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of fifth generation (5G) wireless network technology has matchedthe crucial need for high capacity and speed needs of the new generation mobileapplications. Recent advances in Artificial Intelligence (AI) also empowered 5Gcellular networks with two mainstreams as machine learning (ML) and deeplearning (DL) techniques. Our study aims to uncover the differences inscientific impact for these two techniques by the means of statisticalbibliometrics. The performed analysis includes citation performance withrespect to indexing types, funding availability, journal or conferencepublishing options together with distributions of these metrics along years toevaluate the popularity trends in a detailed manner. Web of Science (WoS)database host 2245 papers for ML and 1407 papers for DL-related studies. DLstudies, starting with 9% rate in 2013, has reached to 45% rate in 2022 amongall DL and ML-related studies. Results related to scientific impact indicatethat DL studies get slightly more average normalized citation (2.256) comparedto ML studies (2.118) in 5G, while SCI-Expanded indexed papers in both sidestend to have similar citation performance (3.165 and 3.162 respectively).ML-related studies those are indexed in ESCI show twice citation performancecompared to DL. Conference papers in DL domain and journal papers in ML domainare superior in scientific interest to their counterparts with minordifferences. Highest citation performance for ML studies is achieved for year2014, while this peak is observed for 2017 for DL studies. We can conclude thatboth publication and citation rate for DL-related papers tend to increase andoutperform ML-based studies in 5G domain by the means of citation metrics.

Machine Learning vs. Deep Learning in 5G Networks -- A Comparison of Scientific Impact