Facilitating mmWave Mesh Reliability in PPDR Scenarios Utilizing Artificial Intelligence

Abstract

The use of advanced AR/VR applications may benefit the efficiency of collaborative public protection and disaster relief (PPDR) missions by providing better situational awareness and deeper real-time immersion. The resultant bandwidth-hungry traffic calls for the use of capable millimeter-wave (mmWave) radio technologies, which are however susceptible to link blockage phenomena. The latter may significantly reduce the network reliability and thus degrade the performance of PPDR applications. Efficient mmWave-based mesh topologies need to, therefore, be constructed, which employ advanced multi-connectivity mechanisms to improve the levels of connectivity. This work conceptualizes predictive blockage avoidance by leveraging emerging artificial intelligence (AI) capabilities. In particular, AI-aided blockage prediction permits the mesh network to reconfigure itself by establishing alternative connections proactively, thus reducing the chances of a harmful link interruption. An illustrative scenario related to a fire suppression mission is then addressed by demonstrating that the proposed approach dramatically improves the connection reliability in dynamic mmWave-based deployments.

Keywords

• Mesh networks
• millimeter wave communication
• artificial intelligence (AI)
• wireless communication
• public protection and disaster relief (PPDR)