Intelligent reflection surface (IRS) is an emerging technology for future wireless networks. The IRS technology’s main advantages in terms of boosting the performance of wireless communication are as follows [1]: (1) IRS is highly compatible with various wireless physical layer technologies that are currently being developed since it focuses on signal propagation over a wireless medium, while the other approaches are primarily used in transceivers; (2) IRS is made up of a massive number of small reflecting elements arranged on a planar surface, and it has a minimal hardware complexity and much flexibility in terms of deployment; (3) The reflecting elements of the IRS are passive, requiring no active radio-frequency (RF) chains that consume a lot of power, and control circuits for the IRS are likewise ultra-low-power electronic circuits [2]. As a result, IRS is an energy-saving device that can be powered by wireless energy harvesting. On the other hand, one of the essential uses for UAVs is wireless communications, which is projected to play a critical part in future wireless networks [3]. UAVs with wireless transceivers can be utilized as relays for data transmission. It can be used as an aerial base station to provide services to places without access to the internet. UAVs can also be utilized to gather and deliver information between ground stations and during mining surveying and exploration missions.

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access candidate for the six-generation (6G) networks [4]. NOMA outperforms traditional orthogonal multiple access (OMA) in various ways, including [5]: (1) It delivers higher spectrum efficiency by simultaneously serving numerous users with the same frequency resource and minimizing interference through successive interference cancellation (SIC); (2) It increases the number of users serviced at the same time, allowing for massive connectivity; (3) Due to the nature of simultaneous transmission, a user does not need to go through a predefined time slot to transmit their data, resulting in shorter latency; (4) Flexible power control between strong and weak users allows NOMA to preserve user fairness and diverse quality of service [6]; in particular, because more power is allocated to a weak user, NOMA offers higher cell-edge throughput and so improves the cell-edge user experience.

Previous studies on IRS-Assisted UAV Communications typically consider a fixed placement for the IRS or to be mounted on a UAV. However, the assumption of placing the IRS at a fixed position will prevent mobile users from enhancing many benefits of the wireless network, such as data rate, coverage, etc. Moreover, the assumption of placing the IRS on a UAV is not practical for many reasons, including the heavy IRS weight and its large size, and the speed of wind in bad weather can effects the stability of the UAV in the sky. For example, the RFocus prototype from MIT is constructed of 3,720 antennas spread across a six-square-meter surface [7]. Moreover, the small-size IRS, such as NTT DOCOMO’s prototype, is designed for indoor applications [8].

This paper uses a single UAV and an IRS mounted on a mobile ground vehicle (M-IRS) to maximize the average data rate in an IoT-6G wireless network. The formulated problem aims to find an efficient trajectory for the UAV, an efficient path for the M-IRS, and users’ power allocation coefficients that maximize the average data rate for mobile ground users in a 6G wireless network. As far as we know, this is the first study that proposes utilizing an M-IRS and a UAV to maximize the average data rate in an IoT-6G wireless network. The optimization problem presented in this study can be expanded using clustering techniques to include multiple UAVs. Then, utilizing our suggested method, a single M-IRS-UAV system can be allocated to service each cluster. The following are the primary contributions of this paper:

• •Realistic path models are utilized for wireless connections. The ground user receives a signal from the UAV in line-of-sight (LoS) and non-line-of-sight (NLoS) paths. Moreover, the ground user is assumed to receive signals from NLoS paths reflected by M-IRS elements due to environmental obstacles.
• •The optimization problem is formulated to find an efficient trajectory for the UAV, an efficient path for the M-IRS, and users’ power allocation coefficients that maximize the average data rate for mobile ground users in an IoT-6G wireless network.
• •We show that the proposed dynamic power allocation technique outperforms the fixed power allocation technique regarding network average sum rate.
• •The individual movement model (Random Waypoint Model) represents the users’ movements inside the coverage area. In this model, the ground-moving users can randomly move and change their location without any constraints. Moreover, the users’ velocity, movement direction, and following location are selected randomly and separated from other users in the group.
• •An efficient approach is proposed using a Genetic Algorithm (GA) for finding an efficient trajectory for the UAV and an efficient path for the M-IRS to provide wireless connectivity for mobile users during their movement.

The rest of the paper is organized as follows. The related work is presented in Section 2. The system model, including the problem formulation, is presented in Section 3. Section 4 introduces the dynamic power allocation strategy. Section 5 describes the proposed mobility model for users during their movement; moreover, this section proposes the Genetic Algorithm to determine an efficient UAV trajectory and an efficient path for the M-IRS. In Section 6, we provide the results of our experiments. Section 7 presents the lessons learned from this research. Finally, Section 8 concludes the paper.