Radio resource allocation in LTE-advanced cellular networks with M2M communications

Zheng, Kan, Hu, Fanglong, Wang, Wenbo, Xiang, Wei, and Dohler, Mischa (2012) Radio resource allocation in LTE-advanced cellular networks with M2M communications. IEEE Communications Magazine, 50 (7). pp. 184-192.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: http://dx.doi.org/10.1109/MCOM.2012.6231...
292


Abstract

Machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity between machines without the need of human intervention. To support such a large number of autonomous devices, the M2M system architecture needs to be extremely power and spectrally efficient. This article thus briefly reviews the features of M2M services in the third generation (3G) long-term evolution and its advancement (LTE-Advanced) networks. Architectural enhancements are then presented for supporting M2M services in LTE-Advanced cellular networks. To increase spectral efficiency, the same spectrum is expected to be utilized for human-to-human (H2H) communications as well as M2M communications. We therefore present various radio resource allocation schemes and quantify their utility in LTE-Advanced cellular networks. System-level simulation results are provided to validate the performance effectiveness of M2M communications in LTE-Advanced cellular networks.

Item ID: 43291
Item Type: Article (Research - C1)
ISSN: 1558-1896
Funders: National Basic Research Program of China (NBRPC), China National Science Funding (CNSF)
Projects and Grants: NBRPC grant 2012CB316005, NBRPC ICT Project INFSO-ICT-258512EXALTED, CNSF no. 61171106
Date Deposited: 24 Feb 2016 07:47
FoR Codes: 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090699 Electrical and Electronic Engineering not elsewhere classified @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8901 Communication Networks and Services > 890199 Communication Networks and Services not elsewhere classified @ 100%
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page