AI-Assisted Joint Search Approach for mmwave Cell Discovery Using Sparse MDT Data

This is the authors' abstract. We don't add key points for this paper.

High signal directivity and sensitivity to blockages make the mmWave base-station (BS) discovery a challenging problem in emerging networks. Existing solutions that rely on the exhaustive periodic-beam-sweeping have high latency and low mmWave cell discovery rate. Recent AI-based solutions address the above problems but rely on impractical assumption of having complete minimization of drive test (MDT) reports traces. This paper is the first to present an AI-based framework that can utilize very sparse MDT style data to enable NLoSaware low latency mmWave cell discovery, hereafter referred to asAI-enabledSparse Data basedMmWave cell discOvery and EN-DC activation framework (AISMO). We first gather MDT traces of mmWave users containing signal-strength and Radio-Link-Failure (RLF) indicators. We then augment this highly sparse MDT data using a variety of interpolation, domain knowledge, and AI-based techniques to create augmented mmWave coverage maps (mW-Amaps) while incorporating the NLoS conditions through the RLF traces that are inherently embedded in the data. The mW-Amaps are then used by the macro-BS to determine the optimal mmWave cell for a given user location. Results show that our proposed Weighted Nearest Neighbor Count (WNNC) approach outperforms other data sparsity alleviation techniques for mW-Amap creation with accuracy of 96%. Second best in terms of accuracy is a deep learning-based solution, that has almost 30xfaster training time than the WNNC. To evaluate AISMO's performance in a realistic 5G deployment scenario, we present a case study where AISMO is used to enable E-UTRAN New-Radio Dual-Connectivity (EN-DC) between macro-BS and mmWave small cells.