Intelligent Reflecting Surface Placement Optimization in Air-Ground Communication Networks Toward 6G

Intelligent reflecting surfaces (IRSs) have emerged as a key enabler for beyond fifth-generation (B5G) communication technology and for realizing sixth-generation (6G) cellular communication. In addition, B5G and 6G networks are expected to support aerial user communications in accordance with the expanded requirements of data transmission for an aerial user. However, there are challenges in providing wireless communication for aerial users owing to the different radio wave propagation properties between terrestrial areas and aerial areas. In this article, we propose an IRS-aided cellular network coverage extension for aerial users. In our proposed network, IRS and base stations (BSs) cooperate with each other to provide air-ground communication to aerial users (AUs), the aim of which is to prevent interference signals from spreading to a wide area. Furthermore, IRS placement is designed to maximize the network performance in terms of the spatial signal-to-interference-plus-noise ratio (SINR) while mitigating inter-cell interference. Numerical analysis results indicate that the proposed IRS-aided network outperforms the benchmark system without IRSs when the IRS installation positions are optimally determined.

[1]  Nei Kato,et al.  Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches , 2020, Proceedings of the IEEE.

[2]  Jianhua Lu,et al.  QoE Driven Resource Allocation in Next Generation Wireless Networks , 2019, IEEE Wireless Communications.

[3]  Mahbub Hassan,et al.  Enhancing Cellular Communications for UAVs via Intelligent Reflective Surface , 2020, 2020 IEEE Wireless Communications and Networking Conference (WCNC).

[4]  Emil Björnson,et al.  Intelligent Reflecting Surface Versus Decode-and-Forward: How Large Surfaces are Needed to Beat Relaying? , 2019, IEEE Wireless Communications Letters.

[5]  Ian F. Akyildiz,et al.  A New Wireless Communication Paradigm through Software-Controlled Metasurfaces , 2018, IEEE Communications Magazine.

[6]  Chau Yuen,et al.  Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication , 2018, IEEE Transactions on Wireless Communications.

[7]  Ya-Ju Yu,et al.  Energy-Aware 3D Unmanned Aerial Vehicle Deployment for Network Throughput Optimization , 2020, IEEE Transactions on Wireless Communications.

[8]  Nei Kato,et al.  Ten Challenges in Advancing Machine Learning Technologies toward 6G , 2020, IEEE Wireless Communications.

[9]  Wei Chen,et al.  The Roadmap to 6G: AI Empowered Wireless Networks , 2019, IEEE Communications Magazine.

[10]  Rui Zhang,et al.  Spatial Throughput Characterization for Intelligent Reflecting Surface Aided Multiuser System , 2020, IEEE Wireless Communications Letters.

[11]  Jie Xu,et al.  Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems via Joint Transmit and Reflective Beamforming , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[12]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[13]  Ya-Ju Yu,et al.  Mobile Small Cell Deployment for Service Time Maximization Over Next-Generation Cellular Networks , 2017, IEEE Transactions on Vehicular Technology.