Network packet

World Wide Web

Scheme (mathematics)

Network congestion

Aloha

Telecommunications

Computer science

Distributed computing

Computer network

Mathematics

Random access

Mathematical analysis

Minification

Programming language

Channel (broadcasting)

Wireless

Throughput

IEEE Communications Letters

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.

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