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.

翻译：现代无线系统不仅需要位置估计，还需要量化的不确定性以支持规划、控制和无线资源管理。本文将定位问题表述为根据接收机测量值对未知发射机位置进行后验推断。我们提出蒙特卡洛候选似然估计（MC-CLE）方法，该方法通过蒙特卡洛采样训练神经评分网络，以比较真实与候选发射机位置。研究表明，在多天线接收机的视距传播仿真中，MC-CLE能够学习包括角度模糊性与天线前后向辐射模式在内的关键特性。在均匀损失度量下，相较于均匀基线分布与高斯后验分布，MC-CLE还实现了更低的交叉熵损失。