Uplink MIMO Detection using Ising Machines: A Multi-Stage Ising Approach

TL;DR

An adaptive multi-stage Ising machine-based MIMO detector that extends the performance gains of physics-inspired computation to Large and Massive MIMO systems with a large number of users and very high modulation schemes (up to 256-QAM).

Abstract

Multiple-Input-Multiple-Output~(MIMO) signal detection is central to every state-of-the-art communication system, and enhancements in error performance and computation complexity of MIMO detection would significantly enhance data rate and latency experienced by the users. Theoretically, the optimal MIMO detector is the maximum-likelihood (ML) MIMO detector; however, due to its extremely high complexity, it is not feasible for large real-world communication systems. Over the past few years, algorithms based on physics-inspired Ising solvers, like Coherent Ising machines and Quantum Annealers, have shown significant performance improvements for the MIMO detection problem. However, the current state-of-the-art is limited to low-order modulations or systems with few users. In this paper, we propose an adaptive multi-stage Ising machine-based MIMO detector that extends the performance gains of physics-inspired computation to Large and Massive MIMO systems with a large number of users and very high modulation schemes~(up to 256-QAM). We enhance our previously proposed delta Ising formulation and develop a heuristic that adaptively optimizes the performance and complexity of our proposed method. We perform extensive micro-benchmarking to optimize several free parameters of the system and evaluate our methods' BER and spectral efficiency for Large and Massive MIMO systems (up to 32 users and 256 QAM modulation).

Figures (17)

• Figure 1: Example: Typical evolution of the CIM state denoted by variables $x_i$.
• Figure 2: Convergence of CIM: variations of the expected value of the square of the CIM state variables demonstrate that approximately 500 integration steps are sufficient for the ODEs to achieve the steady-state behavior. The expectation is taken over different state variables and 1000 problem instances.
• Figure 3: MDI-MIMO: flow diagram illustrating various steps in the algorithm.
• Figure 4: MDI-MIMO: example execution for 16-QAM illustrating that MDI-MIMO adaptively adjusts the search space and improves the starting guess.
• Figure 5: BER and spectral efficiency performance of ($16$ users $\times$$16$ antennas) Large MIMO: demonstrating that MDI-MIMO significantly outperforms MMSE, MMSE-SIC, ZF, and RI-MIMO and provides much higher spectral efficiency.