Article (Scientific journals)
Collaborative Distributed Q-Learning for RACH Congestion Minimization in Cellular IoT Networks
Sharma, Shree Krishna; Wang, Xianbin
2019In IEEE Communications Letters, 23 (4), p. 600-603
Peer reviewed
 

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Keywords :
Cellular IoT; Distributed learning; Q-learning; RACH congestion; Machine-Type Communications
Abstract :
[en] Due to infrequent and massive concurrent access requests from the ever-increasing number of machine-type communication (MTC) devices, the existing contention-based random access (RA) protocols, such as slotted ALOHA, suffer from the severe problem of random access channel (RACH) congestion in emerging cellular IoT networks. To address this issue, we propose a novel collaborative distributed Q-learning mechanism for the resource-constrained MTC devices in order to enable them to find unique RA slots for their transmissions so that the number of possible collisions can be significantly reduced. In contrast to the independent Q-learning scheme, the proposed approach utilizes the congestion level of RA slots as the global cost during the learning process and thus can notably lower the learning time for the low-end MTC devices. Our results show that the proposed learning scheme can significantly minimize the RACH congestion in cellular IoT networks.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Sharma, Shree Krishna ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Wang, Xianbin;  University of Western Ontario > Department of Electrical and Computer Engineering
External co-authors :
yes
Language :
English
Title :
Collaborative Distributed Q-Learning for RACH Congestion Minimization in Cellular IoT Networks
Publication date :
April 2019
Journal title :
IEEE Communications Letters
Volume :
23
Issue :
4
Pages :
600-603
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 13 April 2019

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