Authors: Sangwoo Park, Hyeryung Jang, Osvaldo Simeone, Joonhyuk Kang
Journal: IEEE Transactions on Signal Processing (publication date: Dec. 2020)
Abstract: We propose meta-learning aided AI demodulator that outperforms conventional communication-theory-based demodulator especially for hardware imperfect IoT transmitters. Unlike conventional AI demodulators that require enormous pilot data, we significantly reduced pilot overhead (e.g., 4 pilots for 16QAM) via meta-learning. Since meta-learning requires additional offline phase for pilot data collection/training, we introduce online meta-learning that alleviates this requirement of additional phase.
Fig. 1: Symbol error rate with respect to the number of pilots (used during meta-testing) for offline meta-learning with16-QAM, Rayleigh fading, and I/Q imbalance for 1,000 meta-training devices. The symbol error is averaged over 10,000data symbols and 100 meta-test devices.
