  Cellular Internet-of-things (C-IoT) user equipments (UEs) typically transmit
frequent but small amounts of uplink data to the base station. Undergoing a
traditional random access procedure (RAP) to transmit a small but frequent data
presents a considerable overhead. As an antidote, preconfigured uplink
resources (PURs) are typically used in newer UEs, where the devices are
allocated uplink resources beforehand to transmit on without following the RAP.
A prerequisite for transmitting on PURs is that the UEs must use a valid timing
advance (TA) so that they do not interfere with transmissions of other nodes in
adjacent resources. One solution to this end is to validate the previously held
TA by the UE to ensure that it is still valid. While this validation is trivial
for stationary UEs, mobile UEs often encounter conditions where the previous TA
is no longer valid and a new one is to be requested by falling back on legacy
RAP. This limits the applicability of PURs in mobile UEs. To counter this
drawback and ensure a near-universal adoption of transmitting on PURs, we
propose new machine learning aided solutions for validation and prediction of
TA for UEs of any type of mobility. We conduct comprehensive simulation
evaluations across different types of communication environments to demonstrate
that our proposed solutions provide up to a 98.7% accuracy in predicting the
TA.
