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

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

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Massive M2M Access with Reliability Guarantees in LTE Systems

Machine-to-Machine (M2M) communications are one of the major drivers of the cellular network evolution towards 5G systems. One of the key challenges is on how to provide reliability guarantees to each accessing device in a situation in which there is a massive number of almost-simultaneous arrivals from a large set of M2M devices. The existing solutions take a reactive approach in dealing with massive arrivals, such as non-selective barring when a massive arrival event occurs, which implies that the devices cannot get individual reliability guarantees. In this paper we propose a proactive approach, based on a standard operation of the cellular access. The access procedure is divided into two phases, an estimation phase and a serving phase. In the estimation phase the number of arrivals is estimated and this information is used to tune the amount of resources allocated in the serving phase. Our results show that the proactive approach is instrumental in delivering high access reliability to the M2M devices.Comment: Accepted for presentation in ICC 201

Code-Expanded Random Access for Machine-Type Communications

The random access methods used for support of machine-type communications (MTC) in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. Motivated by the random access method employed in LTE, we propose a novel approach that is able to sustain a wide random access load range, while preserving the physical layer unchanged and incurring minor changes in the medium access control layer. The proposed scheme increases the amount of available contention resources, without resorting to the increase of system resources, such as contention sub-frames and preambles. This increase is accomplished by expanding the contention space to the code domain, through the creation of random access codewords. Specifically, in the proposed scheme, users perform random access by transmitting one or none of the available LTE orthogonal preambles in multiple random access sub-frames, thus creating access codewords that are used for contention. In this way, for the same number of random access sub-frames and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the support of an increased number of MTC users. We present the framework and analysis of the proposed code-expanded random access method and show that our approach supports load regions that are beyond the reach of current systems.Comment: 6 Pages, 7 figures, This paper has been submitted to GC'12 Workshop: Second International Workshop on Machine-to-Machine Communications 'Key' to the Future Internet of Thing