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Two-Level Estimation Enabled Online Congestion Control for Massive IoT Networks
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- Authors
- Shilun Song, Jie Liu, Han Seung Jang, Hu Jin
- Issue Date
- 2025-08
- Citation
- IEEE Communications Letters, v.29, no.8, pp.1968-1972
- ISSN
- 1089-7798
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- In the massive Internet of Things (mIoT) scenario, characterized by a burst of access requests, the random access (RA) mechanism faces significant challenges in establishing radio resource control (RRC) connections. Access class barring (ACB) and Backoff are two typical control schemes. Devices first undergo the ACB check, and upon passing, transmit preambles and payloads in a contention-based manner. Failed attempts then enter the Backoff process for retransmission. Maximizing RA efficiency by collaborating these two control schemes is a critical challenge. This letter presents a performance analysis of the coexistence of ACB and Backoff and proposes an optimal control scheme. To enhance practical applicability, a Bayesian estimation based approach is introduced. Simulation results validate the proposed algorithm’s substantial improvement in RA efficiency.
- Keyword
- Access class barring, backoff scheme, Bayesian estimation, massive internet of things
- KSP Keywords
- Access class barring(ACB), Access requests, Based Approach, Bayesian estimation, Congestion control, Contention-based, Control scheme, Internet of Things(IoT), IoT network, Massive IoT, Optimal control