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
Ultra-reliable and low-latency vehicular communication : an active learning approach
|Author:||Abdel-Aziz, Mohamed K.1; Samarakoon, Sumudu1; Bennis, Mehdi1,2;|
1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Department of Computer Engineering, Kyung Hee University, Yongin 446-701, South Korea
3Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019121146720
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-12-11
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%.
IEEE communications letters
|Pages:||1 - 4|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Academy of Finland project CARMA, and 6Genesis Flagship (grant no. 318927), in part by the INFOTECH project NOOR, in part by the Office of Naval Research (ONR) under MURI Grant N00014-19-1-2621, and in part by the Kvantum Institute strategic project SAFARI.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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