无位置标签的海量天线网络中，用隐马尔可夫模型构建角度功率图。
Blind Construction of Angular Power Maps in Massive MIMO Networks
- 用隐马尔可夫模型关联移动体轨迹与信道演化，实现无位置标签建模
- 实测数据下平均定位误差18米，仅依赖单个服务小区信号
- 适用于基站分布不均场景，为无线资源管理提供新路径
在海量多输入多输出（massive MIMO）网络中，信道状态信息（CSI）获取极具挑战。无线地图通过减少在线CSI采集，为无线资源管理提供了可行方案。然而，传统无线地图构建方法需带位置标签的CSI数据，实际应用困难。本文研究基于大规模时序CSI数据的无监督角度功率图构造，数据来自未标注位置的massive MIMO网络。构建隐马尔可夫模型（HMM），将移动体隐藏轨迹与massive MIMO信道演化关联，从而估计移动体位置，实现角度功率图生成。理论分析表明，在泊松分布基站且匀速直线运动条件下，定位误差的克拉美-罗下界（CRLB）可在任意信噪比下趋近于零；而当基站受限于有限区域时，即使有无限独立测量，误差仍保持非零。基于真实多小区massive MIMO网络采集的参考信号接收功率（RSRP）数据，实现了18米的平均定位误差，尽管测量主要来自单一服务小区。
原文摘要 · Abstract (English)
Channel state information (CSI) acquisition is a challenging problem in massive multiple-input multiple-output (MIMO) networks. Radio maps provide a promising solution for radio resource management by reducing online CSI acquisition. However, conventional approaches for radio map construction require location-labeled CSI data, which is challenging in practice. This paper investigates unsupervised angular power map construction based on large timescale CSI data collected in a massive MIMO network without location labels. A hidden Markov model (HMM) is built to connect the hidden trajectory of a mobile with the CSI evolution of a massive MIMO channel. As a result, the mobile location can be estimated, enabling the construction of an angular power map. We show that under uniform rectilinear mobility with Poisson-distributed base stations (BSs), the Cramer-Rao Lower Bound (CRLB) for localization error can vanish at any signal-to-noise ratios (SNRs), whereas when BSs are confined to a limited region, the error remains nonzero even with infinite independent measurements. Based on reference signal received power (RSRP) data collected in a real multi-cell massive MIMO network, an average localization error of 18 meters can be achieved although measurements are mainly obtained from a single serving cell.
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