Average Age-of-Information Minimization in Aerial IRS-Assisted Data Delivery

Abstract

Aerial intelligent reconfigurable surface (IRS) is a promising technology to enhance channel quality in data delivery. In this paper, we study an aerial IRS deployment problem to enable timely and reliable data delivery in a remote Internet of Things (IoT) scenario, in which an IRS mounted on an unmanned aerial vehicle (UAV) is adopted as a mobile relay to assist devices in uploading data to the base station (BS). The objective is to minimize the average age of information (AoI) of the data received by the BS over time by jointly determining the aerial IRS deployment position and phase shift, transmit power of devices, and data uploading time. Under the requirements of peak AoI (PAoI) and communication reliability, we formulate an average AoI minimization problem. Since the non-linear relations among optimization variables make the formulated problem non-convex and intractable to solve, we propose a block coordinate descent (BCD) based iterative algorithm which decomposes the formulated problem into several sub-problems. The variables are optimized in each sub-problem individually in an alternately iterative manner to attain a near-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm in improving the information freshness compared with the benchmark schemes.