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

Design and analysis of LTE and wi-fi schemes for communications of massive machine devices

Existing communication technologies are designed with speciÿc use cases in mind, however, ex-tending these use cases usually throw up interesting challenges. For example, extending the use of existing cellular networks to emerging applications such as Internet of Things (IoT) devices throws up the challenge of handling massive number of devices. In this thesis, we are motivated to investigate existing schemes used in LTE and Wi-Fi for supporting massive machine devices and improve on observed performance gaps by designing new ones that outperform the former. This thesis investigates the existing random access protocol in LTE and proposes three schemes to combat massive device access challenge. The ÿrst is a root index reuse and allocation scheme which uses link budget calculations in extracting a safe distance for preamble reuse under vari-able cell size and also proposes an index allocation algorithm. Secondly, a dynamic subframe optimization scheme that combats the challenge from an optimisation solution perspective. Thirdly, the use of small cells for random access. Simulation and numerical analysis shows performance improvements against existing schemes in terms of throughput, access delay and probability of collision. In some cases, over 20% increase in performance was observed. The proposed schemes provide quicker and more guaranteed opportunities for machine devices to communicate. Also, in Wi-Fi networks, adaptation of the transmission rates to the dynamic channel condi-tions is a major challenge. Two algorithms were proposed to combat this. The ÿrst makes use of contextual information to determine the network state and respond appropriately whilst the second samples candidate transmission modes and uses the e˛ective throughput to make a deci-sion. The proposed algorithms were compared to several existing rate adaptation algorithms by simulations and under various system and channel conÿgurations. They show signiÿcant per-formance improvements, in terms of throughput, thus, conÿrming their suitability for dynamic channel conditions

Congestion Control for Massive Machine-Type Communications: Distributed and Learning-Based Approaches

The Internet of things (IoT) is going to shape the future of wireless communications by allowing seamless connections among wide range of everyday objects. Machine-to-machine (M2M) communication is known to be the enabling technology for the development of IoT. With M2M, the devices are allowed to interact and exchange data without or with little human intervention. Recently, M2M communication, also referred to as machine-type communication (MTC), has received increased attention due to its potential to support diverse applications including eHealth, industrial automation, intelligent transportation systems, and smart grids. M2M communication is known to have specific features and requirements that differ from that of the traditional human-to-human (H2H) communication. As specified by the Third Generation Partnership Project (3GPP), MTC devices are inexpensive, low power, and mostly low mobility devices. Furthermore, MTC devices are usually characterized by infrequent, small amount of data, and mainly uplink traffic. Most importantly, the number of MTC devices is expected to highly surpass that of H2H devices. Smart cities are an example of such a mass-scale deployment. These features impose various challenges related to efficient energy management, enhanced coverage and diverse quality of service (QoS) provisioning, among others. The diverse applications of M2M are going to lead to exponential growth in M2M traffic. Associating with M2M deployment, a massive number of devices are expected to access the wireless network concurrently. Hence, a network congestion is likely to occur. Cellular networks have been recognized as excellent candidates for M2M support. Indeed, cellular networks are mature, well-established networks with ubiquitous coverage and reliability which allows cost-effective deployment of M2M communications. However, cellular networks were originally designed for human-centric services with high-cost devices and ever-increasing rate requirements. Additionally, the conventional random access (RA) mechanism used in Long Term Evolution-Advanced (LTE-A) networks lacks the capability of handling such an enormous number of access attempts expected from massive MTC. Particularly, this RA technique acts as a performance bottleneck due to the frequent collisions that lead to excessive delay and resource wastage. Also, the lengthy handshaking process of the conventional RA technique results in highly expensive signaling, specifically for M2M devices with small payloads. Therefore, designing an efficient medium access schemes is critical for the survival of M2M networks. In this thesis, we study the uplink access of M2M devices with a focus on overload control and congestion handling. In this regard, we mainly provide two different access techniques keeping in mind the distinct features and requirements of MTC including massive connectivity, latency reduction, and energy management. In fact, full information gathering is known to be impractical for such massive networks of tremendous number of devices. Hence, we assure to preserve the low complexity, and limited information exchange among different network entities by introducing distributed techniques. Furthermore, machine learning is also employed to enhance the performance with no or limited information exchange at the decision maker. The proposed techniques are assessed via extensive simulations as well as rigorous analytical frameworks. First, we propose an efficient distributed overload control algorithm for M2M with massive access, referred to as M2M-OSA. The proposed algorithm can efficiently allocate the available network resources to massive number of devices within relatively small, and bounded contention time and with reduced overhead. By resolving collisions, the proposed algorithm is capable of achieving full resources utilization along with reduced average access delay and energy saving. For Beta-distributed traffic, we provide analytical evaluation for the performance of the proposed algorithm in terms of the access delay, total service time, energy consumption, and blocking probability. This performance assessment accounted for various scenarios including slightly, and seriously congested cases, in addition to finite and infinite retransmission limits for the devices. Moreover, we provide a discussion of the non-ideal situations that could be encountered in real-life deployment of the proposed algorithm supported by possible solutions. For further energy saving, we introduced a modified version of M2M-OSA with traffic regulation mechanism. In the second part of the thesis, we adopt a promising alternative for the conventional random access mechanism, namely fast uplink grant. Fast uplink grant was first proposed by the 3GPP for latency reduction where it allows the base station (BS) to directly schedule the MTC devices (MTDs) without receiving any scheduling requests. In our work, to handle the major challenges associated to fast uplink grant namely, active set prediction and optimal scheduling, both non-orthogonal multiple access (NOMA) and learning techniques are utilized. Particularly, we propose a two-stage NOMA-based fast uplink grant scheme that first employs multi-armed bandit (MAB) learning to schedule the fast grant devices with no prior information about their QoS requirements or channel conditions at the BS. Afterwards, NOMA facilitates the grant sharing where pairing is done in a distributed manner to reduce signaling overhead. In the proposed scheme, NOMA plays a major role in decoupling the two major challenges of fast grant schemes by permitting pairing with only active MTDs. Consequently, the wastage of the resources due to traffic prediction errors can be significantly reduced. We devise an abstraction model for the source traffic predictor needed for fast grant such that the prediction error can be evaluated. Accordingly, the performance of the proposed scheme is analyzed in terms of average resource wastage, and outage probability. The simulation results show the effectiveness of the proposed method in saving the scarce resources while verifying the analysis accuracy. In addition, the ability of the proposed scheme to pick quality MTDs with strict latency is depicted

Protocol-Level Simulations of Massive Medium Access for Machine-Type Communications

In recent years, Machine-Type Communications (MTC) has become one of the most attractive technologies in the area of wireless networking. Different sources are predicting a large grow of smart grid machine-to-machine deployments in several decades, which also means that the total number of wireless devices will increase dramatically. In connection to this problem, the choice of the standard, which will satisfy all the MTC requirements without harming current wireless deployments has become very relevant. Because of these reasons, many companies are proposing to modify one (or several) of the current wireless standard in a way that it will be possible to use for MTC purposes. This will be perfect from point of view of interference problems, because they will be already included in a standard itself. Third Generation Partnership Project (3GPP) Long Term Evolution-Advanced (LTE- A) is one of the most rapidly developing wireless technologies, that seems to be an ideal candidate for future MTC implementation. However, while the capacity of typical LTE-A network should be enough to satisfy traffic demands of large number of MTC devices, the signaling is not ready to face new requirements. In this Thesis, we are considering and partly solving problems, that could occur in LTE-A signaling channels under MTC conditions. Particularly, these are data access mechanisms, which could be realized via Physical Uplink Control Channel (PUCCH) and Physical Random Access Channel (PRACH). Speaking about assessment methods, the research made in this work is based on 2 approaches: simulation and analysis. Both of them are also in details described in the pages of this Thesis. As a conclusion it could be said that PUCCH channel is not suitable for the MTC data access, while PRACH is having problems only in heavily loaded (overloaded) cases and should be slightly modified to face them

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