Search Results (64)

Deep learning techniques have been widely applied in wireless communication systems to enhance resilience and reduce computational complexity. This paper investigates both traditional and deep learning-based approaches for real-time relay selection in a cooperative communication system with multiple energy-harvesting relays and signal space diversity. The assumed relay decoding scheme is decode-and-forward (DF), with selection criteria based on successful decoding from the source, sufficient energy availability, and the best channel to the destination. The system performance is evaluated in terms of outage probability. Monte Carlo simulations are used to determine the exact outage probability of the system and to generate datasets for training machine learning models. The traditional machine learning models implemented include Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machines (SVMs). The deep learning-based method used is the deep neural network (DNN). Two datasets—one with six features and another with nine features—were used for training and testing. The 6-feature datasets are comparatively less random and complex than the 9-feature datasets. The results indicate that among traditional models KNN achieves the highest accuracy and is thus used as a benchmark to compare against DNN performance. For the 9-feature datasets, both KNN and DNN struggle to accurately approximate the exact outage probability, suggesting that the 9-feature datasets are too complex and noisy for effective modeling. However, on the 6-feature datasets, KNN achieves 77% accuracy, while DNN achieves a significantly higher accuracy of 99%. Due to its high accuracy, the DNN model closely approximates the exact outage probability while offering greater computational efficiency compared to the KNN model. These results underscore the potential of deep learning in optimizing real-time relay selection for energy-harvesting cooperative communication systems. Full article

Unmanned aerial vehicle (UAV)-assisted satellite downlink transmission is a promising solution for improving coverage and throughput under challenging propagation conditions. However, the achievable performance gains are fundamentally constrained by the coupling between access transmission and the satellite–UAV backhaul, especially when decode-and-forward (DF) relaying and hybrid multiple access are employed. In this paper, we investigate the problem of end-to-end downlink sum-rate maximization in a UAV-assisted satellite network with hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission. We propose a performance-driven end-to-end optimization framework, in which UAV placement is optimized as an outer-layer control variable through an iterative procedure. For each candidate UAV position, a greedy transmission mode selection mechanism and a KKT-based satellite-to-UAV backhaul bandwidth allocation scheme are jointly executed in the inner layer to evaluate the resulting end-to-end downlink performance, whose feedback is then used to update the UAV position until convergence. Simulation results show that the proposed framework consistently outperforms benchmark schemes without requiring additional spectrum or transmit power. Under low satellite elevation angles, the proposed design improves system sum rate and spectral efficiency by approximately 25–35% compared with satellite-only NOMA transmission. In addition, the average user rate is increased by up to 37% under moderate network sizes, while maintaining stable relative gains as the number of users increases, confirming the effectiveness and scalability of the proposed approach. Full article

In this paper, we propose a two-way hybrid satellite–terrestrial relay scheme employing Fountain codes (FCs). In the proposed model, a satellite and a ground user exchange data through a group of terrestrial relay stations, in the presence of an eavesdropper. In the first phase, the satellite and the ground user simultaneously transmit their encoded packets to the relay stations. The relay stations then apply a successive interference cancelation (SIC) technique to decode the received packets. To reduce the quality of the eavesdropping links, a cooperative jammer is employed to transmit jamming signals toward the eavesdropper during the first phase. Next, one of the relay stations which can successfully decode the encoded packets from both the satellite and the ground user is selected for data forwarding, by using a partial relay selection method. Then, this selected relay performs an XOR operation on the two encoded packets, and then broadcasts the XOR-ed packet to both the satellite and the user in the second phase. We derive exact closed-form expressions of outage probability (OP), system outage probability (SOP), intercept probability (IP), and system intercept probability (SIP), and realize simulations to validate these expressions. This paper also studies the trade-off between OP (SOP) and IP (SIP), as well as the impact of various system parameters on the performance of the proposed scheme. Full article

This paper investigates a simultaneous wireless information and power transfer (SWIPT) decode-and-forward (DF) relay network, where a source node transmits data to a destination node through the assistance of multi-hop passive relays. We employ the time-switching (TS) protocol, enabling the relays to harvest energy from the received previous hop signal to support data forwarding. We first prove that the system throughput monotonically increases with the transmit power of the source node. Next, by employing logarithmic transformations, we convert the non-convex problem of obtaining optimal TS ratios at each relay to maximize the system throughput into a convex optimization problem. Comprehensively taking into account the convergence rate, computational complexity per iteration, and robustness, we selected the log barrier method—a type of interior point method—to address this convex optimization problem, along with providing a detailed implementation procedure. The simulation results validate the optimality of the proposed method and demonstrate its applicability to practical communication systems. For instance, the proposed scheme achieves 1437.3 bps throughput at 40 dBm maximum source power in a 2-relay network—278.6% higher than that of the scheme with TS ratio fixed at 0.75 (379.68 bps). On the other hand, it converges within a 1.36 ms computation time for 5 relays, 6 orders of magnitude faster than exhaustive search (1730 s). Full article

This study investigates a dynamic switching method for signal relay protocols in Cooperative Pattern Division Multiple Access (Co-PDMA) networks. The proposed approach aims to fully utilize the advantages of signal relays in fading-prone environment while simultaneously reducing the network outage probability and improving the throughput and energy efficiency. To demonstrate the necessity of implementing the dynamic switching method for signal relay protocols, Co-PDMA networks with Decode-and-Forward (DF) or Amplify-and-Forward (AF) protocols are explored over Nakagami-m fading. Based on the analysis of these two scenarios, the overall outage probability, throughput, and energy efficiency of the Co-PDMA network with a dynamic DF/AF protocol are determined. The results demonstrate that the proposed method selects the optimal signal relay protocol for forwarding user data in a simple and efficient manner across varying transmit signal-to-noise ratios, quality of service, and signal relay locations. Compared with fixed signal relay protocols, the proposed method is more conducive to achieving green communication in Co-PDMA networks, as it enhances communication reliability and the total volume of data transmitted. Full article

This work examines the performance of a multi-antenna cooperative non-orthogonal multiple access (NOMA) network that employs short-packet communications and operates under the effect of hardware impairments. Specifically, a multi-antenna source transmits superposition-coded NOMA signals to a near user and a far user. Acting as a decode-and-forward (DF) relay, the near user adopts successive interference cancellation (SIC) to decode and subsequently forward the message intended for the far user. In addition, the transmission strategy at the source is the maximum ratio transmission (MRT) and the reception strategy at the far user is selection combining (SC). For Nakagami-m fading channels, closed-form expressions for the average block error rate (BLER) and effective throughput are derived. Then, the effective throughput is maximized through the optimization of the blocklength, accounting for constraints on transmission latency and reliability. The results obtained from simulations confirm the analytical findings and demonstrate that the proposed scheme, with a two-antenna source configuration, achieves a superior effective throughput, reaching up to 240% at a transmit signal-to-noise ratio (SNR) of 33 dB, compared to the existing NOMA scheme in the literature. Full article

With increasing labor costs and rapidly dynamic changes in the market demand, as well as realizing the refined management of production, more and more attention is being given to considering workers, not just machines, in the process of flexible job shop scheduling. Hence, a new dual-resource flexible job shop scheduling problem (DRFJSP) is put forward in this paper, considering workers with flexible working time arrangements and machines with versatile functions in scheduling production, as well as a multi-objective mathematical model for formalizing the DRFJSP and tackling the complexity of scheduling in human-centric manufacturing environments. In addition, a two-stage approach based on a forensic-based investigation (TSFBI) is proposed to solve the problem. In the first stage, an improved multi-objective FBI algorithm is used to obtain the Pareto front solutions of this model, in which a hybrid real and integer encoding–decoding method is used for exploring the solution space and a fast non-dominated sorting method for improving efficiency. In the second stage, a multi-criteria decision analysis method based on an analytic hierarchy process (AHP) is used to select the optimal solution from the Pareto front solutions. Finally, experiments validated the TSFBI algorithm, showing its potential for smart manufacturing. Full article

The salient object detection (SOD) of forward-looking sonar images plays a crucial role in underwater detection and rescue tasks. However, the existing SOD algorithms find it difficult to effectively extract salient features and spatial structure information from images with scarce semantic information, uneven intensity distribution, and high noise. Convolutional neural networks (CNNs) have strong local feature extraction capabilities, but they are easily constrained by the receptive field and lack the ability to model long-range dependencies. Transformers, with their powerful self-attention mechanism, are capable of modeling the global features of a target, but they tend to lose a significant amount of local detail. Mamba effectively models long-range dependencies in long sequence inputs through a selection mechanism, offering a novel approach to capturing long-range correlations between pixels. However, since the saliency of image pixels does not exhibit sequential dependencies, this somewhat limits Mamba’s ability to fully capture global contextual information during the forward pass. Inspired by multimodal feature fusion learning, we propose a hybrid CNN–Transformer–Mamba architecture, termed FLSSNet. FLSSNet is built upon a CNN and Transformer backbone network, integrating four core submodules to address various technical challenges: (1) The asymmetric dual encoder–decoder (ADED) is capable of simultaneously extracting features from different modalities and systematically modeling both local contextual information and global spatial structure. (2) The Transformer feature converter (TFC) module optimizes the multimodal feature fusion process through feature transformation and channel compression. (3) The long-range correlation attention (LRCA) module enhances CNN’s ability to model long-range dependencies through the collaborative use of convolutional kernels, selective sequential scanning, and attention mechanisms, while effectively suppressing noise interference. (4) The recursive contour refinement (RCR) model refines edge contour information through a layer-by-layer recursive mechanism, achieving greater precision in boundary details. The experimental results show that FLSSNet exhibits outstanding competitiveness among 25 state-of-the-art SOD methods, achieving MAE and ${\mathit{E}}_{\mathit{\xi }}$ values of 0.04 and 0.973, respectively. Full article

In this paper, we consider a three-node free space optical (FSO) decode-and-forward (DF) cooperative network. The relay is not connected to a permanent power supply and relies solely on the optical energy harvested (EH) from the source node. This energy is accumulated in an energy buffer in order to enable the relay–destination communications. Moreover, buffer-aided (BA) relaying is considered where the relay is equipped with a data buffer for storing the incoming packets. For such networks, we propose a relaying protocol that delineates the roles of the source and the EH BA relay in each time slot. We develop a Markov chain framework for capturing the dynamics of the data and energy buffers. We derive the transition probabilities between the states of the Markov chain after discretizing the continuous-value energy buffer allowing for the evaluation of the analytical performance of the considered system. A numerical analysis is also presented over a turbulence-induced gamma–gamma fading channel highlighting the impacts of the data rate threshold levels, relay position, relay transmit power and propagation conditions on the achievable performance levels. Results validate the accuracy of the theoretical analysis and demonstrate significant reductions in the network outage, especially when the relay’s transmit level is appropriately selected. Full article

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER. Full article

In this paper, we study covert communications in a hybrid decode-and-forward (DF)/ amplify-and-forward (AF) relay system. The considered relay in normal operation forwards messages from a source node to a destination node in either DF or AF mode on request. Meanwhile, the source and destination nodes also attempt to secretly exchange covert messages such as confidential or sensitive information and avoid detection by the covert message detector embedded on the relay. We first establish an optimal DF/AF mode selection criterion to maximize the covert rate based on the analyses of delay-aware achievable covert rates of individual DF and AF modes. To further reduce the time complexity, we propose a low-complexity selection criterion as well for practical use. The numerical results demonstrate the covert rate gain as high as 50% and running time gain as high as 20% for particular system parameters, which verify the effectiveness of the proposed criteria. Full article

In free-space optical communication (FSOC) systems, atmospheric turbulence can bring about power fluctuations in receiver ends, restricting channel capacity. Relay techniques can divide a long FSOC link into several short links to mitigate the fading events caused by atmospheric turbulence. This paper proposes a Reinforcement Learning-based Relay Selection (RLRS) method based on Deep Q-Network (DQN) in a FSOC system with multiple transceivers, whose aim is to enhance the average channel capacity of the system. Malaga turbulence is studied in this paper. The presence of handover loss is also considered. The relay nodes serve in decode-and-forward (DF). Simulation results demonstrate that the RLRS algorithm outperforms the conventional greedy algorithm, which implies that the RLRS algorithm may be utilized in practical FSOC systems. Full article

To combat the high outage probability, high complexity calculation, and low resource utilization rate of collaborative communications, an optimal decode-and-forward (DF) multirelay selection and power allocation is proposed in cooperative wireless sensor networks. It is suitable for cooperative communications equipped with a large number of relay nodes. It uses the Lagrange multiplier method to perform the power pre-distribution of the source nodes and all relay nodes before the relay selection. In addition, it also optimally exploits the power of the distributed source and relay nodes according to statistics channel status information (CSI). By optimizing the selection of the multirelay nodes and the allocation of the power with the water-filling algorithm, the proposed scheme totally exploits the whole power to greatly reduce the resource waste. Especially, it chooses an optimal relay node in cooperative communications without a large number of instantaneous channel information, and it only need to arrange the relay nodes according to the increase in the equivalent channel gain order for the optimal relay node collection at the proper signal-to-noise ratios (SNRs). Simulation results show that the outage probability of the scheme outperforms those of the existing single selective decode-and-forward (SDF) counterparts by about 2.1 dB at an outage probability of ${10}^{-2}$. Full article

This paper introduces an innovative hybrid wireless power transfer (H-WPT) scheme tailored for IIoT networks employing multiple relay nodes. The scheme allows relay nodes to dynamically select their power source for energy harvesting based on real-time channel conditions. Our analysis evaluates outage probability within decode-and-forward (DF) relaying and adaptive power splitting (APS) frameworks, while also considering the energy used by relay nodes for ACK signaling. A notable feature of the H-WPT scheme is its decentralized operation, enabling relay nodes to independently choose the optimal relay and power source using instantaneous channel gain. This approach conserves significant energy otherwise wasted in centralized control methods, where extensive information exchange is required. This conservation is particularly beneficial for energy-constrained sensor networks, significantly extending their operational lifetime. Numerical results demonstrate that the proposed hybrid approach significantly outperforms the traditional distance-based power source selection approach, without additional energy consumption or increased system complexity. The scheme’s efficient power management capabilities underscore its potential for practical applications in IIoT environments, where resource optimization is crucial. Full article

Orthogonal time frequency space (OTFS) modulation has recently found its place in the literature as a much more effective waveform in time-varying channels. It is anticipated that OTFS will be widely used in the communications of smart vehicles, especially those considered within the scope of Internet of Things (IoT). There are efforts to obtain customized traditional point-to-point single-input single-output (SISO)-OTFS studies in the literature, but their BER performance seems a bit low. It is possible to use cooperative communications in order improve BER performance, but it is noticeable that there are very few OTFS studies in the area of cooperative communications. In this study, to the best of the authors’ knowledge, it is addressed for the first time in the literature that better performance is achieved for the OTFS waveform transmission in a selective decode-and-forward (SDF) cooperative communication scenario. In this context, by establishing a cooperative communication model consisting of a base station/source, a traffic sign/relay and a smart vehicle/destination moving at a constant speed, an end-to-end BER expression is derived. SNR-BER analysis is performed with this SDF-OTFS scheme and it is shown that a superior BER performance is achieved compared to the traditional point-to-point single-input single-output (SISO)-OTFS structure. Full article