  Grant-Free (GF) access has been recognized as a promising candidate for
Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF
access, URLLC still may not effectively gain high reliability and
millimeter-level latency, simultaneously. This is because the network load is
typically time-varying and not known to the base station (BS), and thus, the
resource allocated for GF access cannot well adapt to variations of the network
load, resulting in low resource utilization efficiency under light network load
and leading to severe collisions under heavy network load. To tackle this
problem, we propose a multi-tier-driven computing framework and the associated
algorithms for URLLC to support users with different QoS requirements.
Especially, we concentrate on K - repetition GF access in light of its
simplicity and well-balanced performance for practical systems. In particular,
our framework consists of three tiers of computation, namely network-load
learning, network-load prediction, and adaptive resource allocation. In the
first tier, the BS can learn the network-load information from the states
(success, collision, and idle) of random-access resources in terms of resource
blocks (RB) and time slots. In the second tier, the network-load variation is
effectively predicted based on estimation results from the first tier. Finally,
in the third tier, by deriving and weighing the failure probabilities of
different groups of users, their QoS requirements, and the predicted network
loads, the BS is able to dynamically allocate sufficient resources
accommodating the varying network loads. Simulation results show that our
proposed approach can estimate the network load more accurately compared with
the baseline schemes. Moreover, our adaptive resource allocation offers an
effective way to enhance the QoS for different URLLC services, simultaneously.
