In this paper, we focus on the physical layer security for a $K$ -user multiple-input-single-output (MISO) wiretap channel in the presence of a malicious eavesdropper, where we propose several interference exploitation (IE) precoding schemes for different types of the eavesdropper. Specifically, in the case where a common eavesdropper decodes the signal directly and Eve's full channel state information (CSI) is available at the transmitter, we show that the required transmit power can be further reduced by re-designing the `destructive region' of the constellations for symbol-level precoding and re-formulating the power minimization problem. We further study the SINR balancing problems with the derived `complete destructive region' with full, statistical and no Eve's CSI, respectively, and show that the SINR balancing problem becomes non-convex with statistical or no Eve's CSI. On the other hand, in the presence of a smart eavesdropper using maximal likelihood (ML) detection, the security cannot be guaranteed with all the existing approaches. To this end, we further propose a random jamming scheme (RJS) and a random precoding scheme (RPS), respectively. To solve the introduced convex/non-convex problems in an efficient manner, we propose an iterative algorithm for the convex ones based on the Karush-Kuhn-Tucker (KKT) conditions, and deal with the non-convex ones by resorting to Taylor expansions. Simulation results show that all proposed schemes outperform the existing works in secrecy performance, and that the proposed algorithm improves the computation efficiency significantly.

Since single satellite capacity is limited for hotspot areas, we propose a dual satellite (DS) cooperative transmission network to increase the number of served cells and improve the quality of service (QoS) in this article. We study the global beam hopping and power allocation in the proposed network, where the conflict between cell priority and latency is balanced. Due to the beam power impacting both cochannel interference and capacity, the two multiobjective problems are coupled. The plural method is introduced to simplify the coupled problems, which obtains a better QoS than the fixed-weight method. As the dimensionality of the beam hopping pattern increases with the number of cells, global beam hopping design is a dimensional explosion problem. The demand-weighted power (DWP)-adaptive particle marching (APM) algorithm includes roulette, stochastic exploration, and nonlinear weights strategies. It is proposed to handle high-dimensional problems and converge fast to better optimal solutions. The simulation results show that the DWP-APM algorithm performs better with a few particles than the four baseline algorithms. Compared with baseline algorithms, the DWP-APM algorithm reduces the computation time by 57% and improves the QoS level by 13.6% at least.

The number of base stations (BSs) in remote areas is poor, and seamless coverage cannot be achieved. This paper investigates a cognitive satellite-terrestrial network, including the low earth orbit-beam hopping (BH) satellite and terrestrial systems. Besides, the weighted capacity-request ratio is regarded as the quality of service (QoS) in the satellite system, and the weight indicates channel quality and service priority. Similarly, the capacity-request ratio is introduced as the QoS in the terrestrial system. By establishing a relationship between beams and terminals, the solution space of the BH pattern and beam power is shrunk, and the power allocation competition algorithm is proposed. The proposed algorithm promptly obtains a better solution than the particle swarm optimization and genetic algorithms. As the secondary system, the terrestrial system should obtain an accurate sense of the satellite system. We design a dynamic-ratio threshold spectrum detection mechanism, which causes less miss detection than energy detection under co-channel interference (CCI). Moreover, based on CCI suppression and quick selection of greedy thought, an adaptive resource adjustment algorithm is determined for the BS pattern and transmit power. Finally, the efficiency of the proposed algorithms and spectrum detection mechanism is demonstrated in simulations.

This paper studies the secure degree of freedom (SDOF) of the multiway relay wiretap system K-user MIMO Y wiretap channel, where each legitimate user equipped with M antennas intends to convey independent signals via an intermediate relay with N antennas. There exists an eavesdropper which is equipped with Ne antennas close to the legitimate users. In order to eliminate the multiuser interference and keep the system security, interference alignment is mainly utilized in the proposed broadcast wiretap channel (BWC) and multi-access BWC (MBWC), and cooperative jamming is adopted as a supplementary approach in the MBWC model. The general feasibility conditions of the interference alignment are deduced as M ≥ K - 1, 2M > N and N ≥ ((K(K - 1))/2). In the BWC model, we have deduced the secure degrees of freedom (SDOF) as K min{M, N} - min{N _e , K(K - 1)/2}, which is always a positive value. While in the MBWC model, the SDOF is given by K min{M, N}. Finally, since the relay transmits the synthesized signals of the legal signal and the jamming signal in the MBWC model, we propose a power allocation scheme to maximize the secrecy rate. Simulation results demonstrate that our proposed power allocation scheme can improve secrecy rate under various antenna configurations.

Pareto local search (PLS) is a natural extension of local search for multiobjective combinatorial optimization problems (MCOPs). In our previous work, we improved the anytime performance of PLS using parallel computing techniques and proposed a parallel PLS based on decomposition (PPLS/D). In PPLS/D, the solution space is searched by multiple independent parallel processes simultaneously. This article further improves PPLS/D by introducing two new cooperative process techniques, namely, a cooperative search mechanism and a cooperative subregion-adjusting strategy. In the cooperative search mechanism, the parallel processes share high-quality solutions with each other during the search according to a distributed topology. In the proposed subregion-adjusting strategy, a master process collects useful information from all processes during the search to approximate the Pareto front (PF) and redivide the subregions evenly. In the experimental studies, three well-known NP-hard MCOPs with up to six objectives were selected as test problems. The experimental results on the Tianhe-2 supercomputer verified the effectiveness of the proposed techniques.

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%.