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Safety-Aware Age of Information (S-AoI) for Collision Risk Minimization in Cell-Free mMIMO Platooning Networks.

Mohammad Reza Abedi, Nader Mokari, Mohammad Reza Javan, Hamid Saeedi, Eduard A. Jorswieck, Halim Yanikomeroglu

IEEE Trans. Netw. Serv. Manag.(2024)

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Abstract
In this paper, fresh Basic Safety Messages (BSM) (e.g., vehicle’s position and speed) are used to control the Connected Automated Vehicles (CAVs) to reduce Time to Collision (TTC) error which leads to decrease in Collision Risk (CR). In contrast to exiting works, a novel Safety-aware Age of Information (S-AoI) metric is proposed that in addition to AoI, takes into account the risk assessment of CAVs to design an efficient transmission protocol for BSMs. We also deploy user-centric Cell-free-massive-MIMO (CFmMIMO) to improve the communication coverage, accessibility, and reliability, where each CAV is served by a cluster of nearby Access Points (APs). Unlike previous works, a two time-scale distributed deterministic policy gradients algorithm is adopted which greatly reduces the signal processing complexity, system load as well as signaling overhead while maintaining the performance. Simulation results show that the proposed framework, i.e, user-centric CFmMIMO technology together with S-AoI metric, can reduce average TTC error between 24%-35% across different lane change probabilities compared to the baseline scenario in which we use small cell mMIMO with AoI metric. Such a reduction in TTC error results in significant decrease (as high as 75%) in CR ratio.
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Key words
Connected automated vehicles,Platooning control,Collision avoidance,Cell-free massive MIMO network,Deep deterministic policy gradient,Machine learning,Resource allocation
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