2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net) 2018
DOI: 10.23919/medhocnet.2018.8407086
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Dynamic access class barring parameter tuning in LTE-A networks with massive M2M traffic

Abstract: Machine-to-machine (M2M) communication is one of the leading facilitators of the Internet of Things environment by offering ubiquitous applications and services. Using cellular networks for providing M2M connectivity brings several advantages such as extended coverage, security, robust management, and lower deployment costs; however, coexistence with a large number of M2M devices is still an essential challenge in LTE-A networks, in part due to the difficulty in allowing simultaneous access. Although the rando… Show more

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Cited by 25 publications
(16 citation statements)
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“…The devices that receive this message execute the Access Class Barring (ACB) algorithm. ACB consists of the devices generating a random number between 0 and 1 [ 4 ]. If the generated number is equal or smaller than the access probability sent by the gNB, the devices can access the network.…”
Section: Nr Random-access Proceduresmentioning
confidence: 99%
See 2 more Smart Citations
“…The devices that receive this message execute the Access Class Barring (ACB) algorithm. ACB consists of the devices generating a random number between 0 and 1 [ 4 ]. If the generated number is equal or smaller than the access probability sent by the gNB, the devices can access the network.…”
Section: Nr Random-access Proceduresmentioning
confidence: 99%
“…The devices generate a random number g = U [0, 1). If g ≤ P ACB , the devices transmit a selected preamble; otherwise, the devices wait for a random time ( back-off ) calculated as T barring = [0.7 + 0.6 U [0, 1)] T ACB [ 4 ].…”
Section: Nr Random-access Proceduresmentioning
confidence: 99%
See 1 more Smart Citation
“…In [3], p ACB is increased and decreased in a heuristic manner depending on whether the average number of colliding preambles in the last three slots (also known as Random Access Opportunities, or RAOs) exceeds and drops below certain thresholds, respectively. [4]- [8] on the other hand, introduce innovative methods to estimate the number of backlogged MTDs from the numbers of colliding, singleton, and idle preambles, and the used p ACB in previous RAO(s). Based on such estimates, p ACB for the next RAO can be set correspondingly so that the average number of transmitting MTDs is kept at an optimum.…”
Section: Introductionmentioning
confidence: 99%
“…[9] employs a Kalman filter to refine the estimate further after the arrival period is over, and there is no new MTDs. It is worth noting that except for [8] which assumes a fixed T ACB , most of the works assume that MTDs who fail the barring check in an RAO will retry in the very next RAO (which effectively means discarding the T ACB ) to simplify the backlog estimation process. The effect of different combinations of p ACB and T ACB in the baseline ACB scheme has been investigated in [10].…”
Section: Introductionmentioning
confidence: 99%