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

Abstract

Optimal MIMO detection has been one of the most challenging and computationally inefficient tasks in wireless systems. We show that the new analog computing techniques like Coherent Ising Machines (CIM) 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 problem and further evolves it into an algorithm that achieves near-optimal MIMO detection. Massive MIMO systems, that have a large number of antennas at the Access point (AP), allow linear detectors to be near-optimal. However, the simplified detection in these systems comes at the cost of overall throughput, which could be improved by supporting more users. By means of numerical simulations, we show that in principle a MIMO detector based on a hybrid use of a CIM would allow us to add more transmitter antennas/users and increase the overall throughput of the cell by a significant factor. This would open up the opportunity to operate using more aggressive modulation and coding schemes and hence achieve high throughput: for a $16\times 16$ large MIMO system, we estimate around 2.5$\times$ more throughput in mid-SNR regime ($\approx 12dB$) and 2$\times$ more throughput in high-SNR regime( $>$ 20dB) than the industry standard, Minimum-Mean Square Error decoding (MMSE).

Accepted for IEEE Transactions on Wireless Communication

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Cited by in corpus (6)

• Uplink MIMO Detection using Ising Machines: A Multi-Stage Ising Approach
• Mean-field Coherent Ising Machines with artificial Zeeman terms
• A Finite-Range Search Formulation of Maximum Likelihood MIMO Detection for Coherent Ising Machines
• Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine
• General Oscillator-Based Ising Machine Models with Phase-Amplitude Dynamics and Polynomial Interactions
• Towards Neuromorphic Processing for Next-Generation MU-MIMO Detection