# Blind Construction of Angular Power Maps in Massive MIMO Networks

arXiv: https://arxiv.org/abs/2510.07071 · HTML: https://mlsanity.com/paper/2510.07071/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G](https://mlsanity.com/paper/2309.07460/) (2023)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [Triplet-Based Wireless Channel Charting: Architecture and Experiments](https://mlsanity.com/paper/2005.12242/) (2020)
- [Environment-Aware Hybrid Beamforming by Leveraging Channel Knowledge Map](https://mlsanity.com/paper/2206.08707/) (2022)
- [Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing](https://mlsanity.com/paper/2403.08323/) (2024)
- [UAV-aided Radio Map Construction Exploiting Environment Semantics](https://mlsanity.com/paper/2107.10574/) (2021)
- [Wireless Channel Charting: Theory, Practice, and Applications](https://mlsanity.com/paper/2304.08095/) (2023)
- [Quantized Radio Map Estimation Using Tensor and Deep Generative Models](https://mlsanity.com/paper/2303.01770/) (2023)
- [λ-MIMO: Massive MIMO via Modulo Sampling](https://mlsanity.com/paper/2210.10193/) (2022)
- [Constructing Indoor Region-based Radio Map without Location Labels](https://mlsanity.com/paper/2308.16759/) (2023)
- [Block-Diagonal Guided DBSCAN Clustering](https://mlsanity.com/paper/2404.01341/) (2024)
- [Trajectory Map-Matching in Urban Road Networks Based on RSS Measurements](https://mlsanity.com/paper/2502.01280/) (2025)

## Built on by (most cited first, 3 of 3)

- [Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environments](https://mlsanity.com/paper/2510.08015/) (2025)
- [Blind Radio Mapping via Spatially Regularized Bayesian Trajectory Inference](https://mlsanity.com/paper/2512.13701/) (2025)
- [Temporal Visual Semantics-Induced Human Motion Understanding with Large Language Models](https://mlsanity.com/paper/2512.22249/) (2025)

## Abstract (arXiv)

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.