A Distributed Approach to Interference Alignment in OFDM-based Two-tiered Networks

TL;DR

This paper introduces a distributed interference alignment scheme for LTE-A OFDMA two-tier networks, enabling self-organizing small cells to mitigate cross-tier interference and improve spectral efficiency with minimal CSI requirements.

Contribution

It proposes a novel cognitive interference alignment method that is fully distributed, requiring only local CSI, and enhances spectral efficiency in two-tier OFDMA networks.

Findings

Significant spectral efficiency gains over traditional TDM approaches.

Robust performance even with imperfect channel state information.

Effective interference mitigation in a self-organizing small cell network.

Abstract

In this contribution, we consider a two-tiered network and focus on the coexistence between the two tiers at physical layer. We target our efforts on a long term evolution advanced (LTE-A) orthogonal frequency division multiple access (OFDMA) macro-cell sharing the spectrum with a randomly deployed second tier of small-cells. In such networks, high levels of co-channel interference between the macro and small base stations (MBS/SBS) may largely limit the potential spectral efficiency gains provided by the frequency reuse 1. To address this issue, we propose a novel cognitive interference alignment based scheme to protect the macro-cell from the cross-tier interference, while mitigating the co-tier interference in the second tier. Remarkably, only local channel state information (CSI) and autonomous operations are required in the second tier, resulting in a completely self-organizing approach for the SBSs. The optimal precoder that maximizes the spectral efficiency of the link between each SBS and its served user equipment is found by means of a distributed one-shot strategy. Numerical findings reveal non-negligible spectral efficiency enhancements with respect to traditional time division multiple access approaches at any signal to noise (SNR) regime. Additionally, the proposed technique exhibits significant robustness to channel estimation errors, achieving remarkable results for the imperfect CSI case and yielding consistent performance enhancements to the network.