Impact of Dual Slope Path Loss on User Association in HetNets

TL;DR

This paper investigates how dual slope path loss models impact user association strategies in HetNets, revealing that traditional biasing and decoupling techniques may be less effective under more realistic propagation conditions.

Contribution

It provides new insights into user association in HetNets by analyzing the effects of dual slope path loss models, which better reflect real-world environments, on biasing and decoupling strategies.

Findings

Dual slope models alter the biasing tradeoffs.

Optimal biasing for median rate may not benefit all users.

User association gains are sensitive to path loss exponents.

Abstract

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.