Ising Machines' Dynamics and Regularization for Near-Optimal MIMO Detection

Abhishek Kumar Singh, Kyle Jamieson, Peter L. McMahon, Davide Venturelli

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that new analog computing approaches, such as Coherent Ising Machines (CIMs), are promising candidates for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the naive approach and evolve it into a regularized, Ising-based tree search algorithm that achieves near-optimal performance. By means of numerical simulation using the Rayleigh fading channel model, we show that in principle, a MIMO detector based on a high-speed Ising machine (such as a CIM implementation optimized for latency) would allow a higher transmitter antennas (users)-to-receiver antennas ratio and thus increase the overall throughput of the cell by a factor of two or more for massive MIMO systems. Our methods create an opportunity to operate wireless systems using more aggressive modulation and coding schemes and hence achieve high spectral efficiency: for a $16\times 16$ MIMO system, we estimate around $2.5\times $ more throughput in the mid-SNR regime (≈12 dB) and $2\times $ more throughput in the high-SNR regime (>20 dB) as compared to the industry standard, a Minimum-Mean Square Error (MMSE) linear decoder.

Original languageEnglish (US)
Pages (from-to)11080-11094
Number of pages15
JournalIEEE Transactions on Wireless Communications
Issue number12
StatePublished - Dec 1 2022

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Computer Science Applications


  • Coherent Ising Machines (CIMs)
  • MIMO detection
  • large MIMO
  • massive MIMO


Dive into the research topics of 'Ising Machines' Dynamics and Regularization for Near-Optimal MIMO Detection'. Together they form a unique fingerprint.

Cite this