Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads (CHs) in the rotation basis, the LEACH protocol will increase the energy consumption of the network. To improve the energy efficiency of the WSN, we propose a novel modified routing protocol in this paper. The newly proposed improved energy-efficient LEACH (IEE-LEACH) protocol considers the residual node energy and the average energy of the networks. To achieve satisfactory performance in terms of reducing the sensor energy consumption, the proposed IEE-LEACH accounts for the numbers of the optimal CHs and prohibits the nodes that are closer to the base station (BS) to join in the cluster formation. Furthermore, the proposed IEE-LEACH uses a new threshold for electing CHs among the sensor nodes, and employs single hop, multi-hop, and hybrid communications to further improve the energy efficiency of the networks. The simulation results demonstrate that, compared with some existing routing protocols, the proposed protocol substantially reduces the energy consumption of WSNs.

In a downlink non-orthogonal multiple access (NOMA) system, the reliable transmission of cell-edge users cannot be guaranteed due to severe channel fading. On the other hand, the presence of eavesdroppers can severely threaten the secure transmission due to the open nature of wireless channel. Thus, a two-user NOMA system assisted by a multi-antenna decode-and-forward relay is considered in this paper, and a two-stage jamming scheme, full-duplex-jamming (FDJam), is proposed to ensure the secure transmission of NOMA users. In the FDJam scheme, using full-duplex, the relay transmits the jamming signal to the eavesdropper while receiving confidential messages in the first stage, and the base station generates the jamming signal in the second stage. Furthermore, we eliminate the self-interference and the jamming signal at the relay and the legitimate node, respectively, through relay beamforming. To measure the secrecy performance, analytical expressions for secrecy outage probability (SOP) are derived for both the cellcenter and cell-edge users, and the asymptotic SOP analysis at high transmit power is presented as well. Moreover, two benchmark schemes, half-duplex-jamming and full-duplex-nojamming, are also considered. Simulation results are presented to show the accuracy of the analytical expressions and the effectiveness of the proposed scheme.

A major defect of orthogonal frequency division multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR). In this letter, a novel scheme based on deep leaning, called tone reservation network (TRNet), is proposed for OFDM systems to improve the performance of the tone reservation (TR) technique. More specifically, TRNet reserves a part of tones to generate a peak-canceling signal. The feedforward neural network is used to adaptively generate a peak-canceling signal according to the characteristics of the input signal. Computer simulation results show that the proposed scheme provides a better PAPR reduction performance with fewer reserved tones, which is also beneficial to improve the bandwidth efficiency.

The eigenvalues of the sample covariance matrix can capture signal correlations and noise characteristics well, which are widely used for spectrum sensing in cognitive radio networks. Some totally blind spectrum sensing algorithms were proposed, such as maximum eigenvalue-to-arithmetic mean (ME–AM), maximum eigenvalue-to-geometric mean (ME–GM), and arithmetic-to-geometric mean of eigenvalues (AGM) methods. This article makes full use of the advantages of these algorithms and proposes a universal spectrum sensing algorithm based on maximum eigenvalue, arithmetic mean, and geometric mean of eigenvalues. The proposed algorithm takes the weighted geometric mean of the test statistics of the ME–AM and AGM algorithms as new test statistic and includes the ME–AM, ME–GM, and AGM algorithms as special cases. Following the random matrix theory framework, in a similar vein to the ME–AM and ME–GM algorithms, we derive the analytical expressions of false alarm probability, threshold, and detection probability of the proposed method using the Tracy–Widom distribution of maximum eigenvalue. In addition, we discuss the problem of designing the optimal weighting factor through theoretical analysis and simulation experiments. Finally, simulation results verify that the universal algorithm can effectively improve the detection performance.

Integrating cognitive radio into Internet of Things (IoT) is conducive to reducing spectrum scarcity for large-scale IoT deployment, where a core technology is the design of spectrum access algorithms for effective assignment of spectrum holes. However, due to the partially observable channels and increased number of users in the cognitive radio Internet of Things (CRIoT) network, the secondary users have difficulty avoiding interferences and accessing the spectrum quickly. This study presents a distributed dynamic spectrum access (DSA) algorithm that employs a priority experience replay deep echo state Q-network (PER-DESQN) for CRIoT networks with multiple users and channels. To accelerate the Q-network convergence, we use an echo state network based on the underlying temporal correlation to estimate Q-values. Then, to resolve the Q-value overestimation and improve prediction accuracy, the estimated Q-value and decision action process are trained using a double deep Q-network (DDQN). Moreover, a priority experience replay mechanism that uses the Sum-Tree combined with importance sampling weights is proposed to optimize the DDQN to address the instability of the Q-value resulting from random sampling. As the simulation results demonstrate, the proposed algorithm can make fast and accurate DSA decisions and boost the network channel capacity significantly.

Proactive eavesdropping was used recently to efficiently intercept a suspicious wireless communication link, by jamming the suspicious destination node. However, While jamming helps weaken the suspicious link, it does not improve the eavesdropping channel from the source node to the legitimate eavesdropper. This work proposes to use intelligent reflecting surfaces (IRS) to enhance proactive eavesdropping, by jointly affecting both the suspicious and eavesdropping channels, and is the first to employ the cooperative passive beamforming in the double-IRS aided monitoring system. By considering the cooperative two single-reflection links and especially the double-reflection link, we jointly design the passive phase shift matrices of double IRSs to optimize the eavesdropping capability. Due to the coupled variables caused by double-IRS cooperation and the intractable unit modulus constraints of IRS elements, the non-convex optimization problem is difficult to solve. We first divide the problem into two subproblems, and apply the idea of minimization majorization to make each subproblem convex. We obtain the solution of reflective coefficient matrix in closed form, and then propose an alternating algorithm with low complexity to obtain the Karush-Kuhn-Tucker (KKT) solution. Finally, simulation results are presented to demonstrate the effectiveness of cooperative passive beamforming design over the traditional single-IRS and jamming assisted eavesdropping schemes.