A Digital Pre-Distortion Scheme for CMMB Repeater System Based on Dynamic Neural Networks

CMMB technology employs Orthogonal Frequency Division Multiplexing (OFDM) modulation with 4096 subcarriers for 8MHz bandwidth mode, HPA in the CMMB repeater will cause significant distortion and spectral extension. To compensate HPA nonlinearity and memory effect in a CMMB repeater system, this paper proposed a pre-distortion scheme based on RNN and Bayesian Regularization for the first time. Computer simulation is used to confirm the validation of the proposed scheme and an adjacent channel leakage ratio (ACLR) improvement of 20 dB is obtained in the paper.

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EVM Loss: A Loss Function for Training Neural Networks in Communication Systems

Sensors ◽

10.3390/s21041094 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1094

Author(s):

Scott Stainton ◽

Martin Johnston ◽

Satnam Dlay ◽

Paul Anthony Haigh

Keyword(s):

Neural Networks ◽

Loss Function ◽

Communication Systems ◽

Orthogonal Frequency Division Multiplexing ◽

Frequency Division Multiplexing ◽

Efficiency Gain ◽

Frequency Division ◽

Error Vector Magnitude ◽

Modulation Formats ◽

Complex Approach

Neural networks and their application in communication systems are receiving growing attention from both academia and industry. The authors note that there is a disconnect between the typical objective functions of these neural networks with regards to the context in which the neural network will eventually be deployed and evaluated. To this end, a new loss function is proposed and shown to increase the performance of neural networks when implemented in a communication system compared to previous methods. It is further shown that a ‘split complex’ approach used by many implementations can be improved via formalisation of the ‘concatenated complex’ approach described herein. Experimental results using the orthogonal frequency division multiplexing (OFDM) and spectrally efficient frequency division multiplexing (SEFDM) modulation formats with varying bandwidth compression factors over a wireless visible light communication (VLC) link validate the efficacy of the proposed method in a real system, achieving the lowest error vector magnitude (EVM), and thus bit error rate (BER), across all experiments, with a 5 dB to 10 dB improvement in the received symbols EVM overall compared to the baseline implementation, with bandwidth compressions down to 40% compared to OFDM, resulting in a spectral efficiency gain of 67%.

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A Real-Time Deep Learning OFDM Receiver

ACM Transactions on Reconfigurable Technology and Systems ◽

10.1145/3494049 ◽

2022 ◽

Vol 15 (3) ◽

pp. 1-25

Author(s):

Stefan Brennsteiner ◽

Tughrul Arslan ◽

John Thompson ◽

Andrew McCormick

Keyword(s):

Neural Network ◽

Neural Networks ◽

Real Time ◽

Field Programmable Gate Array ◽

Orthogonal Frequency Division Multiplexing ◽

Frequency Division Multiplexing ◽

Frequency Division ◽

Field Programmable ◽

Gate Array ◽

Fully Connected

Machine learning in the physical layer of communication systems holds the potential to improve performance and simplify design methodology. Many algorithms have been proposed; however, the model complexity is often unfeasible for real-time deployment. The real-time processing capability of these systems has not been proven yet. In this work, we propose a novel, less complex, fully connected neural network to perform channel estimation and signal detection in an orthogonal frequency division multiplexing system. The memory requirement, which is often the bottleneck for fully connected neural networks, is reduced by ≈ 27 times by applying known compression techniques in a three-step training process. Extensive experiments were performed for pruning and quantizing the weights of the neural network detector. Additionally, Huffman encoding was used on the weights to further reduce memory requirements. Based on this approach, we propose the first field-programmable gate array based, real-time capable neural network accelerator, specifically designed to accelerate the orthogonal frequency division multiplexing detector workload. The accelerator is synthesized for a Xilinx RFSoC field-programmable gate array, uses small-batch processing to increase throughput, efficiently supports branching neural networks, and implements superscalar Huffman decoders.

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Practical designs for out-of-band emission suppression and adjacent channel interference rejection for orthogonal frequency division multiplexing-based cognitive radios

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-015-0375-5 ◽

2015 ◽

Vol 2015 (1) ◽

Cited By ~ 1

Author(s):

Juan Fang ◽

I-Tai Lu

Keyword(s):

Orthogonal Frequency Division Multiplexing ◽

Cognitive Radios ◽

Frequency Division Multiplexing ◽

Interference Rejection ◽

Frequency Division ◽

Adjacent Channel Interference ◽

Band Emission ◽

Channel Interference ◽

Adjacent Channel

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Iterative Learning for Reliable Underwater Link Adaptation (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7152 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13761-13762

Author(s):

Junghun Byun ◽

Yong-Ho Cho ◽

Tae-Ho Im ◽

Hak-Lim Ko ◽

Kyung-Seop Shin ◽

...

Keyword(s):

Performance Improvement ◽

Orthogonal Frequency Division Multiplexing ◽

Link Adaptation ◽

Learning Model ◽

Iterative Learning ◽

Frequency Division Multiplexing ◽

Frequency Division ◽

Acoustic Communications ◽

Learning Framework ◽

First Time

This paper describes an iterative learning framework consisting of multi-layer prediction processes for underwater link adaptation. To obtain a dataset in real underwater environments, we implemented OFDM (Orthogonal Frequency Division Multiplexing)-based acoustic communications testbeds for the first time. Actual underwater data measured in Yellow Sea, South Korea, were used for training the iterative learning model. Remarkably, the iterative learning model achieves up to 25% performance improvement over the conventional benchmark model.

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