UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

TL;DR

This paper proposes a novel UAV swarm-based virtual antenna array approach using collaborative beamforming and multi-objective optimization to mitigate uplink interference, improve data transmission efficiency, and conserve energy in aerial-terrestrial networks.

Contribution

It introduces a joint optimization framework combining UAV positioning, beamforming weights, and data transmission sequencing, solved with an enhanced evolutionary algorithm, to effectively reduce interference and enhance network performance.

Findings

CNSGA-II outperforms other algorithms in solving the MOOP.

Significant reduction in UAV-induced interference to non-receiving base stations.

Improved energy efficiency and data transmission performance in UAV-assisted networks.

Abstract

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs.