Contention Resolution Queues for Massive Machine Type Communications in LTE

In this paper, we address the challenge of high device density performing simultaneous transmissions by proposing and evaluating a solution to efficiently handle the initial access contention for highly dense LTE networks. We present the implementation of a tree-splitting algorithm in the access procedure of LTE, which is capable to cope with high number of simultaneous arrivals. Based on simulations we show a feasible implementation capable to achieve, under certain network configuration conditions, up to 85% average access delay reduction and 40% reduction on the average energy consumption, while maintaining a consistently low blocking probability, regardless of the number of initial simultaneous access attempts

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

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

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

Low-latency Networking: Where Latency Lurks and How to Tame It

While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission critical communication services requiring latency in the order of milliseconds or sub-milliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of sub-milliseconds latency. In this article, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent trade-offs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research

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

Random Access Procedure for Machine Type Communication in Mobile Networks

Komunikace strojů (Machine-type communication, MTC) v mobilních sítích může vést k velkému množství požadavků na přístup k médiu a způsobit tak krátkodobá, ale častá přetížení sítě. Velké množství MTC zařízení přistupujících náhodně k radiovému kanálu vede k vysoké pravděpodobnosti kolize a neúnosné době přístupu k médiu, jelikož velké množství MTC zařízení přistupuje ke sdílenému kanálu pro náhodný přístup (Random Access Channel, RACH), který má však omezenou kapacitu. Tato diplomová práce se zabývá novou procedurou dvoufázového náhodného přístupu (Two-Phase Random Access, TPRA). Navržená procedura TPRA pro přístup MTC zařízení v mobilních sítích umožňuje snížit zátěž kanálu pro náhodný přístup tím, že redukuje pravděpodobnost kolize mezi MTC zařízeními při jejich přístupu k radiovým prostředkům. Toho je dosaženo rozdělením všech zařízení do malých skupin. Navržený koncept umožňuje základnové stanici přizpůsobit počet přístupových kanálů podle jejich aktuálního zatížení. V práci je dále navržen analytický model k vyhodnocení výkonnosti navržené procedury TPRA ve smyslu pravděpodobnosti úspěšného přístupu a doby přístupu. Výsledky simulací potvrzují přesnost těchto metrik odvozených analyticky. Výsledky dále ukazují, že TPRA umožnuje zvýšit pravděpodobnost úspěšného přístupu o 9% a zároveň snížit dobu přístupu o 50% pro vysokou hustotu MTC zařízení v porovnání se standardní LTE-A procedurou náhodného přístupu.Machine-type communication (MTC) can generate numerous connection requests and bring explosive load within small time interval. A massive amount of simultaneous random access attempts results in a high collision probability and intolerable access delay because more devices contend in shared random access channels (RACH) with limited capacity. Thus, this thesis addressed a novel mechanism, denoted as two-phase random access (TPRA) procedure, for MTC in mobile networks to relieve the load of RACH. The proposed TPRA reduces probability of collision among the MTC devices when accessing radio resources by separation of the massive number of devices into small groups. The proposed concept allows a base station to adjust the number of additional access channels according to their current load. Furthermore, we propose an analytical model to evaluate the performance of the proposed TPRA by estimating the access success probability and average access delay. The simulations results validate the accuracy of the performance metrics derived analytically. The results further demonstrate that the proposed TPRA can improve the access success probability by 9% and reduce the access delay by 50% for a high density of the MTC devices comparing to the standard LTE-A random access procedure