Learning with Finite Memory for Machine Type Communication

TL;DR

This paper introduces a finite memory learning framework for IoT machine-type devices, significantly reducing critical message delay and improving resource sharing despite limited device memory.

Contribution

It proposes a novel finite memory learning method enabling MTDs to efficiently learn message states and adapt transmission parameters in IoT systems.

Findings

Critical alarm message delay reduced by up to 94%

Finite memory learning effectively mitigates learning limitations

Framework requires minimal memory for significant performance gains

Abstract

Machine-type devices (MTDs) will lie at the heart of the Internet of Things (IoT) system. A key challenge in such a system is sharing network resources between small MTDs, which have limited memory and computational capabilities. In this paper, a novel learning \emph{with finite memory} framework is proposed to enable MTDs to effectively learn about each others message state, so as to properly adapt their transmission parameters. In particular, an IoT system in which MTDs can transmit both delay tolerant, periodic messages and critical alarm messages is studied. For this model, the characterization of the exponentially growing delay for critical alarm messages and the convergence of the proposed learning framework in an IoT are analyzed. Simulation results show that the delay of critical alarm messages is significantly reduced up to $94\%$ with very minimal memory requirements. The results also show that the proposed learning with finite memory framework is very effective in mitigating the limiting factors of learning that prevent proper learning procedures.