  Intelligent load balancing is essential to fully realize the benefits of
dense heterogeneous networks. Current techniques have largely been studied with
single slope path loss models, though multi-slope models are known to more
closely match real deployments. This paper develops insight into the
performance of biasing and uplink/downlink decoupling for user association in
HetNets with dual slope path loss models. It is shown that dual slope path loss
models change the tradeoffs inherent in biasing and reduce gains from both
biasing and uplink/downlink decoupling. The results show that with the dual
slope path loss models, the bias maximizing the median rate is not optimal for
other users, e.g., edge users. Furthermore, optimal downlink biasing is shown
to realize most of the gains from downlink-uplink decoupling. Moreover, the
user association gains in dense networks are observed to be quite sensitive to
the path loss exponent beyond the critical distance in a dual slope model.
