T. K. Vu, M. Bennis, M. Debbah, M. Latva-aho and C. S. Hong, "Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach," in IEEE Communications Letters, vol. 22, no. 4, pp. 708-711, April 2018. doi: 10.1109/LCOMM.2018.2802902
Ultra-reliable communication in 5G mmWave networks : a risk-sensitive approach
|Author:||Vu, Trung Kien1; Bennis, Mehdi1; Debbah, Mérouane2;|
1Centre for Wireless Communications, University of Oulu, Oulu, Finland
2Large Networks and System Group, CentraleSupélec, Université Paris–Saclay, Gif-sur-Yvette, France
3Department of Computer Science and Engineering, Kyung Hee University, Yongin, South Korea
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201803083912
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2018-03-08
In this letter, we investigate the problem of providing gigabit wireless access with reliable communication in 5G millimeter-Wave (mmWave) massive multiple-input multipleoutput (MIMO) networks. In contrast to the classical network design based on average metrics, we propose a distributed risk-sensitive reinforcement learning-based framework to jointly optimize the beamwidth and transmit power, while taking into account the sensitivity of mmWave links due to blockage. Numerical results show that our proposed algorithm achieves more than 9 Gbps of user throughput with a guaranteed probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More importantly, there exists a rate-reliability-network density tradeoff, in which as the user density increases from 16 to 96 per km², the fraction of users that achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively.
IEEE communications letters
|Pages:||708 - 711|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by Tekes, Nokia, Huawei, MediaTek, Keysight, Bittium, Kyynel, in part by the Academy of Finland via the grant 307492 and the CARMA grants 294128 and 289611.
|Academy of Finland Grant Number:||
307492 (Academy of Finland Funding decision)
294128 (Academy of Finland Funding decision)
289611 (Academy of Finland Funding decision)
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