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5G has received significant interest from commercial as well as defense industries. However, resiliency in 5G remains a major concern for its use in military and defense applications. In this paper, we explore physical layer resiliency enhancements for 5G and use narrow-band Internet of Things (NB-IoT) as a study case. Two physical layer modifications, frequency hopping, and direct sequence spreading, are analyzed from the standpoint of implementation and performance. Simulation results show that these techniques are effective to harden the resiliency of the physical layer to interference and jamming. A discussion of protocol considerations for 5G and beyond is provided based on the results. 

Multipath transmission is considered one of the promising solutions to improve wireless resource utilization where there are many kinds of heterogeneous networks around. Most scheduling algorithms rely on real-time network metrics, including delay, packet loss, and arrival rates, and achieve satisfying results in simulation or wired environments. However, the implicit premise of a scheduling algorithm may conflict with the characteristics of real heterogeneous wireless networks, which has been ignored before. This paper analyzes the real network metrics of three Chinese heterogeneous wireless networks under different transmission rates. To make the results more convincing, we conduct experiments in various scenarios, including different locations, different times of the day, different numbers of users, and different motion speeds. Further, we verify the suitability of a typical delay-aware multipath scheduling algorithm, Lowest Round Trip Time, in heterogeneous networks based on the actual data measured above. Finally, we conclude the characteristics of heterogeneous wireless networks, which need to be considered in a well-designed multipath scheduling algorithm. 

Cognitive radio networks (CRNs), which offer novel network architecture for utilising spectrums, have attracted significant attention in recent years. CRN users use spectrums opportunistically, which means they sense a channel, and if it is free, they start transmitting in that channel. In cooperative spectrum sensing, a secondary user (SU) decides about the presence of the primary user (PU) based on information from other SUs. Malicious SUs (MSUs) send false sensing information to other SUs so that they make wrong decisions about the spectrum status. As a result, an SU may transmit during the presence of the PU or may keep starving for the spectrum. In this paper, we propose a reputation-based mechanism which can minimise the effects of MSUs on decision making in cooperative spectrum sensing. Some of the SUs are selected as distributed fusion centres (DFCs), that are responsible for making decisions about the presence of PU and informing the reporting SUs. A DFC uses weighted majority voting among the reporting SUs, where weights are normalised reputation. The DFC updates reputations of SUs based on confidence of an election. If the majority wins by a significant margin, the confidence of the election is high. In this case, SUs that belong to the majority gain high reputations. We conduct extensive simulations to validate our proposed model. 

Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user’s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model. 

Spectrum monitoring is a powerful tool in dynamic spectrum access to help secondary users access the unused spectrum white space. The common approach for spectrum monitoring is to build infrastructures (e.g. spectrum observatories), which cost much money and manpower but have relatively low coverage. To aid in this, we propose a crowdsourcing based spectrum monitoring system for a large geographical area that leverages the power of masses of portable mobile devices. The system can accurately predict future spectrum utilization and intelligently schedule the spectrum monitoring tasks among mobile secondary users accordingly, so that the energy of mobile devices can be saved and more spectrum activities can be monitored. We also demonstrate our system's ability to capture not only the existing spectrum access patterns but also the unknown patterns where no historical spectrum information exist. The experiment shows that our spectrum monitoring system can obtain a high spectrum monitoring coverage and low energy consumption. 

In dynamic spectrum access (DSA), Environmental Sensing Capability (ESC) systems are implemented to detect the incumbent users' (IU) activities for protecting them from secondary users' (SU) interference as well as maximizing secondary spectrum usage. However, IU location information is often highly sensitive and hence it is preferable to hide its true location under the detection of ESCs. In this paper, we design novel schemes to preserve both static and moving IU's location information by adjusting IU's radiation pattern and transmit power. We first formulate IU privacy protection problem for static IU. Due to the intractable nature of this problem, we propose a heuristic approach based on sampling. We also formulate the privacy protection problem for moving IUs, in which two cases are analyzed: (1) protect IU's moving traces; (2) protect its real-time current location information. Our analysis provides insightful advice for IU to preserve its location privacy against ESCs. Simulation results show that our approach provides great protection for IU's location privacy.