Title: Beyond Point Estimates: Likelihood-Based Full-Posterior Wireless Localization Show Chinese title

Title: 超越点估计：基于似然的全后验无线定位

Abstract: Modern wireless systems require not only position estimates, but also quantified uncertainty to support planning, control, and radio resource management. We formulate localization as posterior inference of an unknown transmitter location from receiver measurements. We propose Monte Carlo Candidate-Likelihood Estimation (MC-CLE), which trains a neural scoring network using Monte Carlo sampling to compare true and candidate transmitter locations. We show that in line-of-sight simulations with a multi-antenna receiver, MC-CLE learns critical properties including angular ambiguity and front-to-back antenna patterns. MC-CLE also achieves lower cross-entropy loss relative to a uniform baseline and Gaussian posteriors. alternatives under a uniform-loss metric. Show Chinese abstract

Abstract: 现代无线系统不仅需要位置估计，还需要量化不确定性以支持规划、控制和无线资源管理。 我们将定位问题表述为从接收机测量中对未知发射机位置的后验推断。 我们提出了蒙特卡洛候选似然估计（MC-CLE），该方法使用蒙特卡洛采样训练神经评分网络，以比较真实和候选发射机位置。 我们表明，在具有多天线接收机的视距仿真中，MC-CLE 学习到了包括角度模糊性和前后天线模式在内的关键特性。 相对于均匀基线和高斯后验分布，MC-CLE 在交叉熵损失方面表现更优。 在均匀损失度量下的其他替代方案。