Throughput optimization for massive random access of M2M communications in LTE networks

A key challenge for enabling Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access pe...

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Bibliographic Details
Main Authors: Wen Zhan, Lin Dai
Format: Conference Proceeding
Language:English
Subjects:
Aggregates
Analytical models
Long Term Evolution
Machine-to-Machine (M2M) communications
Machine-to-machine communications
optimization
random access
Steady-state
Throughput
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Summary:A key challenge for enabling Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of M2M communications in LTE networks. Both the maximum network throughput and the corresponding optimal backoff parameters including the Access Class Barring (ACB) factor and the backoff window size are obtained as explicit functions of key system parameters such as the number of preambles, the number of Machine Type Devices (MTDs) and the aggregate input rate. The analysis is verified by simulations and sheds important light on practical network design for supporting massive access of M2M communications in LTE networks.
ISSN:1938-1883
DOI:10.1109/ICC.2017.7996628