SNR Prediction in Cellular Systems based on Channel Charting

We consider a machine learning algorithm to predict the Signal-to-Noise-Ratio (SNR) of a user transmission at a neighboring base station in a massive MIMO (mMIMO) cellular system. This information is needed for Handover (HO) decisions for mobile users. For SNR prediction, only uplink channel characteristics of users, measured in a serving cell, are used. Measuring the signal quality from the downlink signals of neighboring Base Stations (BSs) at the User Equipment (UE) becomes increasingly problematic in forthcoming mMIMO Millimeter-Wave (mmWave) 5G cellular systems, due to the high degree of directivity required from transmissions, and vulnerability of mm Wave signals to blocking. Channel Charting (CC) is a machine learning technique for creating a radio map based on radio measurements only, which can be used for radio-resource-management problems. A CC is a two-dimensional representation of the space of received radio signals. Here, we learn an annotation of the CC in terms of neighboring BS signal qualities. Such an annotated CC can be used by a BS serving a UE to first localize the UE in the CC, and then to predict the signal quality from neighboring BSs. Each BS first constructs a CC from a number of samples, determining similarity of radio signals transmitted from different locations in the network based on covariance matrices. Then, the BS learns a continuous function for predicting the vector of neighboring BS SNRs as a function of a 2D coordinate in the chart. The considered algorithm provides information for handover decisions without UE assistance. UE-power consuming neighbor measurements are not needed, and the protocol overhead for HO is reduced.