Search for: All records

In dynamic spectrum access (DSA), secondary users (SU) should only be allowed to access a licensed band belonging to incumbent users (IU) when the quality-of-service (QoS) requirements of both IUs and SUs can be satisfied at the same time. However, IU’s location and its received interference strength are considered sensitive in many DSA systems which should not be revealed, making it very challenging to optimize the network utility subjected to satisfying the operation and security requirements of SUs and IUs. In this paper, we develop a secure and distributed SU transmit power control algorithm to solve this challenge. Our algorithm achieves optimal SU power control to maximize the sum of SU rates. The SINR-guaranteed coexistence between SUs and IUs are enabled to maintain effective communication, while no information is directly required from IUs. Local measurements of IU signals provided by Environmental sensing capability (ESC) also undergo a security masking process to ensure that IU location cannot be derived from its outputs. Convergence and stability properties of our algorithm and its privacy-protection strength are both theoretically analyzed and experimentally evaluated through simulations 

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 

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.