On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time

Random Access Channel (RACH) has been identified as one of the major bottlenecks for accommodating massive number of machine-to-machine (M2M) users in LTE networks, especially for the case of burst arrival of connection requests. As a consequence, the burst resolution problem has sparked a large number of works in the area, analyzing and optimizing the average performance of RACH. However, the understanding of what are the probabilistic performance limits of RACH is still missing. To address this limitation, in the paper, we investigate the reliability of RACH with access class barring (ACB). We model RACH as a queuing system, and apply stochastic network calculus to derive probabilistic performance bounds for burst resolution time, i.e., the worst case time it takes to connect a burst of M2M devices to the base station. We illustrate the accuracy of the proposed methodology and its potential applications in performance assessment and system dimensioning.Comment: Presented at IEEE International Conference on Communications (ICC), 201

Massive M2M Access with Reliability Guarantees in LTE Systems

Machine-to-Machine (M2M) communications are one of the major drivers of the cellular network evolution towards 5G systems. One of the key challenges is on how to provide reliability guarantees to each accessing device in a situation in which there is a massive number of almost-simultaneous arrivals from a large set of M2M devices. The existing solutions take a reactive approach in dealing with massive arrivals, such as non-selective barring when a massive arrival event occurs, which implies that the devices cannot get individual reliability guarantees. In this paper we propose a proactive approach, based on a standard operation of the cellular access. The access procedure is divided into two phases, an estimation phase and a serving phase. In the estimation phase the number of arrivals is estimated and this information is used to tune the amount of resources allocated in the serving phase. Our results show that the proactive approach is instrumental in delivering high access reliability to the M2M devices.Comment: Accepted for presentation in ICC 201

Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications

Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of the biggest factors hindering cellular-based M2M services because the RACH congestion causes random access (RA) throughput degradation and connection failures to the devices. In this paper, we show the possibility exploiting the capture effects, which have been known to have a positive impact on the wireless network system, on RA procedure for improving the RA performance of M2M devices. For this purpose, we analyze an RA procedure using a capture model. Through this analysis, we examine the effects of capture on RA performance and propose an Msg3 power-ramping (Msg3 PR) scheme to increase the capture probability (thereby increasing the RA success probability) even when severe RACH congestion problem occurs. The proposed analysis models are validated using simulations. The results show that the proposed scheme, with proper parameters, further improves the RA throughput and reduces the connection failure probability, by slightly increasing the energy consumption. Finally, we demonstrate the effects of coexistence with other RA-related schemes through simulation results

Optimizing access demand for mMTC traffic using neural networks

Machine-type communications show unique spatial and temporal correlation properties that often lead to bursty access demand profiles. With the expected large-scale deployment of the Internet of Things (IoT), next-generation mobile networks should be redesigned to manage massive, highly synchronized arrivals of access requests by employing efficient access barring schemes. In this work, we first derived the analytical expression of the optimal Access Class Barring (ACB) parameter as standardized by the Third Generation Partnership Project (3GPP). Secondly, we predict the type and number of accessing devices from measurements acquired by the Base Station (BS) by employing Neural Networks (NNs). These estimates are used to effectively implement the optimal barring scheme, achieving performance results close to the theoretical bound.This work was partially supported by the project ROUTE56 (Agencia Estatal de Investigación, PID2019-104945GBI00/AEI/10.13039/501100011033) and by the grant 2021 SGR 01033 (AGAUR, Generalitat de Catalunya). The work of A. Agustin is supported by the Spanish Government through the project 6G AI-native Air Interface (6G-AINA, PID2021-128373OB-I00 funded by MCIN/AEI/10.13039/501100011033) and by ”ERDF A way of making Europe”Peer ReviewedPostprint (author's final draft