AI 中文总结
针对低信噪比信道建模，该研究提出混合框架NN-CLEAN，结合神经网络与CLEAN算法，在5dB SNR下精度超96%，计算复杂度大幅降低，可作为MIMO系统实时信道估计方案。
AI 中文摘要
精确的多径参数估计对现代无线通信系统至关重要，尤其在极具挑战性的低信噪比（SNR）环境中。传统最大似然估计算法（如CLEAN）可实现高分辨率参数提取，但因采用穷举网格搜索而存在计算复杂度极高的问题。相反，纯数据驱动的深度学习方法缺乏物理依据，且难以在可变多径密度和离网参数间泛化。为解决这些局限，本文提出神经网络辅助CLEAN（NN-CLEAN），这是一种将多头残差网络直接嵌入迭代CLEAN提取循环的混合框架。该方法用快速、可并行的前向传播替代穷举网格搜索，同时将残差减法委托给精确数学模型，使NN-CLEAN在不累积非物理误差的情况下分离物理多径参数。大量蒙特卡洛仿真表明，NN-CLEAN在5 dB SNR下估计精度超过96%，与传统网格搜索CLEAN（GS-CLEAN）基线相当，同时大幅降低计算复杂度，且显著优于子空间方法和独立一次性神经网络。关键的是，NN-CLEAN的执行运行时间和内存消耗随批大小增加呈现近平坦缩放，这种高效并行化使NN-CLEAN成为MIMO系统信道估计的鲁棒实时解决方案。
英文摘要
Accurate multipath parameter estimation is critical for modern wireless communication systems, particularly in challenging low-SNR environments. Traditional Maximum Likelihood Estimation algorithms, such as CLEAN, provide high-resolution parameter extraction but suffer from prohibitive computational complexity due to exhaustive grid search. Conversely, purely data-driven deep learning approaches lack physical grounding and struggle to generalize across variable multipath densities and off-grid parameters. To address these limitations, this paper proposes Neural Network-Assisted CLEAN (NN-CLEAN), a hybrid framework that embeds a multi-head residual network directly into the iterative CLEAN extraction loop. By replacing the exhaustive grid search with rapid, parallelizable forward passes while delegating residual subtraction to exact mathematical models, NN-CLEAN isolates physical multipath parameters without accumulating non- physical errors. Extensive Monte Carlo simulations demonstrate that NN-CLEAN achieves estimation accuracy exceeding 96% at 5 dB SNR, matching the traditional Grid-Search CLEAN (GS- CLEAN) baseline, while providing a massive reduction in computational complexity and substantially outperforming subspace methods and standalone one-shot neural networks. Crucially, NN-CLEAN exhibits a near-flat scaling in execution runtime and memory consumption as batch sizes increase. This highly efficient parallelization establishes NN-CLEAN as a robust, real- time solution for channel estimation in MIMO systems.
