Abstract

Recently, unmanned aerial vehicle (UAV) communications have attracted great research interest. Due to the limited on-board energy, the optimization of energy efficiency (EE) is critical for UAV communications. In this paper, we propose an EE maximization scheme for UAV swarm-enabled small cell networks using large-scale channel state information at the transmitter (CSIT). The proposed scheme provides an agile coordination strategy for the UAVs in a swarm under energy constraints. We first formulate the EE maximization problem, where the objective function is defined as the ratio of the ergodic total data size to the total energy consumption. After that, an accurate approximation is derived to remove the intractable expectation operator in the objective function. As the newly formulated problem is non-convex, we decompose it into two subproblems to optimize the transmit power and the hovering time in an iterative way. Further by leveraging the max-min and linear optimization tools, both subproblems are efficiently solved. Simulation results demonstrate the superiority of our EE maximization scheme.

Highlights

  • Nowadays, unmanned aerial vehicle (UAV) communications have attracted great research interest [1], [2]

  • We formulate an EE maximization problem for UAV swarm-enabled small cell networks with large-scale channel state information at the transmitter (CSIT), where the objective is to maximize the ratio of the ergodic total data size to the total energy consumption

  • We assume each mobile terminals (MTs) equips with M = 6 receive antennas, the number of UAVs in a swarm is set as K = 6 and the number of MTs is set as N = 10

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Summary

INTRODUCTION

Nowadays, unmanned aerial vehicle (UAV) communications have attracted great research interest [1], [2]. To characterize a realistic communication environment, a composite channel model was considered in the design of UAV swarm-enabled small cell networks [25], [26]. This model considers both the line-of-sight (LOS) and non-line-ofsight (NLOS) channel elements, so that it is more practical than the simplified models. We formulate an EE maximization problem for UAV swarm-enabled small cell networks with large-scale CSIT, where the objective is to maximize the ratio of the ergodic total data size to the total energy consumption.

SYSTEM MODEL
PROBLEM DECOMPOSITION
HOVERING TIME SCHEDULING
SIMULATION RESULTS
CONCLUSION
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