CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach
This addresses the problem of precise user localization for 5G network operators, but it is incremental as it applies existing learning methods like extreme learning machines to a specific domain.
The paper tackles outdoor localization for a mobile user in a 5G Massive MIMO system by using a learning-based approach that infers location from the sample spatial covariance matrix of CSI, achieving validated performance benchmarks on experimental data from a Huawei testbed.
We report on experimental results on the use of a learning-based approach to infer the location of a mobile user of a cellular network within a cell, for a 5G-type Massive multiple input, multiple output (MIMO) system. We describe how the sample spatial covariance matrix computed from the CSI can be used as the input to a learning algorithm which attempts to relate it to user location. We discuss several learning approaches, and analyze in depth the application of extreme learning machines, for which theoretical approximate performance benchmarks are available, to the localization problem. We validate the proposed approach using experimental data collected on a Huawei 5G testbed, provide some performance and robustness benchmarks, and discuss practical issues related to the deployment of such a technique in 5G networks.