A Novel Mataheuristic based Interference Alignment for K-User Interference Channel : A Comparative Study

TL;DR

This paper introduces a new interference alignment method for K-User MIMO channels using metaheuristics, comparing PSO, ABC, and their cooperative variants to improve optimization in wireless interference management.

Contribution

It proposes a novel metaheuristic-based IA scheme employing PSO and ABC, including cooperative versions, for better large-scale interference optimization.

Findings

Cooperative approaches outperform individual algorithms.

Metaheuristics effectively optimize interference alignment.

Proposed methods show promising results in large-scale scenarios.

Abstract

This paper presents a new Interference Alignment (IA) scheme for K-User Multiple Input Multiple Output (MIMO) Interference Channel (IC) based on two metaheuristics, namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm. Tackling interference is an essential issue in wireless communications to which Interference Alignment (IA) provides a promising solution. However, IA still lacks of explicit and straightforward design procedures. In fact, most of IA procedures aim to minimize a certain Interference Leakage (IL) which measures the effect of the interference on the network, this results in complex optimization tasks involving a large amount of decision variables, together with a problem of convergence of the IA solutions. In this paper the IA optimization is performed using PSO, ABC and their cooperative counterparts, more suitable for large scale optimization. A comparison between the four algorithms is also carried out. The cooperative proposed approaches seem to be promising.