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"Machine LLRning": Learning to Softly Demodulate
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Soft demodulation, or demapping, of received symbols back into their conveyed soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any modern receiver. In this paper, a trainable universal neural network-based demodulator architecture, dubbed "LLRnet", is introduced. LLRnet facilitates an improved performance with significantly reduced overall computational complexity. For instance for the commonly used quadrature amplitude modulation (QAM), LLRnet demonstrates LLR estimates approaching the optimal log maximum a-posteriori inference with an order of magnitude less operations than that of the straightforward exact implementation. Link-level simulation examples for the application of LLRnet to 5G-NR and DVB-S.2 are provided. LLRnet is a (yet another) powerful example for the usefulness of applying machine learning to physical layer design.
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Cited by 1 Pith paper
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Neural Network-based Information-Theoretic Transceivers for High-Order Modulation Schemes
A neural-network LLR demapper and a CNN autoencoder transceiver achieve BER/SER comparable to conventional QAM receivers over AWGN, with performance strongly dependent on training SNR.
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