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Throughput and Fairness Trade-off Balancing for UAV-Enabled Wireless Communication Systems

Kejie Ni, Jingqing Wang, Wenchi Cheng, Wei Zhang

TL;DR

This work tackles the throughput–fairness–QoS trade-off in UAV-enabled wireless systems for 6G. It introduces a tunable weighted throughput function with weights ω_k[n] = exp(-α R_k[n]) / ∑_i exp(-α R_i[n]), enabling adjustable fairness via the parameter α, and proves key properties of the induced function H_α(x). The authors formulate two optimization problems—Condition 1 (α R_k[n] ≤ 1) and Condition 2 (α → ∞)—and develop an iterative, convex-approximation algorithm that alternates between joint bandwidth/power allocation and UAV trajectory design, for both problem structures. Numerical results show that increasing α shifts resources toward poorer channels, reduces throughput disparity, and yields better QoS provisioning and stability, at the cost of overall system throughput; results are benchmarked against standard max-min schemes. The proposed framework provides a practical, controllable mechanism to balance system throughput and user fairness in UAV-assisted networks, with potential impact on dynamic deployment scenarios and QoS-sensitive applications.

Abstract

Given the imperative of 6G networks' ubiquitous connectivity, along with the inherent mobility and cost-effectiveness of unmanned aerial vehicles (UAVs), UAVs play a critical role within 6G wireless networks. Despite advancements in enhancing the UAV-enabled communication systems' throughput in existing studies, there remains a notable gap in addressing issues concerning user fairness and quality-of-service (QoS) provisioning and lacks an effective scheme to depict the trade-off between system throughput and user fairness. To solve the above challenges, in this paper we introduce a novel fairness control scheme for UAV-enabled wireless communication systems based on a new weighted function. First, we propose a throughput combining model based on a new weighted function with fairness considering. Second, we formulate the optimization problem to maximize the weighted sum of all users' throughput. Third, we decompose the optimization problem and propose an efficient iterative algorithm to solve it. Finally, simulation results are provided to demonstrate the considerable potential of our proposed scheme in fairness and QoS provisioning.

Throughput and Fairness Trade-off Balancing for UAV-Enabled Wireless Communication Systems

TL;DR

This work tackles the throughput–fairness–QoS trade-off in UAV-enabled wireless systems for 6G. It introduces a tunable weighted throughput function with weights ω_k[n] = exp(-α R_k[n]) / ∑_i exp(-α R_i[n]), enabling adjustable fairness via the parameter α, and proves key properties of the induced function H_α(x). The authors formulate two optimization problems—Condition 1 (α R_k[n] ≤ 1) and Condition 2 (α → ∞)—and develop an iterative, convex-approximation algorithm that alternates between joint bandwidth/power allocation and UAV trajectory design, for both problem structures. Numerical results show that increasing α shifts resources toward poorer channels, reduces throughput disparity, and yields better QoS provisioning and stability, at the cost of overall system throughput; results are benchmarked against standard max-min schemes. The proposed framework provides a practical, controllable mechanism to balance system throughput and user fairness in UAV-assisted networks, with potential impact on dynamic deployment scenarios and QoS-sensitive applications.

Abstract

Given the imperative of 6G networks' ubiquitous connectivity, along with the inherent mobility and cost-effectiveness of unmanned aerial vehicles (UAVs), UAVs play a critical role within 6G wireless networks. Despite advancements in enhancing the UAV-enabled communication systems' throughput in existing studies, there remains a notable gap in addressing issues concerning user fairness and quality-of-service (QoS) provisioning and lacks an effective scheme to depict the trade-off between system throughput and user fairness. To solve the above challenges, in this paper we introduce a novel fairness control scheme for UAV-enabled wireless communication systems based on a new weighted function. First, we propose a throughput combining model based on a new weighted function with fairness considering. Second, we formulate the optimization problem to maximize the weighted sum of all users' throughput. Third, we decompose the optimization problem and propose an efficient iterative algorithm to solve it. Finally, simulation results are provided to demonstrate the considerable potential of our proposed scheme in fairness and QoS provisioning.
Paper Structure (16 sections, 2 theorems, 27 equations, 5 figures, 1 algorithm)

This paper contains 16 sections, 2 theorems, 27 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Condition 1: for $\alpha R_k[n]\leq1,\forall {k,n}$ (This condition can be satisfied by minimizing the quantization interval of $\alpha$ no matter how large $R_k[n]$ is, which will be expressed later in the simulation results analysis)
  6. Condition 2: for $\alpha \to +\infty$ (It's a maximizing minimum instantaneous throughput problem)
  7. Joint Resource Allocation and Trajectory Design Algorithm
  8. Condition 1: for $\alpha R_k[n]\leq1,\forall {k,n}$
  9. Joint Bandwidth and Power Allocation
  10. UAV Trajectory Optimization
  11. Overall Algorithm
  12. Condition 2: for $\alpha \to +\infty$
  13. Joint Bandwidth and Power Allocation
  14. UAV Trajectory Optimization
  15. Numerical Results
  16. ...and 1 more sections

Key Result

Lemma 1

The function $H_\alpha(x)$ given by (Hx) is concave and non-decreasing for $\alpha{x_i}\leq1,\forall i$.

Figures (5)

  • Figure 1: UAV-enabled wireless network
  • Figure 2: Optimal trajectories under different values of fairness factor $\alpha$
  • Figure 3: Throughput of all the users in different time slots with different values of fairness factor $\alpha$
  • Figure 4: Comparison of system throughput versus fairness factor $\alpha$ in different scenarios
  • Figure 5: Comparison of performance between our proposed scheme and benchmark scheme

Theorems & Definitions (4)

  • Lemma 1
  • Proof 1
  • Lemma 2
  • Proof 2