Paper Info
Title
Joint Optimization of Preamble Selection and Access Barring for MTC with Correlated Device Activities
Abstract
Most existing works on random access for machine-type communication (MTC) assume independent device activities. However, in several Internet-of-Things (IoT) applications, device activities are driven by events and hence may be correlated. This paper investigates the joint optimization of preamble selection and access barring for correlated device activities. We adopt a random access scheme with general random preamble selection parameterized by the preamble selection distributions of all devices and an access barring scheme parameterized by the access barring factor, to maximally exploit correlated device activities for improving the average throughput. First, we formulate the average throughput maximization problem with respect to the preamble selection distributions and the access barring factor. It is a challenging nonconvex problem. We characterize an optimality property of the problem. Then, we develop two iterative algorithms to obtain a stationary point and a low-complexity solution respectively by using the block coordinate descend (BCD) method. Numerical results show that the two proposed solutions achieve significant gains over existing schemes, demonstrating the significance of exploiting correlation of device activities in improving the average throughput. Numerical results also show that compared to the stationary point, the low-complexity solution achieves a similar average throughput with much lower computational complexity, demonstrating the effectiveness of the low-complexity solution.
Year
DOI
Venue
2021
10.1109/ICCWorkshops50388.2021.9473559
ICC Workshops
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Wang Liu101.69
Ying Cui237738.02
Lianghui Ding3131.53
Jun Sun47611.28
Yangyang Liu501.69
Yang Li614545.40
Li Zhang700.68