T. K. Vu, C. F. Liu, m. bennis, M. Debbah, M. Latva-aho and C. S. Hong, "Ultra-Reliable and Low Latency Communication in mmWave-Enabled Massive MIMO Networks," in IEEE Communications Letters, vol. PP, no. 99, pp. 1-1. doi: 10.1109/LCOMM.2017.2705148
Ultra-reliable and low latency communication in mmWave-enabled massive MIMO Networks
|Author:||Vu, Trung Kien1; Liu, Chen-Feng1; Bennis, Mehdi1;|
1Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
2Large Networks and System Group (LANEAS), CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France
3Mathematical and Algorithmic Sciences Laboratory, Huawei France R&D, Paris, France
4Department of Computer Engineering, Kyung Hee University, Yongin 446-701, South Korea
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201708308277
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2017-08-30
Ultra-reliability and low-latency are two key components in 5G networks. In this letter, we investigate the problem of ultra-reliable and low-latency communication (URLLC) in millimeter wave (mmWave)-enabled massive multiple-input multiple-output (MIMO) networks. The problem is cast as a network utility maximization subject to probabilistic latency and reliability constraints. To solve this problem, we resort to the Lyapunov technique whereby a utility-delay control approach is proposed, which adapts to channel variations and queue dynamics. Numerical results demonstrate that our proposed approach ensures reliable communication with a guaranteed probability of 99.99%, and reduces latency by 28.41% and 77.11% as compared to baselines with and without probabilistic latency constraints, respectively.
IEEE communications letters
|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 Finnish Funding Agency for Technology and Innovation (Tekes), Nokia, Huawei, Anite, in part by the Academy of Finland CARMA project, in part by the Academy of Finland funding through the grant 284704, and in part by the ERC Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering).
|Academy of Finland Grant Number:||
284704 (Academy of Finland Funding decision)
289611 (Academy of Finland Funding decision)
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