Collaborative Distributed Q-Learning for RACH Congestion Minimization in Cellular IoT Networks | Zendy

Shree Krishna Sharma | Zendy; Xianbin Wang | Zendy

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Collaborative Distributed Q-Learning for RACH Congestion Minimization in Cellular IoT Networks

Author(s) -

Shree Krishna Sharma,

Xianbin Wang

Publication year - 2019

Publication title -

ieee communications letters

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.929

H-Index - 148

eISSN - 1558-2558

pISSN - 1089-7798

DOI - 10.1109/lcomm.2019.2896929

Subject(s) - computer science , computer network , aloha , random access , scheme (mathematics) , channel (broadcasting) , network congestion , minification , throughput , distributed computing , wireless , telecommunications , network packet , mathematical analysis , mathematics , programming language

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-lear...

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