  In this paper, the problems of user offloading and resource optimization are
jointly addressed to support ultra-reliable and low latency communications
(URLLC) in HetNets. In particular, a multi-tier network with a single macro
base station (MBS) and multiple overlaid small cell base stations (SBSs) is
considered that includes users with different latency and reliability
constraints. Modeling the latency and reliability constraints of users with
probabilistic guarantees, the joint problem of user offloading and resource
allocation (JUR) in a URLLC setting is formulated as an optimization problem to
minimize the cost of serving users for the MBS. In the considered scheme, SBSs
bid to serve URLLC users under their coverage at a given price, and the MBS
decides whether to serve each user locally or to offload it to one of the
overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low
complexity learning-based heuristic method (LHM) which includes a support
vector machine-based user association model and a convex resource optimization
(CRO) algorithm. To further reduce the delay, we propose an alternating
direction method of multipliers (ADMM)-based solution to the CRO problem.
Simulation results show that using LHM, the MBS significantly decreases the
spectrum access delay for users (by $\sim$ 93\%) as compared to JUR, while also
reducing its bandwidth and power costs in serving users (by $\sim$ 33\%) as
compared to directly serving users without offloading.
