# Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection

arXiv: https://arxiv.org/abs/2306.16264 · HTML: https://mlsanity.com/paper/2306.16264/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures](https://mlsanity.com/paper/1409.2574/) (2014)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Deep Unfolding for Communications Systems: A Survey and Some New Directions](https://mlsanity.com/paper/1906.05774/) (2019)
- [Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding](https://mlsanity.com/paper/2004.10715/) (2020)
- [Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach](https://mlsanity.com/paper/1812.10044/) (2018)
- [Leveraging Quantum Annealing for Large MIMO Processing in Centralized Radio Access Networks](https://mlsanity.com/paper/2001.04014/) (2020)
- [Ising Machines' Dynamics and Regularization for Near-Optimal Large and Massive MIMO Detection](https://mlsanity.com/paper/2105.10535/) (2021)
- [MMSE Signal Detection for MIMO Systems based on Ordinary Differential Equation](https://mlsanity.com/paper/2205.01338/) (2022)
- [Ordinary Differential Equation-based MIMO Signal Detection](https://mlsanity.com/paper/2304.14097/) (2023)

## Abstract (arXiv)

Multiple-input multiple-output (MIMO) is a key ingredient of next-generation wireless communications. Recently, various MIMO signal detectors based on deep learning techniques and quantum(-inspired) algorithms have been proposed to improve the detection performance compared with conventional detectors. This paper focuses on the simulated bifurcation (SB) algorithm, a quantum-inspired algorithm. This paper proposes two techniques to improve its detection performance. The first is modifying the algorithm inspired by the Levenberg-Marquardt algorithm to eliminate local minima of maximum likelihood detection. The second is the use of deep unfolding, a deep learning technique to train the internal parameters of an iterative algorithm. We propose a deep-unfolded SB by making the update rule of SB differentiable. The numerical results show that these proposed detectors significantly improve the signal detection performance in massive MIMO systems.