Performance analysis of an enhanced delay sensitive LTE uplink scheduler for M2M traffic

The Long Term Evolution (LTE) standard is one of the most promising wireless access technologies for Machine to Machine (M2M) communications because of its high data rates, low latency and economies of scale. M2M communications typically involves a large number of autonomous devices sending traffic in a coordinated manner (and possibly even simultaneously), therefore creating an uplink-heavy trend which needs an efficient radio resource management scheme. The conventional scheduling algorithms and performance metrics are not suitable for M2M systems because of the different characteristics and service requirements of M2M traffic. In this paper, we analyze the performance of an enhanced delay sensitive uplink scheduler in context of LTE TDD configurations 0 and 1 for delay sensitive event based M2M traffic. We show that unlike an ordinary equal capacity fair scheduler, our proposed delay sensitive scheduler can make utmost use of the maximally uplink-biased TDD configuration 0, attaining higher capacity and maximizing the chance of satisfying packet delay budget of M2M traffic. We also introduce a new performance metric called 'Effective Allocated Bits/RB pair' to measure the allocation efficiency of a scheduler, evaluate the performance of the proposed scheduler in terms of this metric and identify the scope of possible improvements. © 2013 IEEE

On the Feasibility of Utilizing Commercial 4G LTE Systems for Misson-Critical IoT Applications

Emerging Internet of Things (IoT) applications and services including e-healthcare, intelligent transportation systems, smart grid, and smart homes to smart cities to smart workplace, are poised to become part of every aspect of our daily lives. The IoT will enable billions of sensors, actuators, and smart devices to be interconnected and managed remotely via the Internet. Cellular-based Machine-to-Machine (M2M) communications is one of the key IoT enabling technologies with huge market potential for cellular service providers deploying Long Term Evolution (LTE) networks. There is an emerging consensus that Fourth Generation (4G) and 5G cellular technologies will enable and support these applications, as they will provide the global mobile connectivity to the anticipated tens of billions of things/devices that will be attached to the Internet. Many vital utilities and service industries are considering the use of commercially available LTE cellular networks to provide critical connections to users, sensors, and smart M2M devices on their networks, due to its low cost and availability. Many of these emerging IoT applications are mission-critical with stringent requirements in terms of reliability and end-to-end (E2E) delay bound. The delay bound specified for each application refers to the device-to-device latencies, which is defined as the combined delay resulting from both application level processing time and communication latency. Each IoT application has its own distinct performance requirements in terms of latency, availability, and reliability. Typically, uplink (UL) traffic of most of these IoT applications is the dominant network traffic (much higher than total downlink (DL) traffic). Thus, efficient LTE UL scheduling algorithms at the base station (“Evolved NodeB (eNB)” per 3GPP standards) are more critical for M2M applications. LTE, however, was not originally intended for IoT applications, where traffic generated by M2M devices (running IoT applications) has totally different characteristics than those from traditional Human-to-Human (H2H)-based voice/video and data communications. In addition, due to the anticipated massive deployment of M2M devices and the limited available radio spectrum, the problem of efficient radio resources management (RRM) and UL scheduling poses a serious challenge in adopting LTE for M2M communications. Existing LTE quality of service (QoS) standard and UL scheduling algorithms were mainly optimized for H2H services and can’t accommodate such a wide range of diverging performance requirements of these M2M-based IoT applications. Though 4G LTE networks can support very low Packet Loss Ratio (PLR) at the physical layer, such reliability, however, comes at the expense of increased latency from tens to hundreds of ms due to the aggressive use of retransmission mechanisms. Current 4G LTE technologies may satisfy a single performance metric of these mission critical applications, but not the simultaneous support of ultra-high reliability and low latency as well as high data rates. Numerous QoS aware LTE UL scheduling algorithms for supporting M2M applications as well as H2H services have been reported in the literature. Most of these algorithms, however, were not intended for the support of mission critical IoT applications, as they are not latency-aware. In addition, these algorithms are simplified and don’t fully conform to LTE’s signaling and QoS standards. For instance, a common practice is the assumption that the time domain UL scheduler located at the eNB prioritizes user equipment (UEs)/M2M devices connection requests based on the head-of-line (HOL) packet waiting time at the UE/device transmission buffer. However, as will be detailed below, LTE standard does not support a mechanism that enables the UEs/devices to inform the eNB uplink scheduler about the waiting time of uplink packets residing in their transmission buffers. Ultra-Reliable Low-Latency Communication (URLLC) paradigm has recently emerged to enable a new range of mission-critical applications and services including industrial automation, real-time operation and control of the smart grid, inter-vehicular communications for improved safety and self-deriving vehicles. URLLC is one of the most innovative 5G New Radio (NR) features. URLLC and its supporting 5G NR technologies might become a commercial reality in the future, but it may be rather a distant future. Thus, deploying viable mission critical IoT applications will have to be postponed until URLLC and 5G NR technologies are commercially feasible. Because IoT applications, specifically mission critical, will have a significant impact on the welfare of all humanity, the immediate or near-term deployments of these applications is of utmost importance. It is the purpose of this thesis to explore whether current commercial 4G LTE cellular networks have the potential to support some of the emerging mission critical IoT applications. Smart grid is selected in this work as an illustrative IoT example because it is one of the most demanding IoT applications, as it includes diverse use cases ranging from mission-critical applications that have stringent requirements in terms of E2E latency and reliability to those that require support of massive number of connected M2M devices with relaxed latency and reliability requirements. The purpose of thesis is two fold: First, a user-friendly MATLAB-based open source software package to model commercial 4G LTE systems is developed. In contrast to mainstream commercial LTE software packages, the developed package is specifically tailored to accurately model mission critical IoT applications and above all fully conforms to commercial 4G LTE signaling and QoS standards. Second, utilizing the developed software package, we present a detailed realistic LTE UL performance analysis to assess the feasibility of commercial 4G LTE cellular networks when used to support such a diverse set of emerging IoT applications as well as typical H2H services

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

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

Redução de sinalização e agendador de recursos periódicos para redes industriais M2M

Devido à introdução de novas funcionalidades e aplicações M2M (Machine-to-Machine) na indústria, novos dispositivos precisarão ser conectados às redes de comunicação existentes, coexistindo com os atuais. As novas tecnologias de comunicação móvel, que ainda estão em processo de padronização, prometem cumprir com os requisitos de comunicação exigidos pelos sistemas de automação industrial. Portanto, esta tese tem como principal contribuição analisar a comunicação industrial M2M, sua viabilidade, implantação, características, limitações e se é adequada para os cenários, requisitos e exigências industriais. Além disso, também é realizado o desenvolvimento de um algoritmo de agendamento para dispositivos que necessitam de recursos periódicos de rede. Esse algoritmo tem o objetivo de reduzir a sinalização de rede, aumentar a capacidade de transmissão de dados, ou ainda, aumentar o número de dispositivos operantes na rede industrial. Os métodos propostos são validados usando uma estrutura de simulação LTE-A e métricas de rede tais como: atraso e jitter as quais são usadas para comparar o desempenho da abordagem proposta com os atuais agendadores de última geração. Os resultados mostram que a aplicação do agendador de recursos cíclicos e o esquema de redução de sinalização juntos podem melhorar a função de utilização do sistema bem como a satisfação dos usuários.Due to the introduction of new M2M (Machine-to-Machine) functionalities and applications in the industry, new devices will need to be connected to existing communication networks, coexisting with current ones. The new mobile communication technologies, which are still in the standardization process, promise to comply with the communication requirements demanded by industrial automation systems. Therefore, this thesis has as its main contribution to analyze the M2M communication, its feasibility, implantation, characteristics, limitations and if it is adequate for the scenarios, requirements and industrial needs. In addition, it is also developed a scheduling algorithm for devices that use periodic network resources. This algorithm aims to reduce network signaling, increase data transmission capacity, or increase the number of devices operating in the industrial network. The proposed methods are validated using a LTE-A simulation framework and network metrics such as delay and jitter, which are used to compare the performance of the proposed approach with the current state-of-the-art schedulers. The results show that the application of the cyclic resource scheduler and the signaling reduction scheme together can improve the system’s utilization function as well as user satisfaction