Low Complexity Deep-Decoder for OTSM with Hardware Impairments

Recently, the single-carrier modulation scheme orthogonal time-sequency multiplexing (OTSM) has emerged as an alternative to orthogonal time-frequency space (OTFS). By virtue of usingWalsh-Hadamard transform, OTSM reduces modulation/ demodulation complexity, with a performance similar to OTFS. Howev...

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Vydané v:IEEE communications letters Ročník 27; číslo 12; s. 1
Hlavní autori: Singh, Amit, Sharma, Sanjeev, Sharma, Mohit, Deka, Kuntal
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Complexity
Convolutional neural networks
Data Augmentation
Decoding
Deep Learning
Delays
Demodulation
Detectors
DL-OTSM
Error detection
Gauss-Seidel method
Hardware
Hardware-Impairments (HIs)
IQ Imbalance
Modulation
Multiplexing
Performance evaluation
Signal detection
Signal detectors
Symbols
Transmitters
ISSN:1089-7798, 1558-2558
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Popis
Shrnutí:Recently, the single-carrier modulation scheme orthogonal time-sequency multiplexing (OTSM) has emerged as an alternative to orthogonal time-frequency space (OTFS). By virtue of usingWalsh-Hadamard transform, OTSM reduces modulation/ demodulation complexity, with a performance similar to OTFS. However, the bit error-rate (BER) performance of OTSM deteriorates drastically in presence of transceiver hardware impairments (HIs), particularly with in-phase and quardature phase imbalance. In this paper, we develop a deep neural network based signal detector for OTSM (called DL-OTSM) to effectively compensate for HIs, with low decoding complexity.We extensively evaluate the performance of DL-OTSM with respect to variations in system parameters, including user mobility, HIs, frame size, and modulation order. Our results show that DL-OTSM achieves a significantly better BER performance compared to the state-of-the-art Gauss-Seidel method and conventional minimum mean-square error detector, regardless of HIs.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2023.3327249