Adaptive modulation and coding using deep recurrent neural network

Adaptive Modulation and Coding (AMC) is a promising technique to increase the average spectral efficiency of communication links. This research proposes a novel AMC method based on a supervised deep learning approach to maximize the average spectral efficiency of OFDM wireless systems while the bit...

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Vydáno v:Telecommunication systems Ročník 81; číslo 4; s. 615 - 623
Hlavní autoři: Mohammadvaliei, Sadegh, Sebghati, Mohammadali, Zareian, Hassan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.12.2022
Springer Nature B.V
Témata:
Artificial Intelligence
Bit error rate
Business and Management
Coding
Computer Communication Networks
Efficiency
Feature extraction
Frequency response
IT in Business
Machine learning
Modulation
Probability Theory and Stochastic Processes
Recurrent neural networks
Signal to noise ratio
Telecommunications systems
Link adaptation
Deep neural network
OFDM
Adaptive modulation and coding
ISSN:1018-4864, 1572-9451
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Shrnutí:Adaptive Modulation and Coding (AMC) is a promising technique to increase the average spectral efficiency of communication links. This research proposes a novel AMC method based on a supervised deep learning approach to maximize the average spectral efficiency of OFDM wireless systems while the bit error rate (BER) remains under a predefined threshold. The proposed method consists of a one-dimensional convolutional network that performs feature extraction and a long short-term memory network that learns the behavior of the channel. Input features are the magnitudes and phases of the estimated channel frequency response in the pilot subcarriers and signal-to-noise ratio. Datasets of various fading channel responses were generated using WINNER II. The proposed method was compared with previous methods based on different criteria, including average spectral efficiency, BER, the accuracy of predictions, the average delay of each prediction, and model complexity. The simulation results confirmed the superiority of the proposed AMC method.
Bibliografie:ObjectType-Article-1
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ISSN:1018-4864
1572-9451
DOI:10.1007/s11235-022-00965-4