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

Fast Adaptive S-ALOHA Scheme for Event-driven Machine-to-Machine Communications

Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for M2M communication systems with bursty traffic. The statistics of consecutive idle and collision slots, rather than the observation in a single slot, are used in FASA to accelerate the tracking process of network status. Furthermore, the fast convergence property of FASA is guaranteed by using drift analysis. Simulation results demonstrate that the proposed FASA scheme achieves near-optimal performance in reducing access delay, which outperforms that of traditional additive schemes such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its robustness even under heavy traffic load in addition to better delay performance.Comment: 5 pages, 3 figures, accepted to IEEE VTC2012-Fal

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

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC

[EN] One important issue that needs to be addressed in order to provide effective massive deployments of IoT devices is access control. In 5G cellular networks, the Access Class Barring (ACB) method aims at increasing the total successful access probability by delaying randomly access requests. This mechanism can be controlled through the barring rate, which can be easily adapted in networks where Human-to-Human (H2H) communications are prevalent. However, in scenarios with massive deployments such as those found in IoT applications, it is not evident how this parameter should be set, and how it should adapt to dynamic traffic conditions. We propose a double deep reinforcement learning mechanism to adapt the barring rate of ACB under dynamic conditions. The algorithm is trained with simultaneous H2H and Machine-to-Machine (M2M) traffic, but we perform a separate performance evaluation for each type of traffic. The results show that our proposed mechanism is able to reach a successful access rate of 100 % for both H2H and M2M UEs and reduce the mean number of preamble transmissions while slightly affecting the mean access delay, even for scenarios with very high load. Also, its performance remains stable under the variation of different parameters. (C) 2019 Elsevier B.V. All rights reserved.The research of D. Pacheco-Paramo was supported by Universidad Sergio Arboleda, P.t. Tecnologias para la inclusion social y la competitividad economica. 0.E.6. The research of L Tello-Oquendo was conducted under project CONV.2018-ING010. Universidad Nacional de Chimborazo. The research of V. Pla and J. Martinez-Bauset was supported by Grant PGC2018-094151-B-I00 (MCIU/AEI/FEDER,UE).Pacheco-Paramo, DF.; Tello-Oquendo, L.; Pla, V.; Martínez Bauset, J. (2019). Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC. Ad Hoc Networks. 94:1-14. https://doi.org/10.1016/j.adhoc.2019.101939S1149

Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios

[EN] One of the three main use cases of the fifth generation of mobile networks (5G) is massive machine-type communications (mMTC). The latter refers to the highly synchronized accesses to the cellular base stations from a great number of wireless devices, as a product of the automated exchange of small amounts of data. Clearly, an efficient mMTC is required to support the Internet-of-Things (IoT). Nevertheless, the method to change from idle to connected mode, known as the random access procedure (RAP), of 4G has been directly inherited by 5G, at least, until the first phase of standardization. Research has demonstrated the RAP is inefficient to support mMTC, hence, access control schemes are needed to obtain an adequate performance. In this paper, we compare the benefits of using different filtering methods to configure an access control scheme included in the 5G standards: the access class barring (ACB), according to the intensity of access requests. These filtering methods are a key component of our proposed ACB configuration scheme, which can lead to more than a three-fold increase in the probability of successfully completing the random access procedure under the most typical network configuration and mMTC scenario.This research has been supported in part by the Ministry of Economy and Competitiveness of Spain under Grant TIN2013-47272-C2-1-R and Grant TEC2015-71932-REDT. The research of I. Leyva-Mayorga was partially funded by grant 383936 CONACYT-GEM 2014.Leyva-Mayorga, I.; Rodríguez-Hernández, MA.; Pla, V.; Martínez Bauset, J. (2019). Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios. Electronics. 8(1):1-18. https://doi.org/10.3390/electronics8010027S11881Laya, A., Alonso, L., & Alonso-Zarate, J. (2014). Is the Random Access Channel of LTE and LTE-A Suitable for M2M Communications? A Survey of Alternatives. IEEE Communications Surveys & Tutorials, 16(1), 4-16. doi:10.1109/surv.2013.111313.00244Biral, A., Centenaro, M., Zanella, A., Vangelista, L., & Zorzi, M. (2015). The challenges of M2M massive access in wireless cellular networks. Digital Communications and Networks, 1(1), 1-19. doi:10.1016/j.dcan.2015.02.001Tello-Oquendo, L., Leyva-Mayorga, I., Pla, V., Martinez-Bauset, J., Vidal, J.-R., Casares-Giner, V., & Guijarro, L. (2018). Performance Analysis and Optimal Access Class Barring Parameter Configuration in LTE-A Networks With Massive M2M Traffic. IEEE Transactions on Vehicular Technology, 67(4), 3505-3520. doi:10.1109/tvt.2017.2776868Tavana, M., Rahmati, A., & Shah-Mansouri, V. (2018). Congestion control with adaptive access class barring for LTE M2M overload using Kalman filters. Computer Networks, 141, 222-233. doi:10.1016/j.comnet.2018.01.044Lin, T.-M., Lee, C.-H., Cheng, J.-P., & Chen, W.-T. (2014). PRADA: Prioritized Random Access With Dynamic Access Barring for MTC in 3GPP LTE-A Networks. IEEE Transactions on Vehicular Technology, 63(5), 2467-2472. doi:10.1109/tvt.2013.2290128De Andrade, T. P. C., Astudillo, C. A., Sekijima, L. R., & Da Fonseca, N. L. S. (2017). The Random Access Procedure in Long Term Evolution Networks for the Internet of Things. IEEE Communications Magazine, 55(3), 124-131. doi:10.1109/mcom.2017.1600555cmWang, Z., & Wong, V. W. S. (2015). Optimal Access Class Barring for Stationary Machine Type Communication Devices With Timing Advance Information. IEEE Transactions on Wireless Communications, 14(10), 5374-5387. doi:10.1109/twc.2015.2437872Tello-Oquendo, L., Pacheco-Paramo, D., Pla, V., & Martinez-Bauset, J. (2018). Reinforcement Learning-Based ACB in LTE-A Networks for Handling Massive M2M and H2H Communications. 2018 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2018.8422167Leyva-Mayorga, I., Rodriguez-Hernandez, M. A., Pla, V., Martinez-Bauset, J., & Tello-Oquendo, L. (2019). Adaptive access class barring for efficient mMTC. Computer Networks, 149, 252-264. doi:10.1016/j.comnet.2018.12.003Kalalas, C., & Alonso-Zarate, J. (2017). Reliability analysis of the random access channel of LTE with access class barring for smart grid monitoring traffic. 2017 IEEE International Conference on Communications Workshops (ICC Workshops). doi:10.1109/iccw.2017.7962744Leyva-Mayorga, I., Tello-Oquendo, L., Pla, V., Martinez-Bauset, J., & Casares-Giner, V. (2016). Performance analysis of access class barring for handling massive M2M traffic in LTE-A networks. 2016 IEEE International Conference on Communications (ICC). doi:10.1109/icc.2016.7510814Arouk, O., & Ksentini, A. (2016). General Model for RACH Procedure Performance Analysis. IEEE Communications Letters, 20(2), 372-375. doi:10.1109/lcomm.2015.2505280Zhang, Z., Chao, H., Wang, W., & Li, X. (2014). Performance Analysis and UE-Side Improvement of Extended Access Barring for Machine Type Communications in LTE. 2014 IEEE 79th Vehicular Technology Conference (VTC Spring). doi:10.1109/vtcspring.2014.7023042Cheng, R.-G., Chen, J., Chen, D.-W., & Wei, C.-H. (2015). Modeling and Analysis of an Extended Access Barring Algorithm for Machine-Type Communications in LTE-A Networks. IEEE Transactions on Wireless Communications, 14(6), 2956-2968. doi:10.1109/twc.2015.2398858Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S., Hearn, R. H., … Goodlin, R. C. (1975). Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE, 63(12), 1692-1716. doi:10.1109/proc.1975.1003