5G　is　envisioned　to　have　an　artificial　intelligence　(AI)-empowerment　to　efficiently　plan,　manage　and　optimize　the　extremely　complex　network　by　leveraging　colossal　amount　of　data　(big　data)　generated　at　different　levels　of　the　network　architecture.　Cell　outages　and　congestion　pose　serious　threat　to　the　network　management.　Sleeping　cell　is　a　special　case　of　cell　outage　in　which　the　cell　provides　inferior　services　to　its　users.　This　peculiar　behavior　of　the　cell　is　particularly　challenging　to　detect　as　it　disguises　itself　from　the　network　monitoring　entity.　Inadequate　accuracy　and　high　false　alarms　are　two　major　constraints　of　state-of-the-art　approaches　for　the　anomaly-sleeping　cell　and　surge　in　user　traffic　activity　that　may　lead　to　congestion-detection　in　cellular　networks.　This　implies　squandering　of　scarce　resources　which　ultimately　results　in　increased　operational　expenditure　(OPEX)　while　disrupting　network＇s　quality　of　service　(QoS)　and　user＇s　quality　of　experience　(QoE).　Inspired　from　the　prominent　success　of　deep　learning　(DL)　technology　in　machine　learning　domain,　this　is　the　first　study　that　applies　DL　for　the　detection　of　abovementioned　anomalies.　We　utilized,　and　did　a　comprehensive　study　of,　L-layer　deep　feedforward　neural　network　fueled　by　real　call　detail　record　(CDR)　dataset　(big　data)　and　achieved　94.6%　accuracy　with　1.7%　false　positive　rate　(FPR),　that　are　remarkable　improvements　and　overcome　the　limitations　of　the　previous　studies.　The　preliminary　results　elucidate　the　feasibility　and　preeminence　of　our　proposed　anomaly　detection　framework.　©　2018　IEEE.