Optimal Traffic Aggregation in Multi-RAT Heterogeneous Wireless Networks

TL;DR

This paper proposes a low complexity, utility-maximizing traffic aggregation method for multi-RAT heterogeneous wireless networks, significantly improving throughput by leveraging multi-link aggregation capabilities.

Contribution

It introduces a novel, practical solution for traffic aggregation that maximizes network utility in multi-RAT HetNets, including closed-form solutions for specific cases.

Findings

Substantial throughput gains over single RAT/AP selection.

Effective decentralized implementation with message exchanges.

Versatile utility maximization framework covering various fairness criteria.

Abstract

Traffic load balancing and radio resource management is key to harness the dense and increasingly heterogeneous deployment of next generation "$5$G" wireless infrastructure. Strategies for aggregating user traffic from across multiple radio access technologies (RATs) and/or access points (APs) would be crucial in such heterogeneous networks (HetNets), but are not well investigated. In this paper, we develop a low complexity solution for maximizing an $\alpha$-optimal network utility leveraging the multi-link aggregation (simultaneous connectivity to multiple RATs/APs) capability of users in the network. The network utility maximization formulation has maximization of sum rate ($\alpha=0$), maximization of minimum rate ($\alpha \to \infty$), and proportional fair ($\alpha=1$) as its special cases. A closed form is also developed for the special case where a user aggregates traffic from at most two APs/RATs, and hence can be applied to practical scenarios like LTE-WLAN aggregation (LWA) and LTE dual-connectivity solutions. It is shown that the required objective may also be realized through a decentralized implementation requiring a series of message exchanges between the users and network. Using comprehensive system level simulations, it is shown that optimal leveraging of multi-link aggregation leads to substantial throughput gains over single RAT/AP selection techniques.