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GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection |
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Notice: Paper or abstract available at https://arxiv.org/abs/2605.00423, which is subject to the license by the author or copyright owner provided with this content. Please go to the source to verify the license and copyright information for your use. |
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Qincheng Lu, Sitao Luan, Xiao-Wen Chang |
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https://arxiv.org/abs/2605.00423 |
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Sitao Luan |
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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.