4 citations · 8 across the 6 of their papers we have counts for
10 papers
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations
Yu-Shan Tai, Chieh-Fang Teng, Cheng-Yang Chang +1
Convolutional neural networks (CNNs) achieve remarkable performance in a wide range of fields. However, intensive memory access of activations introduces considerable energy consum…
Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding
Wen-Chiao Tsai, Chieh-Fang Teng, Han-Mo Ou +1
Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely…
Accumulated Polar Feature-based Deep Learning for Efficient and Lightweight Automatic Modulation Classification with Channel Compensation Mechanism
Chieh-Fang Teng, Ching-Yao Chou, Chun-Hsiang Chen +1
In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (…
Syndrome-Enabled Unsupervised Learning for Neural Network-Based Polar Decoder and Jointly Optimized Blind Equalizer
Chieh-Fang Teng, Yen-Liang Chen
Recently, the syndrome loss has been proposed to achieve "unsupervised learning" for neural network-based BCH/LDPC decoders. However, the design approach cannot be applied to polar…
Low-Complexity LSTM-Assisted Bit-Flipping Algorithm for Successive Cancellation List Polar Decoder
Chun-Hsiang Chen, Chieh-Fang Teng, An-Yeu Wu
Polar codes have attracted much attention in the past decade due to their capacity-achieving performance. The higher decoding capacity is required for 5G and beyond 5G (B5G). Altho…
Unsupervised Learning for Neural Network-based Polar Decoder via Syndrome Loss
Chieh-Fang Teng, An-Yeu Wu
With the rapid growth of deep learning in many fields, machine learning-assisted communication systems had attracted lots of researches with many eye-catching initial results. At t…