On Feasibility of Generalized Interference Alignment with Partial Interference Cancelation

TL;DR

This paper introduces a new interference alignment strategy called PIC-IA that focuses on canceling only the most significant interference signals, expanding feasibility beyond traditional IA methods.

Contribution

It proposes and analyzes the feasibility of a partial interference cancelation-based IA strategy that is less limited by the number of users compared to conventional IA.

Findings

PIC-IA is feasible when removing up to N_t+N_r-2 interference signals per receiver.

Feasibility is independent of the total number of users K.

The strategy generalizes conventional IA by allowing partial interference cancelation.

Abstract

We study a new IA strategy which is referred to as "Partial Interference Cancelation-based Interference Alignment" (PIC-IA). Unlike the conventional IA strategy, PIC-IA does not strive to eliminate interference from all users. Instead, it aims to remove the most significant interference signals. This PIC-IA strategy generalizes the conventional IA concept by addressing partial, instead of complete, interference cancelation. The feasibility of this new strategy is studied in this paper. Our results show that for a symmetric, single-stream system with $N_t$ transmit antennas and $N_r$ receive antennas, the PIC-IA is feasible when the number of significant interference signals to be removed at each receiver is no more than $N_t+N_r-2$, no matter how many users are in the network. This is in sharp contrast to the conventional IA whose feasibility is severely limited by the number of users $K$.