Performance, Fairness, and Tradeoff in UAV Swarm Underlaid mmWave Cellular Networks With Directional Antennas

Abstract

Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this paper investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.

Performance Optimization in UAV‐Assisted Wireless Powered mmWave Networks for Emergency Communications

“…However, UAVenabled cellular architecture over mmWave frequency band has been recognized as one of the best solution for ondemand high data rate service. Therefore, the many existing works focused on UAV-assisted mmWave communications [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”

“…The authors of [35] derived analytical channel models to evaluate the benefit of mmWave links associated with UAVs, which were further assessed by using outage probability. The authors of [36] investigated the rate performance, fairness, and their tradeoff in UAV swarm connected to mmWave cellular networks, where all UAVs are located in a 3D area and distributed according to a homogeneous PPP. The authors of [37] studied beamforming training and tracking for a UAVassisted mmWave system.…”

Generalized Traffic Flow Model for Multi-Services Oriented UAV System

“…We now calculate the unknown parameter λ ij in (7). Given the initial input data value of λ 11 representing the average packet arrival rate for the telemetry data from a UAV of the poor subgroup, we have…”

“…Equipped with wireless devices, UAVs can gather/transmit data from sensing, monitoring, filming, etc. It is predicted that the variety of services offered by UAVs will increase exponentially in the near future [6], [7], which poses enormous challenges for future design and deployment of UAV system. Therefore, it is urgently required to develop a general traffic flow model to predict the traffic flows of the multi-services oriented UAV system for supporting extensive applications.…”

On Sum Rate Maximization Study for Cellular-Connected UAV Swarm Communications

“…On the other hand, beamforming technique is promising to mitigate the interference. Recently, we investigate the performances for millimeter wave (mmWave) cellularconnected UAV swarm communications with angle-dependent directional antennas in terms of sum rate, fairness index, maxmin rate and proportional fairness, whereby the UAVs and BS use fixed transmit power to transmit messages at mmWave bands [22]. However, the performances are still unknown for the traditional cellular-connected UAV swarm communications operated at sub-6 GHZ bands with the constraints of variable UAV transmit power and beamforming of antenna arrays.…”