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 millimeterwave (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.INDEX TERMS Mesh networks, millimeter wave communication, artificial intelligence (AI), wireless communication, public protection and disaster relief (PPDR).

Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

“…In turn, that increased the device number, which is also affecting the complexity of the system's architecture [1]. With this respect, researchers are already investigating the potential challenges for future wireless communications starting at 6G and beyond [2]- [5] that bring higher requirements in latency, reliability, security, privacy, etc.…”