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We consider machine learning for intra cell beam handovers in mmWave 5GNR systems by leveraging Channel Charting (CC). We develop a base station centric approach for predicting the Signal-to-Noise-Ratio (SNR) of beams. Beam SNRs are predicted based on measured signal at the BS without the need to exchange information with UEs. In an offline training phase, we construct a beam-specific dimensionality reduction of Channel State Information (CSI) to a low-dimensional CC, annotate the CC with beam-wise SNRs and then train SNR predictors for different target beams. In the online phase, we predict target beam SNRs. K-nearest neighbors, Gaussian Process Regression and Neural Network based prediction are considered. Based on SNR difference between the serving and target beams a handover can be decided. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. SNR prediction accuracy of average root mean square error less than 0.3 dB is achieved.Index Terms-beam-management, channel charting, SNR prediction.

We are interested in deducing whether two user equipments (UEs) in a cellular system are at nearby physical locations from measuring similarity of their channel state information (CSI). This becomes essential for fingerprinting localization as well as for channel charting. A channel chart is a low dimensional (e.g., 2-dimensional) radio map based on CSI measurements only, which is created using self-supervised machine learning techniques. Analyzing CSI in terms of the angledelay power profile (ADPP) takes advantage of the uniqueness of the multipath channel between the base station and the UE over the geographical region of interest. We consider super-resolution features in the angle and delay domains in massive multiple-input multiple-output (MIMO) systems and consider the earth-mover distance (EMD) to measure the distance between two features. Simulation results based on the DeepMIMO data set show that the super-resolution ADPP features with EMD leads to a better quality channel chart as compared to other CSI features and distances from the literature.

We consider a machine learning approach to perform best beam prediction in Non-Standalone Millimeter Wave (mmWave) Systems utilizing Channel Charting (CC). The approach reduces communication overheads and delays associated with initial access and beam tracking in 5G New Radio (NR) systems. The network has a mmWave and a sub-6 GHz component. We devise a Base Station (BS) centric approach for best mmWave beam prediction, based on Channel State Information (CSI) measured at the sub-6 GHz BS, with no need to exchange information with UEs. In a training phase, we collect CSI at the sub-6 GHz BS from sample UEs, and construct a dimensional reduction of the sample CSI, called a CC. We annotate the CC with best beam information measured at a mmWave BS for the sample UEs, assuming autonomous beamformer at the UE side. A beam predictor is trained based on this information, connecting any sub-6 GHz CSI with a predicted best mmWave beam. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetic spatially consistent CSI. With a neural network predictor, we obtain 91% accuracy for predicting best beam and 99% accuracy for predicting one of two best beams. The accuracy of CC based beam prediction is indistinguishable from true location based beam prediction.