GD4用图结构直接在离散符号空间去噪，实现快速高精度MIMO检测。
GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection
- 基于图结构的离散去噪，直接在符号空间操作而非连续松弛空间
- 仅需1~2次去噪即可达到比现有方法更优的解质量，且计算开销低
- 适用于欠定与过定场景，适合需要低延迟高精度的无线通信系统
在无线通信中，求解多输入多输出（MIMO）检测问题的最优解是NP难问题。在发射天线数 $N_t$ 与接收天线数 $N_r < N_t$ 的欠定系统中，获得高性能的次优解并兼顾性能-复杂度权衡尤为困难。现有的基于扩散模型的MIMO检测器虽有潜力，但推理时需大量采样迭代，且在欠定情形下性能下降。本文提出GD4，一种基于图的离散去噪扩散方法用于MIMO检测。与现有在连续松弛空间操作的扩散检测器不同，GD4直接在离散符号空间进行去噪，支持一次或少数几次去噪评估即完成快速推理。数值结果表明，在相似推理计算预算下，GD4生成的次优解质量优于现有扩散类检测器及若干经典基线方法，包括盒约束下的Babai点与K-best盒约束随机化Klein-Babai点，在欠定与过定场景均表现优异。
原文摘要 · Abstract (English)
In wireless communications, recovering the optimal solution to the multiple-input multiple-output (MIMO) detection problem is NP-hard. Obtaining high-quality suboptimal solutions with a favorable performance-complexity trade-off is particularly challenging in under-determined systems with $N_t$ transmit antennas and $N_r < N_t$ receive antennas. Recent diffusion-based MIMO detectors have shown promise, but they require extensive sampling iterations at inference time, and their performance degrades in under-determined scenarios. We propose GD4, a graph-based discrete denoising diffusion method for MIMO detection. Unlike existing diffusion-based detectors that operate in a continuous relaxed space, GD4 performs denoising directly in the discrete symbol space and enables fast inference with one or a few denoising evaluations. Numerical results show that, under a similar inference-time compute budget, GD4 produces higher-quality suboptimal solutions than existing diffusion-based detectors and some widely used classical baseline including box-constrained Babai point and the $K$-best box-constrained randomized Klein-Babai point in both under-determined and overdetermined settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。