Optimal Hierarchical Radio Resource Management for HetNets with Flexible Backhaul

TL;DR

This paper introduces a novel two-timescale optimization framework for radio resource management in HetNets with flexible backhaul, significantly reducing infrastructure costs while maximizing network utility.

Contribution

It formulates a non-convex stochastic optimization problem for cross-layer RRM in HetNets with flexible backhaul and proposes a globally optimal, scalable iterative algorithm.

Findings

The proposed algorithm converges to the global optimum.

It achieves significant performance gains over baseline methods.

The solution is robust to backhaul signaling latency.

Abstract

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization which jointly optimizes flow control, routing, interference mitigation and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signalling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signalling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.