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

5GAuRA. D3.3: RAN Analytics Mechanisms and Performance Benchmarking of Video, Time Critical, and Social Applications

5GAuRA deliverable D3.3.This is the final deliverable of Work Package 3 (WP3) of the 5GAuRA project, providing a report on the project’s developments on the topics of Radio Access Network (RAN) analytics and application performance benchmarking. The focus of this deliverable is to extend and deepen the methods and results provided in the 5GAuRA deliverable D3.2 in the context of specific use scenarios of video, time critical, and social applications. In this respect, four major topics of WP3 of 5GAuRA – namely edge-cloud enhanced RAN architecture, machine learning assisted Random Access Channel (RACH) approach, Multi-access Edge Computing (MEC) content caching, and active queue management – are put forward. Specifically, this document provides a detailed discussion on the service level agreement between tenant and service provider in the context of network slicing in Fifth Generation (5G) communication networks. Network slicing is considered as a key enabler to 5G communication system. Legacy telecommunication networks have been providing various services to all kinds of customers through a single network infrastructure. In contrast, by deploying network slicing, operators are now able to partition one network into individual slices, each with its own configuration and Quality of Service (QoS) requirements. There are many applications across industry that open new business opportunities with new business models. Every application instance requires an independent slice with its own network functions and features, whereby every single slice needs an individual Service Level Agreement (SLA). In D3.3, we propose a comprehensive end-to-end structure of SLA between the tenant and the service provider of sliced 5G network, which balances the interests of both sides. The proposed SLA defines reliability, availability, and performance of delivered telecommunication services in order to ensure that right information is delivered to the right destination at right time, safely and securely. We also discuss the metrics of slicebased network SLA such as throughput, penalty, cost, revenue, profit, and QoS related metrics, which are, in the view of 5GAuRA, critical features of the agreement.Peer ReviewedPostprint (published version

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

Statistical priority-based uplink scheduling for M2M communications

Currently, the worldwide network is witnessing major efforts to transform it from being the Internet of humans only to becoming the Internet of Things (IoT). It is expected that Machine Type Communication Devices (MTCDs) will overwhelm the cellular networks with huge traffic of data that they collect from their environments to be sent to other remote MTCDs for processing thus forming what is known as Machine-to-Machine (M2M) communications. Long Term Evolution (LTE) and LTE-Advanced (LTE-A) appear as the best technology to support M2M communications due to their native IP support. LTE can provide high capacity, flexible radio resource allocation and scalability, which are the required pillars for supporting the expected large numbers of deployed MTCDs. Supporting M2M communications over LTE faces many challenges. These challenges include medium access control and the allocation of radio resources among MTCDs. The problem of radio resources allocation, or scheduling, originates from the nature of M2M traffic. This traffic consists of a large number of small data packets, with specific deadlines, generated by a potentially massive number of MTCDs. M2M traffic is therefore mostly in the uplink direction, i.e. from MTCDs to the base station (known as eNB in LTE terminology). These characteristics impose some design requirements on M2M scheduling techniques such as the need to use insufficient radio resources to transmit a huge amount of traffic within certain deadlines. This presents the main motivation behind this thesis work. In this thesis, we introduce a novel M2M scheduling scheme that utilizes what we term the “statistical priority” in determining the importance of information carried by data packets. Statistical priority is calculated based on the statistical features of the data such as value similarity, trend similarity and auto-correlation. These calculations are made and then reported by the MTCDs to the serving eNBs along with other reports such as channel state. Statistical priority is then used to assign priorities to data packets so that the scarce radio resources are allocated to the MTCDs that are sending statistically important information. This would help avoid exploiting limited radio resources to carry redundant or repetitive data which is a common situation in M2M communications. In order to validate our technique, we perform a simulation-based comparison among the main scheduling techniques and our proposed statistical priority-based scheduling technique. This comparison was conducted in a network that includes different types of MTCDs, such as environmental monitoring sensors, surveillance cameras and alarms. The results show that our proposed statistical priority-based scheduler outperforms the other schedulers in terms of having the least losses of alarm data packets and the highest rate in sending critical data packets that carry non-redundant information for both environmental monitoring and video traffic. This indicates that the proposed technique is the most efficient in the utilization of limited radio resources as compared to the other techniques

Modelling of a Hybrid MAC Protocol for M2M Communications

Machine-to-machine (M2M) communications is one of the enabling technologies for connecting massive number of devices to the Internet of Things (IoT). M2M communications have different characteristics than human-to-human (H2H) communications. In this work, we propose a scalable, hybrid MAC protocol that will satisfy user quality-of-service (QoS) requirements. We model both periodic and nonperiodic traffic. The proposed MAC protocol organizes transmissions into superframes consisting of a number of frames. A machine is assumed to generate a one or zero packet per its period. The machines have been divided into several types according to their packet generation probabilities. The generated packets are classified into different traffic classes according to their tolerance to packet losses and served by a subframe. Further, each subframe is divided into two sub-periods one serving contention and the other reserved traffic of that traffic class. We formulated an optimization problem that minimizes frame length subject to QoS user requirements. Then, we derived packet loss probability for each class as well as total packet loss probability for the optimization. Formulation resulted in a nonlinear optimization problem, but numerical results show that an LP approximation provides a nearly optimal solution. The work also considered the proposed protocol under user mobility. The packet arrival process under user mobility has been derived. Then the performance of the protocol has been evaluated with the contention service under this arrival process. The contention service with and without packet losses have been considered. A priority queueing mechanism also has been studied for M2M communication. The results of this thesis may be useful in the design of M2M communication system

Random Access Analysis for Massive IoT Networks Under a New Spatio-Temporal Model: A Stochastic Geometry Approach

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that faces a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The Random Access CHannel (RACH) procedure is the first step of connection establishment between IoT devices and Base Stations (BSs) in the cellular-based mIoT network, where modelling the interactions between static properties of physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio-temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network is modelled and analyzed based on stochastic geometry in the spatial domain, and the queue evolution is analyzed based on probability theory in the time domain. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with different RACH schemes in each time slot, which offer insights into effectiveness of each RACH scheme. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device. This mathematical model and analytical framework can be applied to evaluate the performance of other types of RACH schemes in the cellular-based networks by simply integrating its preamble transmission principle

Lightly synchronized Multipacket Reception in Machine-Type Communications Networks

Machine Type Communication (MTC) applications were designed to monitor and control elements of our surroundings and environment. MTC applications have a different set of requirements compared to the traditional communication devices, with Machine to Machine (M2M) data being mostly short, asynchronous, bursty and sometimes requiring end-to-end delays below 1ms. With the growth of MTC, the new generation of mobile communications has to be able to present different types of services with very different requirements, i.e. the same network has to be capable of "supplying" connection to the user that just wants to download a video or use social media, allowing at the same time MTC that has completely different requirements, without deteriorating both experiences. The challenges associated to the implementation of MTC require disruptive changes at the Physical (PHY) and Medium Access Control (MAC) layers, that lead to a better use of the spectrum available. The orthogonality and synchronization requirements of the PHY layer of current Long Term Evolution Advanced (LTE-A) radio access network (based on glsofdm and Single Carrier Frequency Domain Equalization (SC-FDE)) are obstacles for this new 5th Generation (5G) architecture. Generalized Frequency Division Multiplexing (GFDM) and other modulation techniques were proposed as candidates for the 5G PHY layer, however they also suffer from visible degradation when the transmitter and receiver are not synchronized, leading to a poor performance when collisions occur in an asynchronous MAC layer. This dissertation addresses the requirements of M2M traffic at the MAC layer applying multipacket reception (MPR) techniques to handle the bursty nature of the traffic and synchronization tones and optimized back-off approaches to reduce the delay. It proposes a new MAC protocol and analyses its performance analytically considering an SC-FDE modulation. The models are validated using a system level cross-layer simulator developed in MATLAB, which implements the MAC protocol and applies PHY layer performance models. The results show that the MAC’s latency depends mainly on the number of users and the load of each user, and can be controlled using these two parameters

Cellular networks for smart grid communication

The next-generation electric power system, known as smart grid, relies on a robust and reliable underlying communication infrastructure to improve the efficiency of electricity distribution. Cellular networks, e.g., LTE/LTE-A systems, appear as a promising technology to facilitate the smart grid evolution. Their inherent performance characteristics and well-established ecosystem could potentially unlock unprecedented use cases, enabling real-time and autonomous distribution grid operations. However, cellular technology was not originally intended for smart grid communication, associated with highly-reliable message exchange and massive device connectivity requirements. The fundamental differences between smart grid and human-type communication challenge the classical design of cellular networks and introduce important research questions that have not been sufficiently addressed so far. Motivated by these challenges, this doctoral thesis investigates novel radio access network (RAN) design principles and performance analysis for the seamless integration of smart grid traffic in future cellular networks. Specifically, we focus on addressing the fundamental RAN problems of network scalability in massive smart grid deployments and radio resource management for smart grid and human-type traffic. The main objective of the thesis lies on the design, analysis and performance evaluation of RAN mechanisms that would render cellular networks the key enabler for emerging smart grid applications. The first part of the thesis addresses the radio access limitations in LTE-based networks for reliable and scalable smart grid communication. We first identify the congestion problem in LTE random access that arises in large-scale smart grid deployments. To overcome this, a novel random access mechanism is proposed that can efficiently support real-time distribution automation services with negligible impact on the background traffic. Motivated by the stringent reliability requirements of various smart grid operations, we then develop an analytical model of the LTE random access procedure that allows us to assess the performance of event-based monitoring traffic under various load conditions and network configurations. We further extend our analysis to include the relation between the cell size and the availability of orthogonal random access resources and we identify an additional challenge for reliable smart grid connectivity. To this end, we devise an interference- and load-aware cell planning mechanism that enhances reliability in substation automation services. Finally, we couple the problem of state estimation in wide-area monitoring systems with the reliability challenges in information acquisition. Using our developed analytical framework, we quantify the impact of imperfect communication reliability in the state estimation accuracy and we provide useful insights for the design of reliability-aware state estimators. The second part of the thesis builds on the previous one and focuses on the RAN problem of resource scheduling and sharing for smart grid and human-type traffic. We introduce a novel scheduler that achieves low latency for distribution automation traffic while resource allocation is performed in a way that keeps the degradation of cellular users at a minimum level. In addition, we investigate the benefits of Device-to-Device (D2D) transmission mode for event-based message exchange in substation automation scenarios. We design a joint mode selection and resource allocation mechanism which results in higher data rates with respect to the conventional transmission mode via the base station. An orthogonal resource partition scheme between cellular and D2D links is further proposed to prevent the underutilization of the scarce cellular spectrum. The research findings of this thesis aim to deliver novel solutions to important RAN performance issues that arise when cellular networks support smart grid communication.Las redes celulares, p.e., los sistemas LTE/LTE-A, aparecen como una tecnología prometedora para facilitar la evolución de la próxima generación del sistema eléctrico de potencia, conocido como smart grid (SG). Sin embargo, la tecnología celular no fue pensada originalmente para las comunicaciones en la SG, asociadas con el intercambio fiable de mensajes y con requisitos de conectividad de un número masivo de dispositivos. Las diferencias fundamentales entre las comunicaciones en la SG y la comunicación de tipo humano desafían el diseño clásico de las redes celulares e introducen importantes cuestiones de investigación que hasta ahora no se han abordado suficientemente. Motivada por estos retos, esta tesis doctoral investiga los principios de diseño y analiza el rendimiento de una nueva red de acceso radio (RAN) que permita una integración perfecta del tráfico de la SG en las redes celulares futuras. Nos centramos en los problemas fundamentales de escalabilidad de la RAN en despliegues de SG masivos, y en la gestión de los recursos radio para la integración del tráfico de la SG con el tráfico de tipo humano. El objetivo principal de la tesis consiste en el diseño, el análisis y la evaluación del rendimiento de los mecanismos de las RAN que convertirán a las redes celulares en el elemento clave para las aplicaciones emergentes de las SGs. La primera parte de la tesis aborda las limitaciones del acceso radio en redes LTE para la comunicación fiable y escalable en SGs. En primer lugar, identificamos el problema de congestión en el acceso aleatorio de LTE que aparece en los despliegues de SGs a gran escala. Para superar este problema, se propone un nuevo mecanismo de acceso aleatorio que permite soportar de forma eficiente los servicios de automatización de la distribución eléctrica en tiempo real, con un impacto insignificante en el tráfico de fondo. Motivados por los estrictos requisitos de fiabilidad de las diversas operaciones en la SG, desarrollamos un modelo analítico del procedimiento de acceso aleatorio de LTE que nos permite evaluar el rendimiento del tráfico de monitorización de la red eléctrica basado en eventos bajo diversas condiciones de carga y configuraciones de red. Además, ampliamos nuestro análisis para incluir la relación entre el tamaño de celda y la disponibilidad de recursos de acceso aleatorio ortogonales, e identificamos un reto adicional para la conectividad fiable en la SG. Con este fin, diseñamos un mecanismo de planificación celular que tiene en cuenta las interferencias y la carga de la red, y que mejora la fiabilidad en los servicios de automatización de las subestaciones eléctricas. Finalmente, combinamos el problema de la estimación de estado en sistemas de monitorización de redes eléctricas de área amplia con los retos de fiabilidad en la adquisición de la información. Utilizando el modelo analítico desarrollado, cuantificamos el impacto de la baja fiabilidad en las comunicaciones sobre la precisión de la estimación de estado. La segunda parte de la tesis se centra en el problema de scheduling y compartición de recursos en la RAN para el tráfico de SG y el tráfico de tipo humano. Presentamos un nuevo scheduler que proporciona baja latencia para el tráfico de automatización de la distribución eléctrica, mientras que la asignación de recursos se realiza de un modo que mantiene la degradación de los usuarios celulares en un nivel mínimo. Además, investigamos los beneficios del modo de transmisión Device-to-Device (D2D) en el intercambio de mensajes basados en eventos en escenarios de automatización de subestaciones eléctricas. Diseñamos un mecanismo conjunto de asignación de recursos y selección de modo que da como resultado tasas de datos más elevadas con respecto al modo de transmisión convencional a través de la estación base. Finalmente, se propone un esquema de partición de recursos ortogonales entre enlaces celulares y D2Postprint (published version

Priority-based initial access for URLLC traffic in massive IoT networks: Schemes and performance analysis

At a density of one million devices per square kilometer, the10’s of billions of devices, objects, and machines that form a massive Internet of things (mIoT) require ubiquitous connectivity. Among a massive number of IoT devices, a portion of them require ultra-reliable low latency communication (URLLC) provided via fifth generation (5G) networks, bringing many new challenges due to the stringent service requirements. Albeit a surge of research efforts on URLLC and mIoT, access mechanisms which include both URLLC and massive machine type communications (mMTC) have not yet been investigated in-depth. In this paper, we propose three novel schemes to facilitate priority-based initial access for mIoT/mMTC devices that require URLLC services while also considering the requirements of other mIoT/mMTC devices. Based on a long term evolution-advanced (LTEA) or 5G new radio frame structure, the proposed schemes enable device grouping based on device vicinity or/and their URLLC requirements and allocate dedicated preambles for grouped devices supported by flexible slot allocation for random access. These schemes are able not only to increase the reliability and minimize the delay of URLLC devices but also to improve the performance of all involved mIoT devices. Furthermore, we evaluate the performance of the proposed schemes through mathematical analysis as well as simulations and compare the results with the performance of both the legacy LTE-A based initial access scheme and a grant-free transmission scheme.acceptedVersio