  The Langevin sampling method relies on an accurate score matching while the
existing massive multiple-input multiple output (MIMO) Langevin detection
involves an inevitable singular value decomposition (SVD) to calculate the
posterior score. In this work, a massive MIMO sampling detection strategy that
leverages the denoising diffusion model is proposed to narrow the gap between
the given iterative detector and the maximum likelihood (ML) detection in an
SVD-free manner. Specifically, the proposed score-based sampling detection
strategy, denoted as approximate diffusion detection (ADD), is applicable to a
wide range of iterative detection methods, and therefore entails a considerable
potential in their performance improvement by multiple sampling attempts. On
the other hand, the ADD scheme manages to bypass the channel SVD by introducing
a reliable iterative detector to produce a sample from the approximate
posterior, so that further Langevin sampling is tractable. Customized by the
conjugated gradient descent algorithm as an instance, the proposed sampling
scheme outperforms the existing score-based detector in terms of a better
complexity-performance trade-off.
