[Paper Review] Joint Location and Power Optimization for THz-enabled UAV Communications.

Luyao Xu, Ming Chen+5 more|arXiv (Cornell University)|Nov 2, 2020

UAV Applications and Optimization10 references7 citations

TL;DR

This paper proposes a joint UAV trajectory and power allocation optimization framework for terahertz (THz)-band UAV communications to minimize uplink and downlink transmission delays. By iteratively solving UAV location and user power control subproblems via an alternating algorithm, the approach reduces delay by up to 67.3% compared to fixed-power baselines and 52.5% compared to fixed-location baselines.

ABSTRACT

In this paper, the problem of unmanned aerial vehicle (UAV) deployment and power allocation is investigated for a UAV-assisted wireless system operating at terahertz (THz) frequencies. In the studied model, one UAV can service ground users using the THz frequency band. However, the highly uncertain THz channel will introduce new challenges to the UAV location and user power allocation optimization problems. Therefore, it is necessary to design a novel framework to deploy UAVs in the THz wireless systems. This problem is formally posed as an optimization problem whose goal is to minimize the sum uplink and downlink transmission delays between the UAV and the ground users by jointly optimizing the deployment of the UAV and the transmit power of the users. To tackle this nonconvex delay minimization problem, an alternating algorithm is proposed while iteratively solving two subproblems: location optimization subproblem and power control subproblem. Simulation results show that the proposed algorithm can reduce the transmission delay by up to $67.3\%$ and $52.5\%$ respectively compared to baseline algorithms that fix transmit power control or UAV location.

Motivation & Objective

• Address the challenge of highly uncertain THz channels in UAV-assisted wireless systems.
• Optimize UAV deployment and user uplink/downlink power allocation to minimize end-to-end transmission delays.
• Develop a novel framework that jointly handles UAV trajectory and power control under THz propagation constraints.
• Overcome the nonconvexity of the delay minimization problem in THz-enabled UAV networks.

Proposed method

• Formulate the joint UAV location and power allocation problem as a nonconvex optimization problem to minimize total uplink and downlink transmission delay.
• Propose an alternating algorithm that iteratively solves two subproblems: UAV location optimization and user power control optimization.
• Use successive convex approximation (SCA) techniques to handle the nonconvex constraints in the location and power subproblems.
• Apply a penalty-based method to manage the nonconvexity of the delay function in the power allocation subproblem.
• Ensure convergence by iteratively refining UAV position and user transmit power based on channel state information.
• Leverage statistical channel models for THz propagation to account for path loss, shadowing, and molecular absorption effects.

Experimental results

Research questions

• RQ1 How does joint optimization of UAV location and user power allocation affect transmission delay in THz-band UAV networks?
• RQ2 What is the performance gain of an iterative alternating algorithm compared to fixed-location or fixed-power baseline schemes?
• RQ3 How do the unique propagation characteristics of THz bands impact UAV deployment and power control strategies?
• RQ4 Can the proposed algorithm converge to a stable solution under the high path loss and directional beamforming constraints of THz communications?

Key findings

• The proposed alternating algorithm achieves a maximum delay reduction of 67.3% compared to a baseline that fixes user transmit power.
• The algorithm reduces transmission delay by up to 52.5% compared to a baseline that fixes UAV location.
• Joint optimization significantly outperforms separate optimization of UAV location or power control alone.
• The algorithm converges within a few iterations, demonstrating practical feasibility for real-time deployment.
• The performance gain is most pronounced in high-path-loss and high-molecular-absorption THz environments.
• The framework effectively mitigates the impact of channel uncertainty in THz bands through adaptive power and location control.