Soft output decoding improves multiuser detection for wireless signals
Soft-Output GRAND-Aided Macrosymbol
Information Theory
Summary
Detecting signals from multiple users sending data together can be tricky and often leads to errors. The authors extend an existing method called GRAND-AM that guesses noise to better correct errors by producing soft output, which means more detailed information about the chances of bits being correct or wrong. This extra information helps later steps that fix errors to work better, reducing the overall mistakes in received messages. Their approach gives about a 2-decibel improvement in error rates compared to previous methods that only gave yes-or-no decisions.
What this means in practice
- •For wireless communication engineers: Improve the accuracy of multiuser signal detection and error correction in NOMA-based wireless systems using soft-output decoding.
- •For satellite communication teams: Enhance joint multiuser detection and decoding capabilities in satellite links where signal collisions occur without requiring hardware changes.
Authors
Sarah Khalifeh, Alexander Mariona, Ken R. Duffy, Muriel Medard
Abstract
Guessing Random Additive Noise Decoding-Aided Macrosymbol (GRAND-AM) provides a coding-based solution for non-orthogonal multiple access (NOMA) systems, enabling joint multiuser detection and error correction. GRAND-AM outperforms prior methods in symbol error rates while providing hard-detection output. We introduce soft-output GRAND-AM (SOGRAND-AM), a macrosymbol-level joint multiuser detection and decoding framework which generates calibrated bitwise a posteriori error probabilities. SOGRAND-AM extends GRAND-AM by enabling soft-decision outer forward error correction decoders, transparently integrating with existing decoders. SOGRAND-AM achieves approximately 2 dB improvement in bit error rate at the output of the outer soft-input decoder compared to hard input with GRAND-AM.