Reliable machine-to-machine multicast services with multi-radio cooperative retransmissions

The final publication is available at Springer via http://dx.doi.org/10.1007/s11036-015-0575-6The 3GPP is working towards the definition of service requirements and technical solutions to provide support for energy-efficient Machine Type Communications (MTC) in the forthcoming generations of cellular networks. One of the envisioned solutions consists in applying group management policies to clusters of devices in order to reduce control signaling and improve upon energy efficiency, e.g., multicast Over-The-Air (OTA) firmware updates. In this paper, a Multi-Radio Cooperative Retransmission Scheme is proposed to efficiently carry out multicast transmissions in MTC networks, reducing both control signaling and improving energy-efficiency. The proposal can be executed in networks composed by devices equipped with multiple radio interfaces which enable them to connect to both a cellular access network, e.g., LTE, and a short-range MTC area network, e.g., Low-Power Wi-Fi or ZigBee, as foreseen by the MTC architecture defined by ETSI. The main idea is to carry out retransmissions over the M2M area network upon error in the main cellular link. This yields a reduction in both the traffic load over the cellular link and the energy consumption of the devices. Computer-based simulations with ns-3 have been conducted to analyze the performance of the proposed scheme in terms of energy consumption and assess its superior performance compared to non-cooperative retransmission schemes, thus validating its suitability for energy-constrained MTC applications.Peer ReviewedPostprint (author's final draft

Dynamic RACH Partition for Massive Access of Differentiated M2M Services

In machine-to-machine (M2M) networks, a key challenge is to overcome the overload problem caused by random access requests from massive machine-type communication (MTC) devices. When differentiated services coexist, such as delay-sensitive and delay-tolerant services, the problem becomes more complicated and challenging. This is because delay-sensitive services often use more aggressive policies, and thus, delay-tolerant services get much fewer chances to access the network. To conquer the problem, we propose an efficient mechanism for massive access control over differentiated M2M services, including delay-sensitive and delay-tolerant services. Specifically, based on the traffic loads of the two types of services, the proposed scheme dynamically partitions and allocates the random access channel (RACH) resource to each type of services. The RACH partition strategy is thoroughly optimized to increase the access performances of M2M networks. Analyses and simulation demonstrate the effectiveness of our design. The proposed scheme can outperform the baseline access class barring (ACB) scheme, which ignores service types in access control, in terms of access success probability and the average access delay

On the Support of Massive Machine-to-Machine Traffic in Heterogeneous Networks and Fifth-Generation Cellular Networks

The widespread availability of many emerging services enabled by the Internet of Things (IoT) paradigm passes through the capability to provide long-range connectivity to a massive number of things, overcoming the well-known issues of ad-hoc, short-range networks. This scenario entails a lot of challenges, ranging from the concerns about the radio access network efficiency to the threats about the security of IoT networks. In this thesis, we will focus on wireless communication standards for long-range IoT as well as on fundamental research outcomes about IoT networks. After investigating how Machine-Type Communication (MTC) is supported nowadays, we will provide innovative solutions that i) satisfy the requirements in terms of scalability and latency, ii) employ a combination of licensed and license-free frequency bands, and iii) assure energy-efficiency and security

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 enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC) and Ultra-Reliable and Low Latency Communications (URLLC), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include Quality of Service (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 Narrowband IoT (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 scenario along with the recent advances towards enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions

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

Monitoring and testing in LTE networks: from experimental analysis to operational optimisation

L'avvento di LTE e LTE-Adavanced, e la loro integrazione con le esistenti tecnologie cellulari, GSM e UMTS, ha costretto gli operatori di rete radiomobile ad eseguire una meticolosa campagna di test e a dotarsi del giusto know-how per rilevare potenziali problemi durante il dispiegamento di nuovi servizi. In questo nuovo scenario di rete, la caratterizzazione e il monitoraggio del traffico nonchè la configurazione e l'affidibilità degli apparati di rete, sono di importanza fondamentale al fine di prevenire possibili insidie durante la distribuzione di nuovi servizi e garantire la migliore esperienza utente possibile. Sulla base di queste osservazioni, questa tesi di dottorato offre un percorso completo di studio che va da un'analisi sperimentale ad un'ottimizzazione operativa. Il punto di partenza del nostro lavoro è stato il monitoraggio del traffico di un eNodeB di campo con tre celle, operativo nella banda 1800 MHz. Tramite campagne di misura successive, è stato possibile seguire l'evoluzione della rete 4G dagli albori del suo dispiegamento nel 2012, fino alla sua completa maturazione nel 2015. I dati raccolti durante il primo anno, evidenziavano uno scarso utilizzo della rete LTE, dovuto essenzialmente alla limitata penetrazione dei nuovi smartphone 4G. Nel 2015, invece, abbiamo assistito ad un aumento netto e decisivo del numero di utenti che utilizzano la tecnolgia LTE, con statistiche aggregate (come gli indici di marketshare per i sistemi operativi degli smartphones, o la percentuale di traffico video) che rispecchiano i trend nazionali e internazionali. Questo importante risultato testimonia la maturità della tecnologia LTE, e ci permette di considerare il nostro eNodeB un punto di osservazione prezioso per l'analisi del traffico. Di pari passo con l'evoluzione dell'infrastruttura, anche i telefoni cellulari hanno avuto una sorprendente evoluzione nel corso degli ultimi due decenni, a partire da dispositivi semplici con servizi di sola voce, fino agli smartphone di ultima generazione che offrono servizi innovativi, come Internet mobile, geolocalizzazione e mappe, servizi multimediali, e molti altri. Monitorare il traffico reale ci ha quindi permesso di studiare il comportamento degli utenti e individuare i servizi maggiormente utilizzati. Per questo, sono state sviluppate diverse librerie software per l'analisi del traffico. In particolare, è stato sviluppato in C++14 un framework/tool per la classificazione del traffico. Il progetto, disponibile su github, si chiama MOSEC, un acronimo per MOdular SErvice Classifier. MOSEC consente di definire e utilizzare un numero arbitrario di plug-in, che processano il pacchetto secondo le loro logiche e possono o no ritornare un valore di classificazione. Una strategia di decisione finale consente di classificare i vari flussi, basandosi sulle classificazioni di ciascun plug-in. Abbiamo quindi validato la bontà del processi di classificazione di MOSEC utilizzando una traccia labellata come ground-truth di classificazione. I risultati mostrano una eccellente capacità di classificazione di traffico TCP-HTTP/HTTPS, mediamente superiore a quella di altri tool di classificazione (nDPI, PACE, Layer-7), ed evidenzia alcune lacune per quanto riguarda la classificazione di traffico UDP. Le carattistiche dei flussi di traffico utente (User Plane) hanno un impatto diretto sul consumo energetico dei terminali e indiretto sul traffico di controllo (Control Plane) che viene generato. Pertanto, la conoscenza delle proprietà statistiche dei vari flussi consente di affrontare un problema del cross-layer optimization, per ridurre il consumo energetico dei terminali variando dei parametri configurabili sugli eNodeB. E' noto che la durata della batteria dei nuovi smartphone, rappresenta uno dei maggiori limiti nell'utilizzo degli stessi. In particolare, lo sviluppo di nuovi servizi e applicazioni capaci di lavorare in background, senza la diretta interazione dell’utente, ha introdotto nuovi problemi riguardanti la durata delle batterie degli smartphone e il traffico di segnalazione necessario ad acquisire/rilasciare le risorse radio. In conformità a queste osservazioni, è stato condotto uno studio approfondito sul meccanismo DRX (Discontinuous Reception), usato in LTE per consentire all’utente di risparmiare energia quando nessun pacchetto è inviato o ricevuto. I parametri DRX e RRC Inactivity Timer influenzano notevolmente l’energia consumata dai vari device. A seconda che le risorse radio siano assegnate o meno, l’UE si trova rispettivamente negli stati di RRC Connected e RRC Idle. Per valutare il consumo energetico degli smartphone, è stato sviluppato un algoritmo che associa un valore di potenza a ciascuno degli stati in cui l’UE può trovarsi. La transizione da uno stato all’altro è regolata da diversi timeout che sono resettati ogni volta che un pacchetto è inviato o ricevuto. Utilizzando le tracce di traffico reale, è stata associata una macchina a stati a ogni UE per valutare il consumo energetico sulla base dei pacchetti inviati e ricevuti. Osservando le caratteristiche statistiche del traffico User Plane è stata ripetuta la simulazione utilizzando dei valori dell’Inactivity Timer diversi da quello impiegato negli eNodeB di rete reale, alla ricerca di un buon trade-off tra risparmio energetico e aumento del traffico di segnalazione. I risultati hanno permesso di determinare che l'Inactivity Timer, settato originariamente sull'eNodeB era troppo elevato e determinava un consumo energetico eccesivo sui terminali. Diminuendone il valore fino a 10 secondi, si può ottenere un risparmio energetico fino al 50\% (a secondo del traffico generato) senza aumentare considerevolemente il traffico di controllo. I risultati dello studio di cui sopra, tuttavia, non tengono in considerazione lo stato di stress cui può essere sottoposto un eNodeB per effetto dell'aumento del traffico di segnalazione, nè, tantomeno, dell'aumento della contesa di accesso alla rete durante la procedura di RACH, necessaria per ristabilire il bearer radio (o connessione RRC) tra terminale ed eNodeB. Valutare le performance di sistemi hardware e software per la rete mobile di quarta generazione, cosi come individuare qualsiasi possibile debolezza all’interno dell’architettura, è un lavoro complesso. Un possibile caso di studio, è proprio quello di valutare la robustezza delle Base Station quando riceve molte richieste di connessioni RRC, per effetto di una diminuzione dell'Inactivity Timer. A tal proposito, all’interno del Testing LAB di Telecom Italia, abbiamo utilizzato IxLoad, un prodotto sviluppato da Ixia, come generatore di carico per testare la robustezza di un eNodeB. I test sono consistiti nel produrre un differente carico di richieste RRC sull'interfaccia radio, similmente a quelle che si avrebbero diminuendo l'Inactivity Timer. Le proprietà statistiche del traffico di controllo sono ricavate a partire dall'analisi dalle tracce di traffico reale. I risultati hanno dimostrato che, anche a fronte di un carico sostenuto di richieste RRC solo una minima parte (percentuale inferiore all'1\% nel caso più sfavorevole) di procedure fallisce. Abbassare l'inactivity timer anche a valori inferiori ai 10 secondi non è quindi un problema per la Base Station. Rimane da valutare, infine, cosa succede a seguito dell'aumento delle richieste di accesso al canale RACH, dal punto di vista degli utenti. Quando due o più utenti tentano, simultaneamente, di accedere al canale RACH, utilizzando lo stesso preambolo, l’eNodeB potrebbe non essere in grado di decifrare il preambolo. Se i due segnali interferiscono costruttivamente, entrambi gli utenti riceveranno le stesse risorse per trasmettere il messaggio di RRC Request e, a questo punto, l’eNodeB può individuare la collisione e non trasmetterà nessun acknowledgement, forzando entrambi gli utenti a ricominciare la procedura dall’inizio. Abbiamo quindi proposto un modello analitico per calcolare la probabilità di collisione in funzione del numero di utenti e del carico di traffico offerto, quando i tempi d’interarrivo tra richieste successive é modellata con tempi iper-esponenziali. In più, abbiamo investigato le prestazioni di comunicazioni di tipo Machine-to-Machine (M2M) e Human-to-Human (H2H), valutando, al variare del numero di preamboli utilizzati, la probabilità di collisione su canale RACH, la probabilità di corretta trasmissione considerando sia il tempo di backoff che il numero massimo di ritrasmissioni consentite, e il tempo medio necessario per stabilire un canale radio con la rete di accesso. I risultati, valutati nel loro insieme, hanno consentito di esprimere delle linee guida per ripartire opportunamente il numero di preamboli tra comunicazioni M2M e H2H. The advent of LTE and LTE-Advanced, and their integration with existing cellular technologies, GSM and UMTS, has forced the mobile radio network operators to perform meticulous tests and adopt the right know-how to detect potential new issues, before the activation of new services. In this new network scenario, traffic characterisation and monitoring as well as configuration and on-air reliability of network equipment, is of paramount relevance in order to prevent possible pitfalls during the deployment of new services and ensure the best possible user experience. Based on this observation, this research project offers a comprehensive study that goes from experimental analysis to operational optimization. The starting point of our work has been monitoring the traffic of an already deployed eNodeB with three cells, operative in the 1800 MHz band. Through subsequent measurement campaigns, it was possible to follow the evolution of the 4G network by the beginning of its deployment in 2012, until its full maturity in 2015. The data collected during the first year, showed a poor use of the LTE network, mainly due to the limited penetration of new 4G smartphone. In 2015, however, we appreciate a clear and decisive increase in the number of terminals using LTE, with aggregate statistics (e.g. marketshare for smartphone operating systems, or the percentage of video traffic) that reflect the national trend. This important outcome testifies the maturity of LTE technology, and allows us to consider our monitored eNodeB as a valuable vantage point for traffic analysis. Hand in hand with the evolution of the infrastructure, even mobile phones have had a surprising evolution over the past two decades, from simple devices with only voice services, towards smartphones offering novel services such as mobile Internet, geolocation and maps, multimedia services, and many more. Monitoring the real traffic has allowed us to study the users behavior and identify the services most used. To this aim, various software libraries for traffic analysis have been developed. In particular, we developed a C/C++ library that analyses Control Plane and User Plane traffic, which provides corse and fined-grained statistics at flow-level. Another framework/tool has been exclusively dedicated to the topic of traffic classification. Among the plethora of existing tool for traffic classification we provide our own solution, developed from scratch. The project, which is available on github, is named MOSEC, an acronym for Modular SErvice Classifier. The modularity is given by the possibility to implement multiple plug-ins, each one will process the packet according to its logic, and may or may not return a packet/flow classification. A final decision strategy allows to classify the various streams, based on the classifications of each plug-in. Despite previous approaches, the ability of keeping together multiple classifiers allows to mitigate the deficiency of each classifiers (e.g. DPI\nomenclature{DPI}{Deep Packet Inspection} does not work when packets are encrypted or DNS\nomenclature{DNS}{Domain Name Server} queries don't have to be sent if name resolution is cached in device memory) and exploit their full-capabilities when it is feasible. We validated the goodness of MOSEC using a labelled trace synthetically created by colleagues from UPC BarcelonaTech. The results show excellent TCP-HTTP/HTTPS traffic classification capabilities, higher, on average, than those of other classification tools (NDPI, PACE, Layer-7). On the other hand, there are some shortcomings with regard to the classification of UDP traffic. The characteristics of User Plane traffic have a direct impact on the energy consumed by the handset devices, and an indirect impact on the Control Plane traffic that is generated. Therefore, the acquaintances of the statistical properties of the various flows, allows us to deal with the problem of cross-layer optimization, that is reducing the power consumption of the terminals by varying some control plane parameters configurable on the eNodeB. It is well known that the battery life of the new smartphones is one of the major limitations in the use of the same. In particular, the birth of new services and applications capable of working in the background without direct user interaction, introduced new issues related to the battery lifetime and the signaling traffic necessary to acquire/release the radio resources. Based on these observations, we conducted a thorough study on the DRX mechanism (Discontinuous Reception), exploited by LTE to save smartphones energy when no packet is sent or received. The DRX configuration set and the RRC Inactivity Timer greatly affect the energy consumed by the various devices. Depending on which radio resources are allocated or not, the user equipment is in the states of RRC Connected and Idle, respectively. To evaluate the energy consumption of smartphones, an algorithm simulates the transition between all the possible states in which an UE can be and maps a power value to each of these states. The transition from one state to another is governed by different timeouts that are reset every time a packet is sent or received. Using the traces of real traffic, we associate a state machine to each for assessing the energy consumption on the basis of the sent and received packets. We repeated these simulations using different values of the inactivity timer, that appear to be more suitable than the one currently configured on the monitored eNodeB, looking for a good trade-off between energy savings and increased signaling traffic. The results highlighted that the Inactivity Timer set originally sull'eNodeB was too high and determined an excessive energy consumption on the terminals. Reducing the value up to 10 seconds permits to achieve energy savings of up to 50\% (depending on the underling traffic profile) without up considerably the control traffic. The results of the study mentioned above, however, do not consider neither the stress level which the eNodeB is subject to, given the raise of signaling traffic that could occur, nor the increase of collision probability during the RACH procedure, needed to re-establish the radio bearer (or RRC connection ) between the terminal and eNodeB . Evaluate the performance of hardware and software systems for the fourth-generation mobile network, as well as identify any possible weakness in the architecture, it is a complex job. A possible case study, is precisely to assess the robustness of the base station when it receives many requests for RRC connections, as effect of a decrease of the inactivity timer. In this regard, within the Testing LAB of Telecom Italia, we used IxLoad, a product developed by Ixia, as a load generator to test the robustness of one eNodeB. The tests consisted in producing a different load of RRC request on the radio interface, similar to those that would be produced by decreasing the inactivity timer to certain values. The statistical properties for the signalling traffic are derived from the analysis of real traffic traces. The main outcomes have shown that, even in the face of an high load of RRC requests only a small part (less than 1\% in the most unfavorable of the cases) of the procedure fails. Therefore, even lowering the inactivity timer at values lower than 10 seconds is not an issue for the Base Station. Finally, remains to be evaluated how such surge of RRC request impacts on users performance. If one of the users under coverage in the RRC Idle is paged for an incoming packet or need to send an uplink packet a state transition from RRC Idle to RRC Connected is needed. At this point, the UE initiates the random access procedure by sending the random access channel preamble (RACH Preamble). When two or more users attempt, simultaneously, to access the RACH channel, using the same preamble, the eNodeB may not be able to decipher the preamble. If the two signals interfere constructively, both users receive the same resources for transmitting the RRC Request message and, at this point, the eNodeB can detect the collision and will not send any acknowledgment, forcing both users to restart the procedure from the beginning. We have proposed an analytical model to calculate the probability of a collision based on the number of users and the offered traffic load, when the interarrival time between requests is modeled with hyper-exponential times. In addition, we investigated some performance for Machine-to-Machine (M2M) and Human-to-Human (H2H) type communications, including the probability of correct transmission considering either the backoff time either the maximum number of allowed retransmissions, and the average time required to established a radio bearer with the access network. The results, considered as a whole, have made possible to express the guidelines to properly distribute the number of preambles in H2H and M2M communications

Technique d'accès pour la communication machine-à-machine dans LTE/LTE-A

Machine type communications is seen as a form of data communication, among devices and/or from devices to a set of servers, that do not necessarily require human interaction. However, it is challenging to accommodate MTC in LTE as a result of its specific characteristics and requirements. The aim of this thesis is to propose mechanisms and optimize the access layer techniques for MTC in LTE. For uplink access, we propose two methods to improve the performance of random access in terms of latency: a packet aggregation method and a Transmission Time Interval bundling scheme. To further reduce the uplink latency and enable massive number of connected device, we propose a new contention based access method (CBA) to bypass both the redundant signaling in the random access procedure and also the latency of regular scheduling. For downlink reception, we propose two methods to analyze the performance of discontinuous reception DRX mode for MTC applications: the first with the Poisson distribution and the second with the Pareto distribution for sporadic traffic. With the proposed models, the power saving factor and wake up latency can be accurately estimated for a given choice of DRX parameters, thus allowing to select the ones presenting the optimal tradeoff.Les communications de type machine-à-machine M2M sont considérées comme des formes de communication de données qui ne requièrent pas nécessairement d'interaction humaine. Cependant, ce type de communication n'est pas efficace dans les réseaux cellulaires, en raison de leurs caractéristiques spécifiques, telles que. L'objectif de cette thèse est de proposer des mécanismes et d'optimiser les techniques de la couche d'accès radio LTE pour les communications M2M. Pour l'accès au canal de liaison montante, nous proposons deux méthodes afin d'améliorer la performance d'accès aléatoire en terme de latence et de consommation énergétique: une méthode d'agrégation de paquets et une autre de transmission multiple pendant l'intervalle de temps de transmission. Afin de réduire encore plus le temps de latence de liaison montante et permettre une connexion d'un grand nombre de machines au réseau, nous proposons une nouvelle méthode d'accès basée sur la contention CBA pour éviter d'une part la signalisation redondante pour accéder au canal et d'autre part la latence de l'ordonnanceur. Pour la réception de liaison descendante, nous proposons deux méthodes pour analyser les performances du mécanisme de réception discontinu DRX pour les applications M2M: la première se base sur une distribution de Poisson, la suivante sur une distribution Pareto pour le trafic sporadique. Avec les modèles proposés, le facteur d'économie d’énergie et la latence pour transiter du mode sommeil au mode actif peuvent être estimés avec précision pour un choix donné de paramètres DRX, permettant ainsi de sélectionner ceux permettant d'atteindre le compromis optimal