Practical MU-MIMO Detection and LDPC Decoding Through Digital Annealing

Digital annealing has been successfully applied to solving combinatorial optimization (CO) problems. It is more flexible, robust, and easier to deploy on edge platforms compared to its counterparts including quantum annealing and analog and in-memory Ising machines. In this work, we apply digital annealing to compute-intensive communication digital signal processing problems, including multi-user detection in multiple-input and multiple-output (MU-MIMO) wireless communication systems and decoding low-density parity-check (LDPC) codes. We show that digital annealing can achieve near maximum likelihood (ML) accuracy for MIMO detection with even lower complexity than the conventional minimum mean square error (MMSE) detection. In LDPC decoding, we enhance digital annealing by introducing a new cost function that improves decoding accuracy and reduces computational complexity compared to the standard formulations.