Common Throughput Maximization in UAV-Enabled OFDMA Systems with Delay Consideration (1801.00444v2)

Abstract: The use of unmanned aerial vehicles (UAVs) as communication platforms is of great practical significance in future wireless networks, especially for on-demand deployment in temporary events and emergency situations. Although prior works have shown the performance improvement by exploiting the UAV's mobility, they mainly focus on delay-tolerant applications. As delay requirements fundamentally limit the UAV's mobility, it remains unknown whether the UAV is able to provide any performance gain in delay-constrained communication scenarios. Motivated by this, we study in this paper a UAV-enabled orthogonal frequency division multiple access (OFDMA) network where a UAV is dispatched as a mobile base station (BS) to serve a group of users on the ground. We consider a minimum-rate ratio (MRR) for each user, defined as the minimum instantaneous rate required over the average achievable throughput, to flexibly adjust the percentage of its delay-constrained data traffic. Under a given set of constraints on the users' MRRs, we aim to maximize the minimum average throughput of all users by jointly optimizing the UAV trajectory and OFDMA resource allocation. First, we show that the max-min throughput in general decreases as the users' MRR constraints become more stringent, which reveals a fundamental throughput-delay tradeoff in UAV-enabled communications. Next, we propose an iterative parameter-assisted block coordinate descent method to optimize the UAV trajectory and OFDMA resource allocation alternately, by applying the successive convex optimization and the Lagrange duality, respectively. Furthermore, an efficient and systematic UAV trajectory initialization scheme is proposed based on a simple circular trajectory. Finally, simulation results are provided to verify our theoretical findings and demonstrate the effectiveness of our proposed designs.

• The paper demonstrates a novel iterative approach that jointly optimizes UAV trajectory and OFDMA resource allocation under stringent delay constraints.
• It reveals that tighter delay requirements, quantified by higher minimum-rate ratios, lead to a significant decline in maximum average throughput.
• Simulation results confirm that the proposed method outperforms traditional static and circular UAV trajectories, offering enhanced performance in dynamic scenarios.

Analytical Study of Throughput Maximization in UAV-Enabled OFDMA Systems with Delay Constraints

The examined paper focuses on a crucial aspect of contemporary wireless communication systems: the integration of unmanned aerial vehicles (UAVs) into orthogonal frequency division multiple access (OFDMA) networks, specifically considering the challenge of throughput maximization under delay constraints. UAVs, due to their flexibility and mobility, are being increasingly proposed as enhancers of existing communication infrastructures, especially in scenarios demanding rapid deployment or temporary coverage, such as in disaster recovery or large-scale events.

Core Contributions

In this work, the authors address the challenge of ensuring effective data throughput while meeting diverse quality-of-service (QoS) constraints, specifically focusing on delay-sensitive applications. Previous studies have mainly emphasized delay-tolerant systems, but this paper extends the analysis to scenarios where delay constraints significantly limit UAV mobility.

Methodology

The central objective of the research is to maximize the minimum average throughput across multiple users served by a mobile UAV base station. Constraints are imposed through a minimum-rate ratio (MRR) for each user, representing the required balance between instantaneous rate and average throughput. The research employs an iterative optimization framework:

1. Trajectory Optimization: The paper proposes a block coordinate descent approach, where the UAV trajectory is optimized alternately with OFDMA resource allocation. This iterative method utilizes successive convex optimization and the Lagrange duality to effectively solve the non-convex problem.
2. Resource Allocation: An efficient allocation scheme for bandwidth and power resources is devised to adapt to dynamically changing user demands. To ensure computational feasibility, a parameter-assisted strategy is introduced to initialize and guide the optimization process.
3. Simulation and Verification: Utilizing simulations, the theoretical findings are substantiated, highlighting the efficacy of the proposed approach in balancing throughput and delay constraints.

Key Findings

• Tradeoff Analysis: The paper provides insight into the fundamental tradeoff between throughput and delay. It is observed that as delay constraints tighten (reflected in higher MRR values), the max-min throughput experiences a significant decline. This elucidates the challenges posed by stringent QoS requirements in mobile drone-based networks.
• Algorithm Performance: The proposed algorithm demonstrates substantial improvements over traditional static UAV and initial circular trajectory strategies, particularly at lower delay constraints, where UAV mobility plays a crucial role in enhancing system performance.

Implications and Future Directions

Pragmatically, the findings of this paper suggest potential improvements in the deployment strategies of UAV-enabled communication systems, enabling more efficient data handling in scenarios with rigid delay constraints. Moreover, the theoretical insights into the throughput-delay tradeoff are invaluable for designing future UAV communication protocols.

Looking ahead, the paper lays the groundwork for exploring multi-UAV scenarios and complex network architectures where interference patterns could further complicate the resource allocation and trajectory optimization problems. Additionally, incorporating energy-efficiency considerations could propel further advancements, addressing the endurance issues associated with UAV operations. The implementation of cross-layer designs that integrate physical and network layer constraints remains a promising avenue for extending this work.

In essence, this paper contributes significantly to the ongoing discourse on UAV-enabled communications, providing a nuanced understanding of delay-dependent throughput maximization in rapidly evolving wireless environments.