论文标题
最大似然模拟物检测的有限范围搜索公式
A Finite-Range Search Formulation of Maximum Likelihood MIMO Detection for Coherent Ising Machines
论文作者
论文摘要
在过去的几年中，物理启发的计算出现了最大似然性mimo检测。这些方法涉及将MIMO检测问题转换为ISIN的最小化问题，然后可以在ISING机器上求解。最近的作品显示了使用量子退火优化器和连贯的ISING机器的MIMO无线检测的有希望的预测。尽管这些方法对于BPSK和4-QAM的表现非常好，但它们努力为16 QAM和更高的调制提供良好的BER。在本文中，我们探索了增强的CIM模型，并提出了一种新颖的Ising配方，该配方被证明是第一个在大型和庞大的MIMO系统的BER性能中提供显着增长的求解器，例如$ 16 \ TIMES16 $和$ 16 \ times32 $，甚至在256-QAM调制下保持其性能增长。我们进一步执行了光谱效率分析，并表明，对于带有自适应调制和编码的$ 16 \ Times16 $ MIMO，我们的方法可以提供超过MMSE的大量吞吐量，可实现SNR $ \ leq25 $ db的$ 2 \ times $ thimput $ \ leq25 $ db，最高$ 1.5 \ $ 1.5 \ $ 1.5 \ $ 1.5 \ $ 1.5 \ geq $ $ \ geq 30 $ \ geq 30 $ \ db db。
The last couple of years have seen an emergence of physics-inspired computing for maximum likelihood MIMO detection. These methods involve transforming the MIMO detection problem into an Ising minimization problem, which can then be solved on an Ising Machine. Recent works have shown promising projections for MIMO wireless detection using Quantum Annealing optimizers and Coherent Ising Machines. While these methods perform very well for BPSK and 4-QAM, they struggle to provide good BER for 16-QAM and higher modulations. In this paper, we explore an enhanced CIM model, and propose a novel Ising formulation, which together are shown to be the first Ising solver that provides significant gains in the BER performance of large and massive MIMO systems, like $16\times16$ and $16\times32$, and sustain its performance gain even at 256-QAM modulation. We further perform a spectral efficiency analysis and show that, for a $16\times16$ MIMO with Adaptive Modulation and Coding, our method can provide substantial throughput gains over MMSE, achieving $2\times$ throughput for SNR $\leq25$ dB, and up to $1.5\times$ throughput for SNR $\geq 30$ dB.
