Ultra-Reliable and Low-Latency Vehicular Communication

In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles’ AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to actively learn the network dynamics, estimate the vehicles’ future AoI, and proactively allocate resources. Simulation results show a significant improvement in terms of AoI violation probability, compared to several baselines, with a reduction of at least 50%.

Abdel-Aziz Mohamed K., Samarakoon Sumudu, Bennis Mehdi, Saad Walid

Publication type:
A1 Journal article – refereed

Place of publication:

age of information (AoI), Gaussian process regression (GPR), ultra-reliable low-latency communication (URLLC), V2X


Full citation:
M. K. Abdel-Aziz, S. Samarakoon, M. Bennis and W. Saad, “Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach,” in IEEE Communications Letters, vol. 24, no. 2, pp. 367-370, Feb. 2020. doi: 10.1109/LCOMM.2019.2956929


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