Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks

Abstract

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.

Energy Efficiency and Throughput Optimization in 5G Heterogeneous Networks

“…The GA-based scheme performs better than the PSO and achieves a higher percentage of energy efficiency compared to the PSO. In another work [31], energy optimization is investigated in energyharvesting-enabled mmWave unmanned aerial vehicle (UAV) HNs. The results show that the performance of the GA and that of the PSO achieve comparable network energy efficiencies.…”

Livestock Management With Unmanned Aerial Vehicles: A Review

“…Limited resources such as: battery and hardware (memory and computation) Deployment of energyaware networks low power and lossy networks (LLT) [203]- [207] [208] and other technologies such as RPL [203] and IEEE 802.15.4 [206] Network management and hardware configuration difficulties The use of softwaredefined networks (SDN) and Network function virtualization (NVF) [209], [210], [219]- [222], [211]- [218] High node mobility leading to data loss and high network delay…”