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Massive multiple-input multiple-output antenna systems, millimeter wave communications, and ultra-dense networks have been widely perceived as the three key enablers that facilitate the development and deployment of 5G systems. This article discusses the intelligent agent that combines sensing, learning, and optimizing to facilitate these enablers. We present a flexible, rapidly deployable, and cross-layer artificial intelligence (AI)-based framework to enable the imminent and future demands on 5G and beyond. We present example AI-enabled 5G use cases that accommodate important 5G-specific capabilities and discuss the value of AI for enabling network evolution.

The commercial success of the Long Term Evolution (LTE) and the resulting growth in mobile data demand have urged cellular network operators to strive for new innovations. LTE in unlicensed spectrum has been proposed to allow cellular network operators to offload some of their data traffic by accessing the unlicensed 5 GHz frequency band. Currently, there are three proposed variants for LTE operation in the unlicensed band, namely LTE-U, Licensed Spectrum Access (LAA), and MulteFire. This paper provides a comparative analysis of these variants and explains the current regulations of the 5 GHz band in different parts of the world. We present the technical details of the three proposed versions and analyze them in terms of their operational features and coexistence capabilities to provide an R&D perspective for their deployment and coexistence with legacy systems.

The 3 rd Generation Partnership Project released the cellular vehicular-to-everything (C-V2X) specifications as part of the LTE framework in Release 14. C-V2X is the alternative to dedicated short range communications and both are specifically designed for V2X control signaling. C-V2X extends the device-to-device specifications by adding two more modes of operation targeting the vehicular environment in coverage and out of coverage of LTE base stations. Vehicle-to-vehicle communications (V2V) is established with Mode 4, where the devices schedule their transmissions in a distributed way employing sensing-based semi-persistent scheduling (SPS). Research is needed to assess the performance of SPS, especially in congested radio environments. This paper presents the first open-source C-V2X simulator that enables such research. The simulator is implemented in ns-3. We analyze the effect of the Mode 4 resource pool configuration and some of the key SPS parameters on the scheduling performance and find that the resource reservation interval significantly influences packet data rate performance, whereas resource reselection probability has little effect in dense vehicular highway scenarios. Our results show that proper configuration of scheduling parameters can significantly improve performance. We conclude that research on congestion control mechanisms is needed to further enhance the SPS performance for many practical use cases.

In December 2017, the Third Generation Partnership Project (3GPP) released the first set of specifications for 5G New Radio (NR), which is currently the most widely accepted 5G cellular standard. 5G NR is expected to replace LTE and previous generations of cellular technology over the next several years, providing higher throughput, lower latency, and a host of new features. Similar to LTE, the 5G NR physical layer consists of several physical channels and signals, most of which are vital to the operation of the network. Unfortunately, like for any wireless technology, disruption through radio jamming is possible. This paper investigates the extent to which 5G NR is vulnerable to jamming and spoofing, by analyzing the physical downlink and uplink control channels and signals. We identify the weakest links in the 5G NR frame, and propose mitigation strategies that should be taken into account during implementation of 5G NR chipsets and base stations.

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Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.

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