"Machine LLRning": Learning to Softly Demodulate
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication
Recent advances in wireless communication with the enormous demands of sensing ability have given rise to the integrated sensing and communication (ISAC) technology, among which passive sensing plays an important role. T…
Integrated sensing and communicationISACChannel Estimation Based on Machine Learning Paradigm for Spatial Modulation OFDM
In this paper, deep neural network (DNN) is integrated with spatial modulation-orthogonal frequency division multiplexing (SM-OFDM) technique for end-to-end data detection over Rayleigh fading channel. This proposed syst…
BIG-bench Machine LearningError analysis of a demodulation procedure for multicarrier signals with slowly-varying carriers
We propose a procedure to demodulate analog signals encoded by a multicarrier modulator, with slowly-varying carrier shapes. We prove that the asymptotic demodulation error can be made arbitrarily small. The intended app…
Softly Symbolifying Kolmogorov-Arnold Networks
Kolmogorov-Arnold Networks (KANs) offer a promising path toward interpretable machine learning: their learnable activations can be studied individually, while collectively fitting complex data accurately. In practice, ho…
Interpretable Machine LearningExploring Softly Masked Language Modelling for Controllable Symbolic Music Generation
This document presents some early explorations of applying Softly Masked Language Modelling (SMLM) to symbolic music generation. SMLM can be seen as a generalisation of masked language modelling (MLM), where instead of e…
Language ModellingMusic Generation