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

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 nearoptimal 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. Massive MIMO systems use a large number of antennas at the base station to allow a linear detector to achieve near-optimal performance. However, this comes at the cost of overall throughput, which could be improved by supporting more users with the same number of antennas. 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 more transmitter antennas (users) 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 16×16 MIMO system, we estimate around 2.5× more throughput in the mid-SNR regime (≈ 12 dB) and 2× more throughput in the high-SNR regime (>20 dB) as compared to the industry standard, a Minimum-Mean Square Error (MMSE) linear decoder.