power consumption. However, because the proposed method was processed earlier by the sliding window algorithm [13], this caused a prediction delay in the actual data [14], in other studies carried out by Young-Jun in electrical energy forecasting By comparing the models contained in the Deep Learning [15] including the LSTM, Gru, and SEQ2SEQ models with the results of the LSTM experiment get the best results with RMSE 0.96 [16], but this value is not good enough to use the actual data to use seasonal data features and Long term in forecasting more accurate electrical energy. Therefore, the researcher will propose a multivariate time series model [17] using the LSTM algorithm as a model and electrical energy prediction algorithm and Teacher Forcing Technique [18] to help in long-term predictions using public consumption datasets taken from the Smart Meters in London Some conditions or seasons.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Riady

Sen²

2021

JISA

View full text Add to dashboard Cite

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

show abstract

Design of dual‐mode decoder based on LDPC/turbo code

Wang

et al. 2017

IET Communications

View full text Add to dashboard Cite

An efficient sliding window algorithm using adaptive-length guard window for turbo decoders

Cited by 9 publications

References 7 publications

A Parallelized Implementation of Turbo Decoding Based on Network on Chip Multi - core Processor

A Parallelized Implementation of Turbo Decoding Based on Network on Chip Multi - core Processor

Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Design of dual‐mode decoder based on LDPC/turbo code

Contact Info

Product

Resources

About