Analyzing Random Access Collisions in Massive IoT Networks

Abstract

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see Abstract-The cellular-based infrastructure is regarded as one of potential solutions for massive Internet of Things (mIoT), where the Random Access (RA) procedure is used for requesting channel resources in the uplink data transmission. Due to the nature of mIoT network with the sporadic uplink transmissions of a large amount of IoT devices, massive concurrent channel resource requests lead to a high probability of RA failure. To relieve the congestion during the RA in mIoT networks, we model RA procedure, and analyze as well as evaluate the performance improvement due to different RA schemes, including power ramping (PR), back-off (BO), access class barring (ACB), hybrid ACB and back-off schemes (ACB&BO), and hybrid power ramping and back-off (PR&BO). To do so, we develop a traffic-aware spatio-temporal model for the contention-based RA analysis in the mIoT network, where the signal-to-noiseplus-interference ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared with existing literature only modelled collision from single cell perspective, we model both SINR outage and the collision from the network perspective. Based on this analytical model, we derive the analytical expression for the RA success probabilities to show the effectiveness of different RA schemes. We also derive the average queue lengths and the average waiting delays of each RA scheme to evaluate the packets accumulation status and packets serving efficiency. Our results show that our proposed PR&BO scheme outperforms other schemes in heavy traffic scenario in terms of the RA success probability, the average queue length, and the average waiting delay.

Physical layer security for massive access in cellular Internet of Things

“…Thus, the BS should first detect which devices are active, and then performs data transmission. However, the conventional multiple access protocols, e.g., ALOHA, which limit the number of access devices and require a high signalling overhead, are no longer really fit for the massive access systems [44,45]. To solve this problem, the grant-free random access protocol is widely applied in the massive access systems [46,47].…”

Spatiotemporal Model for Uplink IoT Traffic: Scheduling and Random Access Paradox

“…The work in [23] addresses downlink scheduling, which does not involve an RA-SR phase because the BS encloses the data queue and is responsible for scheduling. The work in [24], [25] analyzes the Random Access CHannel (RACH) performance in cellular-based IoT networks which does not involve EA-Tx phase for data transmission. The UL scenario where the data queue is at the device side is considered in [26].…”

“…The average value, the variance, and the index of dispersion can be computed by substituting (28) in (24), (25), and (26), respectively. 2) RA-UL Scheme: The spatiotemporal iterative algorithm for the SC-UL scheme is given in the following corollary.…”

Analysis of Random Access in NB-IoT Networks With Three Coverage Enhancement Groups: A Stochastic Geometry Approach

“…Our previous work [21] has provided a general analytical framework to characterize the RACH success probability in NB-IoT networks with preamble repetition scheme based on the preamble transmission model in [22] and collision model in [23]. Note that [21] only considered NB-IoT networks with a single CE group in a single time slot with the transmit power of the IoT device determined by the path-loss inversion power control due to the analytical simplicity, which does not align with the practical NB-IoT networks with multiple CE groups setting.…”