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Pinching Antenna-Aided Wireless Powered Communication Networks

Yixuan Li, Hongbo Xu, Ming Zeng, Yuanwei Liu

TL;DR

This work investigates a pinching-antenna-aided WPCN (PA-WPCN) where multiple pinching antennas lie along a waveguide to establish robust LoS links with IoT devices under HTT, using TDMA and NOMA. It integrates a proportional PA power model, waveguide loss, and a nonlinear energy harvesting model, and jointly optimizes PA positions ${x_n^{PA}}$ and resource allocation to maximize the sum rate. The authors decouple the problem via alternating optimization into a resource-allocation subproblem (solved by convex optimization/KKT) and a PA-position subproblem (addressed with an element-wise (EW) method and a stochastic parameter differential evolution (SPDE) algorithm), accompanied by complexity and convergence analysis. Numerical results show PA-WPCN outperforms fixed-antenna systems, with TDMA generally surpassing NOMA when circuit power is nonzero, and reveal the optimal PA power distribution ratio is about $0.55$--$0.60$. This work offers practical, scalable optimization strategies for energy-aware, LoS-enhanced WPCNs employing pinching-antenna systems.

Abstract

In this letter, we investigate a novel pinching antenna (PA)-aided wireless powered communication network (WPCN), in which multiple PAs are activated along a waveguide to establish robust line-of-sight links with multiple devices. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols are considered in the PA-WPCN. Moreover, some practical considerations, including a proportional power model for the PAs, a waveguide transmission loss model, and a nonlinear energy harvesting model, are incorporated into the PA-WPCN. Furthermore, we formulate a sum-rate maximization problem by jointly optimizing resource allocation and PAs position. To address the challenging problem of the PAs position optimization, we propose a high-performance element-wise (EW) algorithm and a low-complexity stochastic parameter differential evolution (SPDE) algorithm. Numerical results validate the remarkable performance of the proposed PA-WPCN and the effectiveness of our algorithms, indicating that optimal performance is attained when the PA power distribution ratio of approximately 0.55-0.6.

Pinching Antenna-Aided Wireless Powered Communication Networks

TL;DR

This work investigates a pinching-antenna-aided WPCN (PA-WPCN) where multiple pinching antennas lie along a waveguide to establish robust LoS links with IoT devices under HTT, using TDMA and NOMA. It integrates a proportional PA power model, waveguide loss, and a nonlinear energy harvesting model, and jointly optimizes PA positions and resource allocation to maximize the sum rate. The authors decouple the problem via alternating optimization into a resource-allocation subproblem (solved by convex optimization/KKT) and a PA-position subproblem (addressed with an element-wise (EW) method and a stochastic parameter differential evolution (SPDE) algorithm), accompanied by complexity and convergence analysis. Numerical results show PA-WPCN outperforms fixed-antenna systems, with TDMA generally surpassing NOMA when circuit power is nonzero, and reveal the optimal PA power distribution ratio is about --. This work offers practical, scalable optimization strategies for energy-aware, LoS-enhanced WPCNs employing pinching-antenna systems.

Abstract

In this letter, we investigate a novel pinching antenna (PA)-aided wireless powered communication network (WPCN), in which multiple PAs are activated along a waveguide to establish robust line-of-sight links with multiple devices. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) protocols are considered in the PA-WPCN. Moreover, some practical considerations, including a proportional power model for the PAs, a waveguide transmission loss model, and a nonlinear energy harvesting model, are incorporated into the PA-WPCN. Furthermore, we formulate a sum-rate maximization problem by jointly optimizing resource allocation and PAs position. To address the challenging problem of the PAs position optimization, we propose a high-performance element-wise (EW) algorithm and a low-complexity stochastic parameter differential evolution (SPDE) algorithm. Numerical results validate the remarkable performance of the proposed PA-WPCN and the effectiveness of our algorithms, indicating that optimal performance is attained when the PA power distribution ratio of approximately 0.55-0.6.

Paper Structure

This paper contains 11 sections, 18 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Problem Formulation for TDMA and NOMA
  4. Proposed Algorithms
  5. Optimal Resource Allocation for TDMA and NOMA
  6. Position Optimization for PAs
  7. Element-wise algorithm
  8. Stochastic Parameter Differential Evolution Algorithm
  9. Complexity and Convergence Analysis
  10. Numerical Results
  11. Conclusion

Figures (4)

  • Figure 1: System model and operation protocols of the proposed PA-WPCN.
  • Figure 2: Sum rate versus number of iterations.
  • Figure 3: Sum rate versus $N$ with different $p_{c,k}$.
  • Figure 4: Sum rate versus $\delta$ with TDMA and NOMA protocols.

Theorems & Definitions (1)

  • proof