  The performance of user-centric ultra-dense networks (UCUDNs) hinges on the
Service zone (Szone) radius, which is an elastic parameter that balances the
area spectral efficiency (ASE) and energy efficiency (EE) of the network.
Accurately determining the Szone radius requires the precise location of the
user equipment (UE) and data base stations (DBSs). Even a slight error in
reported positions of DBSs or UE will lead to an incorrect determination of
Szone radius and UE-DBS pairing, leading to degradation of the UE-DBS
communication link. To compensate for the positioning error impact and improve
the ASE and EE of the UCUDN, this work proposes a data-driven optimization and
error compensation (DD-OEC) framework. The framework comprises an additional
machine learning model that assesses the impact of residual errors and
regulates the erroneous datadriven optimization to output Szone radius,
transmit power, and DBS density values which improve network ASE and EE. The
performance of the framework is compared to a baseline scheme, which does not
employ the residual, and results demonstrate that the DD-OEC framework
outperforms the baseline, achieving up to a 23% improvement in performance.
