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Learning to Equalize OTFS
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AI
DOI：10.1109/TWC.2022.3160600.png)
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Abstract
En 中文
Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain. The conventional waveform, orthogonal frequency division multiplexing (OFDM), requires tracking frequency selective fading channels over the time, whereas OTFS benefits from full time-frequency diversity by leveraging appropriate equalization techniques. In this paper, we consider a neural network-based supervised learning framework for OTFS equalization. Learning of the introduced neural network is conducted in each OTFS frame fulfilling an online learning framework: the training and testing datasets are within the same OTFS-frame over the air. Utilizing reservoir computing, a special recurrent neural network, the resulting one-shot online learning is sufficiently flexible to cope with channel variations among different OTFS frames (e.g., due to the link/rank adaptation and user scheduling in cellular networks). The proposed method does not require explicit channel state information (CSI) and simulation results demonstrate a lower bit error rate (BER) than conventional equalization methods in the low signal-to-noise (SNR) regime under large Doppler spreads. When compared with its neural network-based counterparts for OFDM, the introduced approach for OTFS will lead to a better tradeoff between the processing complexity and the equalization performance.
Keywords:
OFDM
Training
Time-frequency analysis
Wireless communication
Channel estimation
Modulation
MIMO communication
OTFS
OFDM
delay-Doppler
neural network
online learning
reservoir computing
one-shot learning
channel equalization
5G-advanced
symbol detection
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W