On/Off Macrocells and Load Balancing in Heterogeneous Cellular Networks

TL;DR

This paper investigates the joint optimization of load balancing and interference coordination in HetNets using almost blank subframes, demonstrating significant rate improvements for cell edge users through a convex relaxation approach.

Contribution

It introduces a convex relaxation for joint cell association and blank resource optimization, showing near-binary solutions and highlighting the importance of combined offloading and interference management.

Findings

Optimal association varies significantly with macrocell on/off states.

Aggressive offloading is possible with macrocell blanking, improving cell edge rates.

Rate gains of 5-10x for worst-off users with joint optimization.

Abstract

The rate distribution in heterogeneous networks (HetNets) greatly benefits from load balancing, by which mobile users are pushed onto lightly-loaded small cells despite the resulting loss in SINR. This offloading can be made more aggressive and robust if the macrocells leave a fraction of time/frequency resource blank, which reduces the interference to the offloaded users. We investigate the joint optimization of this technique - referred to in 3GPP as enhanced intercell interference coordination (eICIC) via almost blank subframes (ABSs) - with offloading in this paper. Although the joint cell association and blank resource (BR) problem is nominally combinatorial, by allowing users to associate with multiple base stations (BSs), the problem becomes convex, and upper bounds the performance versus a binary association. We show both theoretically and through simulation that the optimal solution of the relaxed problem still results in an association that is mostly binary. The optimal association differs significantly when the macrocell is on or off; in particular the offloading can be much more aggressive when the resource is left blank by macro BSs. Further, we observe that jointly optimizing the offloading with BR is important. The rate gain for cell edge users (the worst 3-10%) is very large - on the order of 5-10x - versus a naive association strategy without macrocell blanking.