Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications.

Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications

Internet of Things (IoT) is one of the evolutionary directions of the Internet. This paper focuses on the low earth orbit (LEO) satellite constellation-based IoT services for their irreplaceable functions. In many cases, IoT devices are distributed in remote areas (e.g., desert, ocean, and forest) in some special applications, they are placed in some extreme topography, where are unable to have direct terrestrial network accesses and can only be covered by satellite. Comparing with the traditional geostationary earth orbit (GEO) systems, LEO satellite constellation has the advantages of low propagation delay, small propagation loss and global coverage. Furthermore, revision of existing IoT protocol are necessary to enhance the compatibility of the LEO satellite constellation-based IoT with terrestrial IoT systems. In this paper, we provide an overview of the architecture of the LEO satellite constellation-based IoT including the following topics: LEO satellite constellation structure, efficient spectrum allocation, heterogeneous networks compatibility, and access and routing protocols.

LEO Satellite Constellation for Internet of Things

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 (mMTCs), and ultra-reliable and low latency communications (URLLCs), 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 toward 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 toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.

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Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

Real-time big data processing for anomaly detection: A Survey

The advancement of technologies over years has poised Internet of Things (IoT) to scoop out untapped information and communication technology opportunities. It is anticipated that IoT will handle the gigantic network of billions of devices to deliver plenty of smart services to the users. Undoubtedly, this will make our life more resourceful but at the cost of high energy consumption and carbon footprint. Consequently, there is a high demand for green communication to reduce energy consumption, which requires optimal resource availability and controlled power levels. In contrast to this, IoT devices are constrained in terms of resources—memory, power, and computation. Low power wide area (LPWA) technology is a response to the need for efficient utilization of power resource, as it evinces characteristics such as the capability to proffer low power connectivity to a huge number of devices spread over wide geographical areas at low cost. Various LPWA technologies, such as LoRa and SigFox, exist in the market, offering a proficient solution to the users. However, in order to abstain the need of new infrastructure (like base station) that is required for proprietary technologies, a new cellular-based licensed technology, narrowband IoT (NBIoT), is introduced by 3GPP in Rel-13. This technology presents a good candidature to handle LPWA market because of its characteristics like enhanced indoor coverage, low power consumption, latency insensitivity, and massive connection support towards NBIoT. This survey presents a profound view of IoT and NBIoT, subsuming their technical features, resource allocation, and energy-efficiency techniques and applications. The challenges that hinder the NBIoT path to success are also identified and discussed. In this paper, two novel energy-efficient techniques “zonal thermal pattern analysis” and energy-efficient adaptive health monitoring system have been proposed towards green IoT.

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

In this article we present the major challenges of future machine-to-machine (M2M) cellular networks such as spectrum scarcity, and support for a large number of low-power, lowcost devices. As an integral part of the future Internet-of-Things (IoT), the true vision of M2M communications cannot be reached with conventional solutions that are typically cost inefficient. The cognitive radio concept has emerged to address spectrum under-utilization and scarcity. The heterogeneous network model is another alternative to relax the number of covered users. To this extent, we present a complete fundamental understanding and the engineering details of cognitive radios, the heterogeneous network model, and power and cost challenges in the context of future M2M cellular networks.

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Next generation M2M cellular networks: challenges and practical considerations

In this article, we present the major challenges of future machine-to-machine (M2M) cellular networks such as spectrum scarcity problem, support for low-power, low-cost, and numerous number of devices. As being an integral part of the future Internet-of-Things (IoT), the true vision of M2M communications cannot be reached with conventional solutions that are typically cost inefficient. Cognitive radio concept has emerged to significantly tackle the spectrum under-utilization or scarcity problem. Heterogeneous network model is another alternative to relax the number of covered users. To this extent, we present a complete fundamental understanding and engineering knowledge of cognitive radios, heterogeneous network model, and power and cost challenges in the context of future M2M cellular networks.

Next Generation M2M Cellular Networks: Challenges and Practical Considerations

Similar to all mobile communication networks, synchronization in the time-frequency domain is a fundamental step that allows a fifth-generation (5G) new radio (NR) user equipment (UE) to properly receive and transmit its data. Due to the wide range of frequencies that are defined for the 5G NR systems, the corresponding synchronization procedure becomes critical and presents many challenges, especially for the applications that would need accurate oscillators to reduce the large values of the frequency offset. In this paper, we present and detail the 5G NR physical layer. Then, we describe the required synchronization procedure for 5G NR. And finally, we present the main challenges and issues within the 5G NR synchronization.

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Synchronization procedure in 5G NR systems

Human-generated information has been the main interest of the wireless communication technologies designs for decades. However, we are currently witnessing the emerge of an entirely different paradigm of communication introduced by machines, and hence, the name machine type communication (MTC). Such paradigm arises as a result of the new applications included in the Internet-of-Things (IoT) framework. Among the enabling technologies of the IoT, cellular-based communication is the most promising and more efficient. This is justified by the currently well-developed and mature radio access networks, along with the large capacities and flexibility of the offered data rates to support a large variety of applications. On the other hand, several radio-access-network groups put efforts to optimize the 3GPP LTE standard to accommodate for the new challenges by introducing new communication categories paving the way to support the machine-to-machine communication within the IoT framework. In this paper, we provide a step-by-step tutorial discussing the development of MTC design across different releases of LTE and the newly introduced user equipment categories, namely, MTC category (CAT-M) and narrowband IoT category (CAT-N). We start by briefly discussing the different physical channels of the legacy LTE. Then we provide a comprehensive and up-to-date background for the most recent standard activities to specify CAT-M and CAT-N technologies. We also emphasize on some of necessary concepts used in the new specifications, such as the narrowband concept used in CAT-M and the frequency hopping. Finally, we identify and discuss some of the open research challenges related to the implementation of the new technologies in real life scenarios.

Abdelmohsen Ali

Papers

Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications

Next generation M2M cellular networks: challenges and practical considerations

Next Generation M2M Cellular Networks: Challenges and Practical Considerations

Synchronization procedure in 5G NR systems

Cellular LTE-A Technologies for the Future Internet-of-Things: Physical Layer Features and Challenges