# Joint Trajectory and IRS Phase Shift Optimization for Dual IRS-UAV-Assisted Uplink Data Collection in Wireless Sensor Networks

## TL;DR

This paper introduces a new dual IRS-UAV model and algorithm to improve data collection in wireless sensor networks by optimizing flight paths and signal reflection.

## Contribution

A novel dual IRS-UAV model and a joint optimization algorithm for trajectory and phase shifts to maximize communication rates in WSNs.

## Key findings

- The dual IRS-UAV model significantly enhances the system's sum rate compared to traditional single IRS-UAV models.
- The proposed algorithm effectively addresses non-convex optimization problems and improves pairwise total rates by 11.91% and 16.36% over traditional schemes.
- The joint optimization of UAV trajectories and IRS phase shifts meets minimum QoS constraints while maximizing communication performance.

## Abstract

Based on practical communication scenarios and existing research, this paper innovatively proposed a dual IRS-UAV-assisted uplink model for WSNs. The dual reflection link between the IRSs was utilized to effectively enhance the system’s transmission rate. Additionally, a novel algorithm was designed to jointly optimize the IRS phase shifts and UAV flight trajectory, aiming to maximize the system sum rate. Experimental results demonstrate that the scheme proposed in this paper is significantly superior to the traditional IRS-UAV trajectory design schemes.

What are the main findings?
This paper designed a completely new model of dual IRS-UAV-assisted communication.This paper presents a new trajectory optimization algorithm.

This paper designed a completely new model of dual IRS-UAV-assisted communication.

This paper presents a new trajectory optimization algorithm.

What is the implication of the main finding?
Compared with the traditional single IRS-UAV-assisted communication model, the newly proposed model can significantly enhance the sum rate of the communication system.The proposed new algorithm can effectively address the non-convexity issue in optimization problems.

Compared with the traditional single IRS-UAV-assisted communication model, the newly proposed model can significantly enhance the sum rate of the communication system.

The proposed new algorithm can effectively address the non-convexity issue in optimization problems.

Intelligent reflecting surface-assisted unmanned aerial vehicles (IRS-UAVs) have been widely applied in various communication scenarios. This paper addressed the uplink communication problem in wireless sensor networks (WSNs) by proposing a novel double IRS-UAVs assisted framework to improve the pairwise sum rate. Specifically, nodes with relatively short signal transmission distances upload signals via a single-reflection link, while nodes with relatively long distances upload signals through a dual-reflection link involving two IRSs. Within each work cycle, the IRS-UAVs followed a fixed service sequence to cyclically assist all sensor node pairs. We designed a joint optimization algorithm that simultaneously optimized the UAV trajectories and IRS phase shifts to maximize the pairwise sum rate while guaranteeing each node’s transmission rate meets a minimum quality of service (QoS) constraint. Specifically, we introduce slack variables to linearize the inherently nonlinear constraints arising from interdependent variables, thereby transforming each subproblem into a more manageable form. These subproblems are then solved iteratively within a coordinated optimization framework: in each iteration, one subproblem is optimized while keeping variables of others fixed, and the solutions are alternately updated to refine the overall performance. The numerical results show that this algorithm can effectively optimize the flight trajectory of the unmanned aircraft and significantly improve the pairwise total rate of the system. Compared with the two traditional schemes, the average optimization rates are 11.91% and 16.36%.