On Improving Throughput of Multichannel ALOHA using Preamble-based Exploration

TL;DR

This paper introduces a preamble-based exploration method for multichannel ALOHA in machine-type communication, significantly increasing throughput and reducing delay in IoT networks by leveraging feedback and steady-state analysis.

Contribution

It proposes a novel exploration approach with preambles for multichannel ALOHA, improving throughput and delay performance in MTC systems.

Findings

Maximum throughput improved by a factor of approximately 1.632.

Lower packet collision probability with fast retrial.

Simulation results validate analytical improvements.

Abstract

Machine-type communication (MTC) has been extensively studied to provide connectivity for devices and sensors in the Internet-of-Thing (IoT). Thanks to the sparse activity, random access, e.g., ALOHA, is employed for MTC to lower signaling overhead. In this paper, we propose to adopt exploration for multichannel ALOHA by transmitting preambles before transmitting data packets in MTC, and show that the maximum throughput can be improved by a factor of 2 - exp(-1) = 1.632, In the proposed approach, a base station (BS) needs to send the feedback information to active users to inform the numbers of transmitted preambles in multiple channels, which can be reliably estimated as in compressive random access. A steady-state analysis is also performed with fast retrial, which shows that the probability of packet collision becomes lower and, as a result, the delay outage probability is greatly reduced for a lightly loaded system. Simulation results also confirm the results from analysis.