Throughput maximization of an IRS-assisted wireless powered network with interference: A deep unsupervised learning approach

TL;DR

This paper introduces a deep unsupervised learning method to optimize throughput in an IRS-assisted wireless powered network, achieving comparable or better results than genetic algorithms with significantly reduced computational complexity.

Contribution

It presents a novel deep unsupervised learning approach for joint optimization in IRS-assisted wireless networks, reducing computational complexity while maintaining high throughput.

Findings

The proposed DUL approach is several times faster than GA.

DUL achieves similar or higher throughput compared to GA.

The method offers a better performance-complexity trade-off.

Abstract

We consider an intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) in which a multi antenna power beacon (PB) sends a dedicated energy signal to a wireless powered source. The source first harvests energy and then utilizing this harvested energy, it sends an information signal to destination where an external interference may also be present. For the considered system model, we formulated an analytical problem in which the objective is to maximize the throughput by jointly optimizing the energy harvesting (EH) time and IRS phase shift matrices. The optimization problem is high dimensional non-convex, thus a good quality solution can be obtained by invoking any state-of-the-art algorithm such as Genetic algorithm (GA). It is well-known that the performance of GA is generally remarkable, however it incurs a high computational complexity. To this end, we propose a deep unsupervised learning (DUL) based approach in which a neural network (NN) is trained very efficiently as time-consuming task of labeling a data set is not required. Numerical examples show that our proposed approach achieves a better performance-complexity trade-off as it is not only several times faster but also provides almost same or even higher throughput as compared to the GA.