  Introduction of fifth generation (5G) wireless network technology has matched
the crucial need for high capacity and speed needs of the new generation mobile
applications. Recent advances in Artificial Intelligence (AI) also empowered 5G
cellular networks with two mainstreams as machine learning (ML) and deep
learning (DL) techniques. Our study aims to uncover the differences in
scientific impact for these two techniques by the means of statistical
bibliometrics. The performed analysis includes citation performance with
respect to indexing types, funding availability, journal or conference
publishing options together with distributions of these metrics along years to
evaluate the popularity trends in a detailed manner. Web of Science (WoS)
database host 2245 papers for ML and 1407 papers for DL-related studies. DL
studies, starting with 9% rate in 2013, has reached to 45% rate in 2022 among
all DL and ML-related studies. Results related to scientific impact indicate
that DL studies get slightly more average normalized citation (2.256) compared
to ML studies (2.118) in 5G, while SCI-Expanded indexed papers in both sides
tend to have similar citation performance (3.165 and 3.162 respectively).
ML-related studies those are indexed in ESCI show twice citation performance
compared to DL. Conference papers in DL domain and journal papers in ML domain
are superior in scientific interest to their counterparts with minor
differences. Highest citation performance for ML studies is achieved for year
2014, while this peak is observed for 2017 for DL studies. We can conclude that
both publication and citation rate for DL-related papers tend to increase and
outperform ML-based studies in 5G domain by the means of citation metrics.

Digital Libraries;Artificial Intelligence