Energy detection is of crucial importance in cognitive radio networks. However, its performance is poor when the channel fading is severe, which causes interference to the primary users. In order to tackle this issue, an intelligent reflecting surface (IRS)-enhanced energy detection for spectrum sensing is proposed. Both the cases with and without the direct link between the primary user and the secondary user are considered. By using the Gamma distribution approximation and central limit theorem, the closed-form expressions for the average probability of detection are derived. In order to further improve the detection performance, IRS-enhanced energy detection for cooperative spectrum sensing and multiple IRSs-enhanced square-law selection diversity reception are also proposed. Expressions for the average probability of detection for these two schemes are provided by using the ${K}$ -rank fushion criterion and square-law selection, respectively. Simulation results verify our theoretical analysis and demonstrate the superiority of our proposed IRS-enhanced energy detection compared with the benchmark schemes in terms of the spectrum sensing performance.

This paper considers a new wireless information surveillance scenario, where a legitimate monitor tries to eavesdrop on the suspicious messages sent by an unmanned aerial vehicle (UAV)-aided suspicious transmitter. By using the characteristic of the suspicious system, we propose an effective proactive jamming scheme to maximize the achievable eavesdropping rate. A successive convex approximation based iterative algorithm is first developed to solve the original non-convex optimization problem. Based on this result, the eavesdropping rate maximization problem is then solved by applying the one-dimensional search method. Moreover, we also study the special case of two suspicious receivers for gaining more useful insight on designing the optimal jamming transmission strategy. Numerical results show that the proposed proactive jamming approach can significantly enhance the achievable eavesdropping rate compared to the reference scheme.

Cognitive radio (CR) is one of the most disruptive techniques for enabling the next generation wireless communication networks due to its potential in improving the spectral efficiency. In this paper, intelligent reflecting surface (IRS) is exploited to enhance both the accuracy of spectrum sensing and the secondary transmission in a CR network (CRN) employing the opportunistic spectrum access. A novel detection threshold based on the probability of false alarm is derived for improving the spectrum sensing performance. The average achievable rate of the secondary network is maximized under both the two-stage and one-stage IRS phase shifts case. To tackle the challenging non-convex optimization problem under the two-stage case, a computationally efficient block coordinate descent (BCD)-based algorithm is proposed coputilizing the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR). Moreover, a BCD method and a tractable approximation of the probability of detection are exploited to tackle the problem under one-stage IRS phase shifts case. Simulation results demonstrate that our proposed designs are superior to the benchmark schemes in terms of the achievable rate and the sensing performance, and IRS can greatly improve the spectral efficiency of the CRN.

This paper studies an uplink nonorthogonal multiple access (NOMA)-enabled mobile-edge computing network. Specifically, we focus on the practical design of secure offloading without knowing the eavesdropper’s channel state information. The aim is to maximize the minimum antieavesdropping ability (AEA) for uplink NOMA users subject to the worst-case secrecy rate requirements and limited transmission power budgets. The formulated problem is nonconvex and difficult to be solved directly. In order to tackle this issue, we propose an efficient iterative algorithm by jointly designing the secrecy rate, local computing bits, and power allocation. The local computing bits and the power allocation are derived in closed form. Numerical results are provided to demonstrate the efficiency of our proposed scheme in terms of AEA.