[Paper Review] Energy-Efficient Data Collection and Wireless Power Transfer Using A MIMO Full-Duplex UAV

Jiancao Hou, Zhaohui Yang+1 more|arXiv (Cornell University)|Nov 22, 2018

Energy Harvesting in Wireless Networks17 references4 citations

TL;DR

This paper proposes an energy-efficient framework for simultaneous data collection and wireless power transfer (WPT) using a MIMO full-duplex (FD) UAV, jointly optimizing UAV trajectory, WPT beamforming, and uplink transmission power to minimize total UAV energy consumption. The method employs an iterative successive convex approximation algorithm to address non-convexity, achieving superior energy efficiency with low computational complexity and fast convergence compared to benchmarks.

ABSTRACT

In this paper, we propose a novel energy-efficient data collection and wireless power transfer (WPT) framework for internet of things (IoT) applications, via a multiple-input multiple-output (MIMO) full-duplex (FD) unmanned aerial vehicle (UAV). To exploit the benefits of UAV-enabled WPT and MIMO FD communications, we allow the MIMO FD UAV charge low-power IoT devices while at the same time collect data from them. With the aim of saving the total energy consumed at the UAV, we formulate an energy minimization problem by taking the FD hardware impairments, the number of uploaded data bits, and the energy harvesting causality into account. Due to the non-convexity of the problem in terms of UAV trajectory and transmit beamforming for WPT, tracking the global optimality is quite challenge. Alternatively, we find a local optimal point by implementing the proposed iterative search algorithm combining with successive convex approximation techniques. Numerical results show that the proposed approach can lead to superior performance compared with other benchmark schemes with low computational complexity and fast convergence.

Motivation & Objective

• To address the energy constraints of low-power IoT devices in large-scale IoT deployments by enabling simultaneous data collection and wireless energy transfer via UAVs.
• To minimize total energy consumption at the UAV by jointly optimizing trajectory, WPT beamforming, and uplink transmission power under realistic hardware impairments.
• To account for the non-convex nature of the optimization problem due to UAV trajectory and transmit covariance matrix, which complicates global optimality.
• To develop a low-complexity, fast-converging algorithm that achieves near-optimal performance in practical UAV-enabled IoT networks.
• To demonstrate the superiority of the proposed scheme over benchmark trajectories in terms of energy efficiency and convergence speed.

Proposed method

• Formulates a non-convex energy minimization problem incorporating UAV hardware impairments, data rate requirements, and energy harvesting causality constraints.
• Uses successive convex approximation (SCA) to iteratively approximate the non-convex problem into a sequence of convex subproblems for tractable solution.
• Employs an iterative search algorithm to jointly optimize UAV trajectory (horizontal position over time slots), WPT beamforming (covariance matrix), and IoT device uplink transmit power.
• Models the UAV as a MIMO FD system with M antennas, serving up to K single-antenna IoT devices, using Rician fading channels for both uplink and self-interference.
• Incorporates realistic hardware impairments via distortion coefficients κ and β, and models propulsion energy as a function of flight time and distance.
• Divides the total time T into N equal time slots, assuming quasi-static channels within each slot, enabling discrete-time trajectory optimization.

Experimental results

Research questions

• RQ1 How can a MIMO full-duplex UAV simultaneously enable energy harvesting and data collection from multiple low-power IoT devices while minimizing total energy consumption?
• RQ2 What is the impact of hardware impairments on the performance of a full-duplex UAV in a WPT and data collection scenario?
• RQ3 How does the joint optimization of UAV trajectory, WPT beamforming, and uplink power improve energy efficiency compared to fixed or heuristic trajectories?
• RQ4 Can a low-complexity iterative algorithm achieve fast convergence and near-optimal performance in this non-convex optimization problem?
• RQ5 How does the UAV's moving time affect the trade-off between propulsion energy and WPT/communication energy in the system?

Key findings

• The proposed algorithm achieves the lowest UAV energy consumption compared to two benchmark schemes, demonstrating superior energy efficiency.
• The optimized UAV trajectory lies between the two benchmarks, hovering closer to IoT devices with higher data demand and minimizing flight distance.
• For UAV moving time t < 1.32 s, benchmark 2 (linear path) consumes less energy than benchmark 1 (central hover), due to shorter travel distance.
• For t > 1.32 s, benchmark 1 (central hover) becomes more energy-efficient due to better WPT and data collection gains from central positioning.
• The iterative algorithm converges in approximately 4 complete loops for all tested moving times, indicating low computational complexity.
• The system achieves fast convergence and low computational cost, making it suitable for real-time deployment in dynamic IoT environments.