Noise Recycling using GRAND for Improving the Decoding Performance
Arslan Riaz, Amit Solomon, Furkan Ercan, Muriel Médard, Rabia Tugce Yazicigil, Ken R. Duffy · 2023
Although noise is often modeled as an independent Gaussian process in communication systems, a single communication channel is typically impacted by temporally correlated noise. While this correlation is usually broken up through interleaving and discarded, this demonstration shows that an accurate continuous noise estimate can be obtained in a purely hard-detection scenario by exploiting the temporal correlation that can be removed from the subsequent channels resulting in a significantly improved decoding performance and energy efficiency. This technique requires only simple receiver-side changes with no sender-side alterations and works for any codebook structure, decoder, and modulation scheme [1]. We will use the hard-detection Guessing Random Additive Noise Decoding (GRAND) chip [2] to demonstrate the decoding gains of greater than 2 dB obtained by employing noise recycling in hardware. We further show that significant energy savings of up to 36× and latency reduction of up to 12× can be obtained when GRAND is used for noise recycling.