高维MIMO系统的确定性平滑最大后验检测
Deterministic Smoothed MAP Detection for High-Dimensional MIMO Systems
浏览论文内容
中文总结 AI 辅助
提出确定性平滑MAP框架用于高维MIMO检测，通过高斯混合近似星座先验，实现硬检测SMAP-KR和软输出方法，在性能和复杂度间取得更优权衡。
中文摘要 AI 辅助
我们提出了一种用于高维多输入多输出（MIMO）检测的确定性平滑最大后验（SMAP）框架。离散星座先验被近似为独立同分布的高斯混合，从而产生一个可微的MAP目标，该目标保留了星座结构，同时支持连续优化。与基于采样的方法不同，SMAP将检测表述为一个确定性优化问题，并可由任意检测器的输出初始化，使其既能作为独立检测器运行，也能作为细化阶段。对于硬检测，我们引入了SMAP-KR，它通过根据原始最大似然度量评估的局部$K_R$-邻域搜索来补充连续解。退火延续提高了高阶星座的鲁棒性，而双邻域近似降低了评估混合先验的成本。我们进一步开发了一种软输出SMAP方法，其中基于平滑后验曲率的局部高斯近似提供符号概率和比特对数似然比，无需后验采样或显式反假设列表。针对临界负载MU-MIMO系统的数值结果表明，随着系统维度的增长，SMAP-KR提供了日益有利的性能-复杂度权衡，并且还能有效细化现有检测器的输出。对于编码传输，软SMAP在块错误率上相对于基于LMMSE和K-best的软检测均提供了显著的增益。
英文摘要
We propose a deterministic smoothed maximum a posteriori (SMAP) framework for high-dimensional multiple-input multiple-output (MIMO) detection. The discrete constellation prior is approximated by an i.i.d. Gaussian mixture, yielding a differentiable MAP objective that retains the constellation structure while enabling continuous optimization. Unlike sampling-based approaches, SMAP formulates detection as a deterministic optimization problem and can be initialized by the output of an arbitrary detector, allowing it to operate either as a standalone detector or as a refinement stage. For hard detection, we introduce SMAP-KR, which complements the continuous solution with a local $K_R$-neighbor search evaluated according to the original maximum-likelihood metric. Annealed continuation improves robustness for higher-order constellations, while a two-neighbor approximation reduces the cost of evaluating the mixture prior. We further develop a soft-output SMAP method in which a local Gaussian approximation based on the curvature of the smoothed posterior provides symbol probabilities and bit log-likelihood ratios without posterior sampling or explicit counterhypothesis lists. Numerical results for critically loaded MU-MIMO systems show that SMAP-KR provides an increasingly favorable performance-complexity tradeoff as the system dimension grows and can also effectively refine the output of existing detectors. For coded transmission, soft SMAP provides substantial block-error-rate gains over both LMMSE- and K-best-based soft detection.
发表机构
- Orange, Orange Innovation（法国电信，法国电信创新）
- CentraleSupélec-CNRS-Université Paris-Saclay（巴黎萨克雷大学中央理工-高等学院-CNRS）
机构由 AI 辅助整理，请以论文原文为准。
