Distributed Delay Optimization of Machine-Type Communications in 5G Networks

Abstract

Optimization of delay performance is crucial to support delay critical applications for Machine-type Communications (MTC) in 5G networks. Centralized approaches to optimize delay performance rely on information exchange between gNB and machine type devices (MTDs), which would lead to unaffordable overhead especially when the number of MTDs becomes large. To address this issue, a novel distributive method is proposed. Specifically, each MTD can estimate the number of MTDs by counting the number of Msg4 on the channel along with their own successful and total transmissions of access requests in each estimation interval and then obtain the optimal Access Class Barring (ACB) factor according to an explicit expression. It is shown that the proposed method can achieve the minimum mean access delay. Moreover, the proposed method can overcome the disparity of the estimated ACB factor caused by the difference of independent observations of each MTD, which can ensure that the delay jitter is optimized simultaneously.