Coverage Enhancement for NLOS mmWave Links Using Passive Reflectors
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Abstract (arXiv)
The future 5G networks are expected to use millimeter wave (mmWave) frequency bands to take advantage of large unused spectrum. However, due to the high path loss at mmWave frequencies, coverage of mmWave signals can get severely reduced, especially for non-line-of-sight (NLOS) scenarios as mmWave signals are severely attenuated when going through obstructions. In this work, we study the use of passive metallic reflectors of different shapes/sizes to improve 28 GHz mmWave signal coverage for both indoor and outdoor NLOS scenarios. We quantify the gains that can be achieved in the link quality with metallic reflectors using measurements, analytical expressions, and ray tracing simulations. In particular, we provide an analytical model for the end-to-end received power in an NLOS scenario using reflectors of different shapes and sizes. For a given size of the flat metallic sheet reflector approaching to the size of incident plane waves, we show that the reflected received power for the NLOS link is same as line-of-sight (LOS)free space received power of the same link distance. Extensive results are provided to study impact of environmental features and reflector characteristics on NLOS link quality.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2019.
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimationcited by 0
- 2026Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusingcited by 0
- 2026Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrayscited by 0
- 2026Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectorscited by 0
- 2026Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors6 Apr 2026
- 2026Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrays6 Apr 2026
- 2026Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing7 Mar 2026
- 2025Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation5 Jun 2025