Throughput and Latency in the Distributed Q-Learning Random Access mMTC Networks

• URL: http://arxiv.org/abs/2111.00299v1
• Date: Sat, 30 Oct 2021 17:57:06 GMT
• Title: Throughput and Latency in the Distributed Q-Learning Random Access mMTC Networks
• Authors: Giovanni Maciel Ferreira Silva, Taufik Abrao
• Abstract summary: In mMTC mode, with thousands of devices trying to access network resources sporadically, the problem of random access (RA) is crucial. In this work, we propose a distributed packet-based learning method by varying the reward from the central node that favors devices having a larger number of remaining packets to transmit. Our numerical results indicated that the proposed distributed packet-based Q-learning method attains a much better throughput-latency trade-off than the alternative independent and collaborative techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In mMTC mode, with thousands of devices trying to access network resources sporadically, the problem of random access (RA) and collisions between devices that select the same resources becomes crucial. A promising approach to solve such an RA problem is to use learning mechanisms, especially the Q-learning algorithm, where the devices learn about the best time-slot periods to transmit through rewards sent by the central node. In this work, we propose a distributed packet-based learning method by varying the reward from the central node that favors devices having a larger number of remaining packets to transmit. Our numerical results indicated that the proposed distributed packet-based Q-learning method attains a much better throughput-latency trade-off than the alternative independent and collaborative techniques in practical scenarios of interest. In contrast, the number of payload bits of the packet-based technique is reduced regarding the collaborative Q-learning RA technique for achieving the same normalized throughput.

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