Enabling Robust Communication Among Military Ground Vehicles Using Multi-Connectivity

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Abstract
Content
Vehicles-to-Everything or V2X communications provide attractive advantages in achieving reliable and high-performance connectivity amongst ground and aerial military vehicles. The 5G New Radio (NR) based cellular-V2X (C-V2X) technology, can support wide coverage areas with higher data rates and lower latencies needed for demanding military applications ranging from real-time sensing to navigation of autonomous military ground vehicles. Millimeter wave technology (mmWave) is critical to meet such throughput and latency requirements. However, mmWave links have a low transmission range and are often subject to blockages due to factors like weather, terrain, etc. that make them unreliable. Multi-connectivity with packet duplication can be used to enhance the reliability and latency by transmitting concurrently over independent links between a mobile device and multiple base stations. We propose and evaluate a novel method based on new radio dual connectivity (NR-DC) and packet duplication techniques to achieve reliable communication between military ground vehicles, especially in mobility scenarios. We further propose and analyze a Channel State Information Reference Signal Received Quality (CSI-RSRQ) based duplication strategy to improve the system's radio resource utilization. Channel State Information Reference Signal symbols in downlink transmissions are used to accurately compute the CSI-RSRQ values of the radio channel in real-time. This is critical on the battlefield for real-time awareness and adaptive control in fast-changing environments. Prototyped in the Simu5G network simulator and MATLAB, our results show packet duplication achieved less than 5 milliseconds of latency with zero packet loss under mobility.
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Citation
Mishra, P., Kar, S., Lin, C., Wang, K. et al., "Enabling Robust Communication Among Military Ground Vehicles Using Multi-Connectivity," SAE Int. J. Adv. & Curr. Prac. in Mobility 6(1):111-120, 2024, .
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Publisher
Published
Apr 11, 2023
Product Code
2023-01-0110
Content Type
Journal Article
Language
English