On an Intelligent Reflecting Surface-Assisted Task Offloading Strategy for Indoor Terahertz Networks

The Internet of Things (IoT) Network brings more possibilities and connections to indoor scenarios. With the growing number of mobile devices and online services, indoor wireless networks are required to achieve higher speed and capacity for carrying the exponentially increasing amount of data. The Terahertz (THz) communication, with its large spectrum resources, is considered to be the main technology to achieve ultra-high-speed wireless communications in the 6G era. However, THz signals are severely attenuated over distances and the transmission of narrow-beam signals in THz is severely affected by the None-Line-of-Sight (NLoS) environment. To tackle this indoor transmission issue, Intelligent Reflecting Surface (IRS) attracts attention with its beyond Line-of-Sight (LoS) ability and easy deployment. However, an IRS can only serve a limited number of users and requires proper allocation due to the constraints on hardware. The transmission performance is also severely affected by the interference among sub-channels. In this paper, we study IRS-aided data offloading for indoor THz networks. Considering the user interference, transmission conditions, and diversified tasks, we optimize the allocation of limited IRS resource as well as partition planning. A dynamic algorithm based on combinatorial optimization is proposed. Simulation results reveal that our proposed approach can significantly improve the offloading performance.