Allocation of control resources for machine-to-machine and human-to-human communications over LTE/LTE-A networks

The Internet of Things (IoT) paradigm stands for virtually interconnected objects that are identifiable and equipped with sensing, computing, and communication capabilities. Services and applications over the IoT architecture can take benefit of the long-term evolution (LTE)/LTE-Advanced (LTE-A), cellular networks to support machine-type communication (MTC). Moreover, it is paramount that MTC do not affect the services provided for traditional human-type communication (HTC). Although previous studies have evaluated the impact of the number of MTC devices on the quality of service (QoS) provided to HTC users, none have considered the joint effect of allocation of control resources and the LTE random-access (RA) procedure. In this paper, a novel scheme for resource allocation on the packet downlink (DL) control channel (PDCCH) is introduced. This scheme allows PDCCH scheduling algorithms to consider the resources consumed by the random-access procedure on both control and data channels when prioritizing control messages. Three PDCCH scheduling algorithms considering RA-related control messages are proposed. Moreover, the impact of MTC devices on QoS provisioning to HTC traffic is evaluated. Results derived via simulation show that the proposed PDCCH scheduling algorithms can improve the QoS provisioning and that MTC can strongly impact on QoS provisioning for real-time traffic.The Internet of Things (IoT) paradigm stands for virtually interconnected objects that are identifiable and equipped with sensing, computing, and communication capabilities. Services and applications over the IoT architecture can take benefit of the long-33366377CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOsem informaçãosem informaçã

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

Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability

The fifth generation (5G) mobile telecommunication network is expected to support Multi- Access Edge Computing (MEC), which intends to distribute computation tasks and services from the central cloud to the edge clouds. Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services, the current mobile network security architecture should enable a more decentralized approach for authentication and authorization processes. This paper proposes a novel decentralized authentication architecture that supports flexible and low-cost local authentication with the awareness of context information of network elements such as user equipment and virtual network functions. Based on a Markov model for backhaul link quality, as well as a random walk mobility model with mixed mobility classes and traffic scenarios, numerical simulations have demonstrated that the proposed approach is able to achieve a flexible balance between the network operating cost and the MEC reliability.Comment: Accepted by IEEE Access on Feb. 02, 201

Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks

The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method

Goodbye, ALOHA!

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The vision of the Internet of Things (IoT) to interconnect and Internet-connect everyday people, objects, and machines poses new challenges in the design of wireless communication networks. The design of medium access control (MAC) protocols has been traditionally an intense area of research due to their high impact on the overall performance of wireless communications. The majority of research activities in this field deal with different variations of protocols somehow based on ALOHA, either with or without listen before talk, i.e., carrier sensing multiple access. These protocols operate well under low traffic loads and low number of simultaneous devices. However, they suffer from congestion as the traffic load and the number of devices increase. For this reason, unless revisited, the MAC layer can become a bottleneck for the success of the IoT. In this paper, we provide an overview of the existing MAC solutions for the IoT, describing current limitations and envisioned challenges for the near future. Motivated by those, we identify a family of simple algorithms based on distributed queueing (DQ), which can operate for an infinite number of devices generating any traffic load and pattern. A description of the DQ mechanism is provided and most relevant existing studies of DQ applied in different scenarios are described in this paper. In addition, we provide a novel performance evaluation of DQ when applied for the IoT. Finally, a description of the very first demo of DQ for its use in the IoT is also included in this paper.Peer ReviewedPostprint (author's final draft

Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.Comment: Submitted for conference publicatio

Architectures and Key Technical Challenges for 5G Systems Incorporating Satellites

Satellite Communication systems are a promising solution to extend and complement terrestrial networks in unserved or under-served areas. This aspect is reflected by recent commercial and standardisation endeavours. In particular, 3GPP recently initiated a Study Item for New Radio-based, i.e., 5G, Non-Terrestrial Networks aimed at deploying satellite systems either as a stand-alone solution or as an integration to terrestrial networks in mobile broadband and machine-type communication scenarios. However, typical satellite channel impairments, as large path losses, delays, and Doppler shifts, pose severe challenges to the realisation of a satellite-based NR network. In this paper, based on the architecture options currently being discussed in the standardisation fora, we discuss and assess the impact of the satellite channel characteristics on the physical and Medium Access Control layers, both in terms of transmitted waveforms and procedures for enhanced Mobile BroadBand (eMBB) and NarrowBand-Internet of Things (NB-IoT) applications. The proposed analysis shows that the main technical challenges are related to the PHY/MAC procedures, in particular Random Access (RA), Timing Advance (TA), and Hybrid Automatic Repeat reQuest (HARQ) and, depending on the considered service and architecture, different solutions are proposed.Comment: Submitted to Transactions on Vehicular Technologies, April 201