scholarly journals Evaluasi Penerapan Algoritma Neural Network Sebagai Teknik Reduksi PAPR Pada Sistem OFDM

2021 ◽  
Vol 6 (1) ◽  
pp. 147
Author(s):  
Mohamad Ridwan ◽  
Melki Mario Gulo ◽  
Yoedy Moegiharto ◽  
Arifin Arifin ◽  
Muhammad Milchan
Keyword(s):  
Neural Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Partial Transmit Sequence ◽  
Selective Mapping ◽  
Clipping And Filtering

Pada makalah ini dilakukan evaluasi kinerja algoritma Neural Network sebagai teknik reduksi sistem OFDM.  Hasil simulasi untuk sinyal OFDM dengan jumlah subcarrier sebanyak 64 dan modulasi 16 QAM menunjukkan penerapan algorima NN menghasilkan penurunan nilai PAPR sekitar 5,6 dB dari PAPR sinyal OFDM tanpa reduksi. Juga dibandingkan dengan teknik reduksi PAPR metode Iterative Clipping and Filtering, (ICF), Selective Mapping (SLM) dan Partial Transmit Sequence (PTS). Dari kurva CCDF juga ditunjukkan bahwa metode NN menghasilkan kinerja yang lebih baik dibanding metode digabungkan dengan teknik Iterative Clipping and Filtering, (ICF), Selective Mapping (SLM) dan Partial Transmit Sequence (PTS). Evaluasi di sisi penerima dengan pengamatan nilai bit error rate, penerapan algoritma NN memiliki kinerja yang terbaik

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.

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 PAPR Reduction of a Universal Filtered Multicarrier Using a Selective Mapping Scheme

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Farooq Sijal Shawqi ◽  
Ahmed Talaat Hammoodia ◽  
Lukman Audah ◽  
Ammar Ahmed Falih
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Communication Systems ◽  
Average Power ◽  
Mapping Method ◽  
Signal Representation ◽  
Multiple Signal ◽  
Representation Technique ◽  
Selective Mapping ◽  
Papr Performance

The new generation of wireless communication systems involves several different technologies. The universal filtered multicarrier (UFMC) is one of these technologies. UFMC supports various numerology designs; however, the high peak to average power ratio (PAPR) is a major limitation faced by designers. Therefore, diverse approaches have been introduced, such as amplitude clipping, tone reservation, and active constellation extension, to mitigate the PAPR problem. These algorithms produce significant degradation in terms of bit error rate or power consumption. Another proposed solution is multiple signal representation schemes, which have promised to conserve bit error rate performance without power waste. Selected mapping is a multiple signal representation technique that reduces the PAPR without bit error degradation. This paper focuses on integrating the selected mapping method with the UFMC. Simulation results show that the integrated algorithm presents better PAPR performance: the PAPR was reduced by 2.1 dB and 1 dB for UFMC and CP-OFDM, respectively, without bit error rate degradation.

scholarly journals Reduksi Peak-To-Average Power Ratio Pada Sistem STBC MIMO-OFDM dengan Metode Selected Mapping dan Partial Transmit Sequence

2017 ◽  
Vol 3 (1) ◽  
pp. 85
Author(s):  
Andi Maddanaca
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
High Power ◽  
Side Information ◽  
Average Power ◽  
Multipath Fading ◽  
Power Ratio ◽  
Partial Transmit Sequence ◽  
High Power Amplifier ◽  
Mimo Ofdm

Teknik OFDM merupakan teknik multicarrier yang mengefisienkan bandwidth. Penggunaan teknik OFDM dapat mengatasi multipath fading dan intersymbol interference (ISI). Namun demikian, OFDM mempunyai dua kelemahan, salah satunya adalah peak-to-average power ratio (PAPR) yang tinggi. PAPR yang tinggi akan menyebabkan distorsi nonlinear pada high power amplifier (HPA) karena HPA membatasi keluaran dengan nilai tertentu dan mengurangi efisiensi daya amplifier. Oleh karena itu, PAPR yang tinggi harus direduksi. Metode reduksi PAPR yang diajukan adalah dengan menggunakan metode Selected Mapping (SLM) dan Partial Transmit Sequence (PTS). Kedua metode ini memiliki kekurangan dalam hal kompleksitas multiplikasi dan penjumlahan dan adanya bit side information yang harus dikirimkan ke receiver. Oleh karena itu, penulis mengajukan pengembangan dari metode tersebut dengan memodifikasi faktor rotasi fasa menjadi pattern konversi terdefinisi yang lebih adaptif pada SLM, dan mengurangi iterasi pembangkitan faktor rotasi fasa pada metode PTS dengan pendefinisian faktor rotasi fasa yang terbatas. Hasil simulasi dengan 1000 simbol OFDM menunjukkan bahwa kemampuan reduksi PAPR pada metode m-SLM dan m-PTS mendekati kemampuan reduksi metode konvensional. Bit error rate (BER) yang dihasilkan juga mengalami perbaikan dibandingkan BER tanpa reduksi. m-SLM secara keseluruhan mengungguli kinerja dari m-PTS, baik pada nilai reduksi PAPR maupun pada perbaikan BER.

Performance evaluation of the sequential neural network decoder with improved bit error rate

2021 ◽  
Author(s):  
J. Ravindrababu ◽  
Arun Sadanand Tigadi ◽  
J.K. Kiruthika ◽  
Mohd Zeeshan Ansari
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Bit Error Rate ◽  
Error Rate
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