Study and Performance Analysis of LTE MAC Schedulers for M2M

Cellular systems are forecasted to play a fundamental role in the future Machine-to-Machine scenario. The 3GPP LTE networks appear to be the de facto standard for machine type communications. Beside opportunities given by M2M devices spreading, such as vehicular-to-vehicular communications and environmental monitoring, the operators will have to deal with a greater number of devices connected, which don t fulfil with the nowadays human centred traffic standard characteristics and will force to re-standardize the current uplink scheduling procedures. In this report an extensive study of the literature papers, both standards and M2M centred, is carried on a dense machine scenario. Problems such as Human-to-Human throughput drops are high lined with the raise of the number of M2M devices present. Moreover the channel utilization, both in uplink and in downlink, drops drastically among the studied scheduler schemes. A new M2M Aware Scheduler, with the goal of maximise the medium utilization, is designed and implemented. Its results over the simulations shows that M2M and Human-to-Human devices could live over the same LTE Network but new schedulers, such as the one here presented, have to be further studied and analysed. This thesis work was carried on during an exchange period at the Norwegian University of Science and Technology in Trondheim with the collaboration and supervision of Telenor Norge AS

A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled

Simulating device-to-device communications in OMNeT++ with SimuLTE: scenarios and configurations

SimuLTE is a tool that enables system-level simulations of LTE/LTE-Advanced networks within OMNeT++. It is designed such that it can be plugged within network elements as an additional Network Interface Card (NIC) to those already provided by the INET framework (e.g. Wi-Fi). Recently, device-to-device (D2D) technology has been widely studied by the research community, as a mechanism to allow direct communications between devices of a LTE cellular network. In this work, we present how SimuLTE can be employed to simulate both one-to-one and one-to-many D2D communications, so that the latter can be exploited as a new communication opportunity in several research fields, like vehicular networks, IoT and machine-to-machine (M2M) applications

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. 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SymbioCity: Smart Cities for Smarter Networks

The "Smart City" (SC) concept revolves around the idea of embodying cutting-edge ICT solutions in the very fabric of future cities, in order to offer new and better services to citizens while lowering the city management costs, both in monetary, social, and environmental terms. In this framework, communication technologies are perceived as subservient to the SC services, providing the means to collect and process the data needed to make the services function. In this paper, we propose a new vision in which technology and SC services are designed to take advantage of each other in a symbiotic manner. According to this new paradigm, which we call "SymbioCity", SC services can indeed be exploited to improve the performance of the same communication systems that provide them with data. Suggestive examples of this symbiotic ecosystem are discussed in the paper. The dissertation is then substantiated in a proof-of-concept case study, where we show how the traffic monitoring service provided by the London Smart City initiative can be used to predict the density of users in a certain zone and optimize the cellular service in that area.Comment: 14 pages, submitted for publication to ETT Transactions on Emerging Telecommunications Technologie

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

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