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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 as AI -enabled S parse Data based M mWave cell disc O very 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 …