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Pinching-Antenna Systems (PASS)-Enabled UAV Delivery

Suyu Lv, Meng Li, Qi Li, Yuanwei Liu

TL;DR

The paper tackles the challenge of energy-efficient UAV delivery under high-rate communication requirements by integrating Pinching-Antenna Systems (PASS) with a joint, double-layer optimization (DLO). It introduces a PASS-enabled framework and formulates a TSP-like delivery sequence problem plus a highly-coupled PA activation MINLP, solving them with a Hierarchical Alternating Optimization (HAO) outer layer and two inner-layer PA-solvers (BnB for optimality and ISLR for low complexity). Key contributions include a GA-DP approach for sequence planning, a convex-relaxation–based Branch-and-Bound (BnB) algorithm for global PA activation optimization, and an Incremental Search with Local Refinement (ISLR) for fast sub-optimal PA activation, all validated by simulations showing reduced flight distance, energy savings, and PASS superiority over conventional MIMO at high rate demands. The results demonstrate that PASS can shift the communication energy burden from the last mile to the last meter, enabling reliable, energy-efficient UAV delivery in dynamic environments.

Abstract

A pinching-antenna systems (PASS)-enabled unmanned aerial vehicle (UAV) delivery framework is proposed, which exploits the capability of PASS to establish a strong line-of-sight link and reduce free-space pathloss.Aiming at minimizing the communication energy consumption in one cycle, a double-layer optimization (DLO) algorithm is developed by jointly optimizing the UAV delivery sequence and the pinching antenna (PA) activation vector. More specifically, at the outer layer, a hierarchical alternating optimization (HAO) scheme is proposed to tackle the NP-hard problem of delivery sequence planning, where a genetic algorithm performs global exploration to generate candidate solutions at the top-level, while a dynamic programming performs local refinement to obtain elite solutions at the lower-level. With determined UAV trajectory, at the inner layer, focus is placed on addressing the highly coupled mixed-integer nonlinear programming problem of PA activation vector optimization, where a pair of algorithms are proposed: 1) Branch-and-Bound (BnB) algorithm for finding global optimum; 2) incremental search and local refinement (ISLR) algorithm for reducing computational complexity. Simulation results indicate that: i) The proposed HAO-based delivery sequence planning scheme can effectively reduce the total flight distance, thereby decreasing flight time and communication energy consumption; ii) Both the proposed BnB and ISLR algorithms can achieve energy-efficient PA activation, with the former exhibiting better performance and the latter having lower complexity; iii) PASS outperforms the conventional multi-antenna systems, especially with higher communication rate requirements.

Pinching-Antenna Systems (PASS)-Enabled UAV Delivery

TL;DR

The paper tackles the challenge of energy-efficient UAV delivery under high-rate communication requirements by integrating Pinching-Antenna Systems (PASS) with a joint, double-layer optimization (DLO). It introduces a PASS-enabled framework and formulates a TSP-like delivery sequence problem plus a highly-coupled PA activation MINLP, solving them with a Hierarchical Alternating Optimization (HAO) outer layer and two inner-layer PA-solvers (BnB for optimality and ISLR for low complexity). Key contributions include a GA-DP approach for sequence planning, a convex-relaxation–based Branch-and-Bound (BnB) algorithm for global PA activation optimization, and an Incremental Search with Local Refinement (ISLR) for fast sub-optimal PA activation, all validated by simulations showing reduced flight distance, energy savings, and PASS superiority over conventional MIMO at high rate demands. The results demonstrate that PASS can shift the communication energy burden from the last mile to the last meter, enabling reliable, energy-efficient UAV delivery in dynamic environments.

Abstract

A pinching-antenna systems (PASS)-enabled unmanned aerial vehicle (UAV) delivery framework is proposed, which exploits the capability of PASS to establish a strong line-of-sight link and reduce free-space pathloss.Aiming at minimizing the communication energy consumption in one cycle, a double-layer optimization (DLO) algorithm is developed by jointly optimizing the UAV delivery sequence and the pinching antenna (PA) activation vector. More specifically, at the outer layer, a hierarchical alternating optimization (HAO) scheme is proposed to tackle the NP-hard problem of delivery sequence planning, where a genetic algorithm performs global exploration to generate candidate solutions at the top-level, while a dynamic programming performs local refinement to obtain elite solutions at the lower-level. With determined UAV trajectory, at the inner layer, focus is placed on addressing the highly coupled mixed-integer nonlinear programming problem of PA activation vector optimization, where a pair of algorithms are proposed: 1) Branch-and-Bound (BnB) algorithm for finding global optimum; 2) incremental search and local refinement (ISLR) algorithm for reducing computational complexity. Simulation results indicate that: i) The proposed HAO-based delivery sequence planning scheme can effectively reduce the total flight distance, thereby decreasing flight time and communication energy consumption; ii) Both the proposed BnB and ISLR algorithms can achieve energy-efficient PA activation, with the former exhibiting better performance and the latter having lower complexity; iii) PASS outperforms the conventional multi-antenna systems, especially with higher communication rate requirements.

Paper Structure

This paper contains 33 sections, 55 equations, 8 figures, 5 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel and Signal Model
  4. Power Radiation Model
  5. UAV Delivery Model
  6. Problem Formulation
  7. Proposed Solutions
  8. Outer Layer: UAV Delivery Sequence Planning
  9. Top-level: GA-based global exploration
  10. Population and chromosome initialization
  11. Design of fitness function
  12. Selection, crossover, and mutation
  13. Generation of candidate solution
  14. Lower-level: DP-based Local Refinement
  15. HAO-based UAV Delivery Sequence Planning Scheme
  16. ...and 18 more sections

Figures (8)

  • Figure 1: Illustration of PASS-enabled UAV delivery framework.
  • Figure 2: Illustration of power radiation model.
  • Figure 3: Illustration of the DLO algorithm.
  • Figure 4: Total flight distance of UAV to complete the delivery tasks.
  • Figure 5: Optimized UAV trajectory.
  • ...and 3 more figures

Theorems & Definitions (2)

  • Remark 1
  • Remark 2