Hybrid interweave-underlay spectrum access in cognitive radio (CR) networks can explore spectrum opportunities when primary users (PUs) are either active or inactive, which significantly improves spectrum utilization. The practical wireless systems such as Long Term Evolution-Advanced (LTE-A), usually operate at multiple transmission power levels, leading to a multiple primary transmission power (MPTP) scenario. In such a case, the two fundamental issues in hybrid interweave-underlay spectrum access, are to detect the “on/off” status of PUs and to recognize the operating power level of PUs, which are challenging due to non-Gaussian transmitted signals. In this paper, we exploit high order cumulants (HOCs) to efficiently perform spectrum sensing and power recognition. Specifically, for a given order and time lag, we first propose a single HOC based spectrum sensing and power recognition (SCSR) scheme with low computational complexity, by leveraging minimum Bayes risk criterion. Moreover, we propose a hybrid multiple HOCs based spectrum sensing and power recognition (HCSR) scheme with multiple orders and time lags, to further improve the detection performance. Both the proposed schemes can eliminate the adverse impact of the noise power uncertainty. Finally, simulation results are provided to evaluate the proposed schemes.

Surveillance performance is studied for a wireless eavesdropping system, where a full-duplex legitimate monitor eavesdrops a suspicious user's link with artificial noise (AN) assistance. Different from the existing works, the suspicious receiver is assumed to be capable of detecting the presence of AN. Once such receiver detects the AN, the suspicious user will stop transmission, which can therefore degrade the surveillance performance. Hence, to improve the surveillance performance, AN should be transmitted covertly with a low detection probability. Under these assumptions, an optimization problem is formulated to maximize the surveillance performance under a covert constraint. Then, based on the detection ability at the suspicious receiver, a novel scheme is proposed to solve the optimization problem using an iterative search. Moreover, we investigate the impact of both the suspicious-transmitter-to-suspicious-receiver and the monitor-to-suspicious-receiver links uncertainties on the covert surveillance performance. Simulations are performed to verify the analyses. We show that the uncertainty in the suspicious user's link can enhance the surveillance performance, while the uncertainty in the monitor-to-suspicious-receiver link can degrade the surveillance performance.

In this paper, we propose a covert wireless uplink transmission strategy in space-air-ground integrated vehicular networks, where the source vehicle transmits its own message over the channel that being used by the host communication system, to avoid being detected by the warden. It is obvious that the data transmission efficiency of the covert communication system is limited due to the co-channel interference. To improve the data transmission efficiency, we consider that the covert communication system adopts improper Gaussian signaling (IGS). We formulate a joint transmit power and IGS factor optimization problem to minimize the outage probability of the covert communication system. The minimum error detection probability of the warden is first analyzed with noise uncertainty, which is used to measure the system covertness. Under the constraints of the quality of service (QoS) of host communication system and the covertness requirement, the optimal transmit power is first derived with proper Gaussian signaling (PGS) scheme. Then, with the approximate outage probability derived under IGS scheme, the optimization problem is solved by jointly designing the transmit power and IGS factor. Finally, we provide extensive numerical results to validate the proposed covert transmission strategy, and demonstrate that the IGS scheme is beneficial in improving the data transmission efficiency in terms of outage probability compared to PGS scheme.

Massive malicious jamming devices and advanced jamming techniques have been emerged along with the development of wireless communication networks. In order to effectively eliminate the harmful interference, a new scheme known as the cognitive frequency hopping (CHF) is proposed recently, which can evaluate the occupancy of frequency hopping slots and adjust the parameters dynamically according to the spectrum sensing results. Although the existing literature only shows that CFH systems can achieve reliable data transmission, the factors affecting the reliability are rarely analyzed. Therefore, we analyze the reliability performance of the CHF systems in this article, and define a new metric named anti-interference tolerance to measure the reliability performance of the CHF systems. Moreover, we derive the analytic expression of anti-interference tolerance by analyzing the effect of false alarm probability, missed detection probability, and communication link convergence delay. Simulation results validate the effectiveness of our analyses for measuring the reliability capacity of the CHF systems. To satisfy the demands of different communication scenarios, the CFH systems can adjust relevant parameters in the light of our theoretical derivation.

Covert wireless communications aim to hide the existence of transmission behavior from watchful adversaries to enhance security. In this paper, we propose a spectrum mask based covert communication strategy in Internet of Things (IoT) networks, where the overt channels are leveraged to enhance the covertness. Specifically, the legitimate IoT transmitter superimposes its own message on the overt channel to avoid being detected by the warden. We assume that proper Gaussian signaling (PGS) is adopted at the overt channel, and improper Gaussian signaling (IGS) is adopted at the legitimate transmitter to improve the covert transmission performance. To maximize the covert rate of the legitimate IoT system, a joint transmit power and IGS factor optimization problem is formulated under the constraints of covertness requirement. The metric of minimum error detection probability, that represents the worst-case for the legitimate transmitter, is utilized to measure the covertness. By exploiting the piece-wise monotonic properties of the objective function and the constraints, we derive the optimal transmit power and IGS factor pairs in both the IGS and PGS schemes. Finally, extensive numerical results are presented to demonstrate that the IGS scheme can improve the covert rate compared to the PGS scheme under a given covertness constraint.

With unprecedented progress on Internet of Things (IoT), spectrum scarcity becomes even severe with the explosive growth of wireless smart devices. To deal with spectrum scarcity issues, cognitive radio (CR)-enabled IoT has been emerged as a promising solution, which allows IoT devices reusing the underutilized spectrum bands. In this article, we investigate spectrum sensing in CR-IoT, in which full-duplex CR-IoT node can perform spectrum sensing and data transmission concurrently for reducing sensing delay. Two sensing methods are proposed based on multiple high-order cumulants for excavating rich information of the non-Gaussian transmitted signals. Specifically, for the scenarios with a single sensing antenna, we first propose a multiple high-order cumulants-based sensing method (MCS) derived from the likelihood ratio test, which is assumed to be near optimum. The test statistics are derived, respectively, in two cases, i.e., the case only performing sensing and the case performing sensing and transmission simultaneously. Interestingly, the derived two test statistics have same expression, while the corresponding sensing thresholds are different from each other. For the scenarios with multiple sensing antennas, we propose a multiantenna-assisted multiple high-order cumulants-based sensing method (MMCS), which can provide a tradeoff between the computational complexity and sensing performance. We conduct the hypothesis test with Hotelling's T ² -statistic and derive the corresponding sensing threshold. Theoretical performance evaluated by detection probability and computational complexity of the proposed methods are analyzed. Additionally, extensive simulations are provided, which show both the proposed methods can counter the adverse effects of noise uncertainty, and MCS has superiority over MMCS in terms of sensing accuracy.