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Rethinking Grant-Free Protocol in mMTC

Minhao Zhu, Yifei Sun, Lizhao You, Zhaorui Wang, Ya-Feng Liu, Shuguang Cui

TL;DR

This paper proposes a two-stage communication protocol which consists of estimation of K in Phase I and detection of identities of active devices in Phase II and designs an algorithm for estimating K in Phase I, and exploits the estimated K to reduce the computational complexity of the identity detector in Phase II.

Abstract

This paper revisits the identity detection problem under the current grant-free protocol in massive machine-type communications (mMTC) by asking the following question: for stable identity detection performance, is it enough to permit active devices to transmit preambles without any handshaking with the base station (BS)? Specifically, in the current grant-free protocol, the BS blindly allocates a fixed length of preamble to devices for identity detection as it lacks the prior information on the number of active devices $K$. However, in practice, $K$ varies dynamically over time, resulting in degraded identity detection performance especially when $K$ is large. Consequently, the current grant-free protocol fails to ensure stable identity detection performance. To address this issue, we propose a two-stage communication protocol which consists of estimation of $K$ in Phase I and detection of identities of active devices in Phase II. The preamble length for identity detection in Phase II is dynamically allocated based on the estimated $K$ in Phase I through a table lookup manner such that the identity detection performance could always be better than a predefined threshold. In addition, we design an algorithm for estimating $K$ in Phase I, and exploit the estimated $K$ to reduce the computational complexity of the identity detector in Phase II. Numerical results demonstrate the effectiveness of the proposed two-stage communication protocol and algorithms.

Rethinking Grant-Free Protocol in mMTC

TL;DR

This paper proposes a two-stage communication protocol which consists of estimation of K in Phase I and detection of identities of active devices in Phase II and designs an algorithm for estimating K in Phase I, and exploits the estimated K to reduce the computational complexity of the identity detector in Phase II.

Abstract

This paper revisits the identity detection problem under the current grant-free protocol in massive machine-type communications (mMTC) by asking the following question: for stable identity detection performance, is it enough to permit active devices to transmit preambles without any handshaking with the base station (BS)? Specifically, in the current grant-free protocol, the BS blindly allocates a fixed length of preamble to devices for identity detection as it lacks the prior information on the number of active devices . However, in practice, varies dynamically over time, resulting in degraded identity detection performance especially when is large. Consequently, the current grant-free protocol fails to ensure stable identity detection performance. To address this issue, we propose a two-stage communication protocol which consists of estimation of in Phase I and detection of identities of active devices in Phase II. The preamble length for identity detection in Phase II is dynamically allocated based on the estimated in Phase I through a table lookup manner such that the identity detection performance could always be better than a predefined threshold. In addition, we design an algorithm for estimating in Phase I, and exploit the estimated to reduce the computational complexity of the identity detector in Phase II. Numerical results demonstrate the effectiveness of the proposed two-stage communication protocol and algorithms.
Paper Structure (16 sections, 36 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 16 sections, 36 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Two-Stage Communication Protocol
  3. Problem of Existing Grant-Free Protocol
  4. Proposed New Communication Protocol
  5. Estimation of Number of Active Devices
  6. Problem Formulation and Estimator Design
  7. Performance of Designed Estimator
  8. Detection of Identities of Active Devices
  9. Problem Formulation
  10. Review of Coordinate Descent (CD) Algorithm in haghighatshoar2018improved
  11. Proposed K-Coordinate Descent Algorithm
  12. A Related Work in wang2023cov
  13. Complexity Analysis
  14. Numerical Results
  15. Performance of K-CD Algorithm
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Identity detection performance in the scenario where the total number of devices $N=1000$ and the number of BS antennas $M=15$.
  • Figure 2: Estimation performance over different $L_{\rm I}$ and $K$ when $N=1000$.
  • Figure 3: Activity detection performance when $K=100$, $L_{\rm I}=4$, $L_{\rm II}=100$, and $M=15$.
  • Figure 4: Number of total coordinate updates and gradient component computation when algorithms meet the termination condition.
  • Figure 5: Performance of the proposed two-stage protocol when $M=32$.