Non-orthogonal multiple access (NOMA)-enabled incremental relaying can significantly enhance spectrum efficiency and boost the performance of cognitive radio (CR) networks. However, imperfect channel state information (CSI) due to estimation errors, and hardware impairments (HIs) caused by phase noise, quantization errors, and nonlinearities, introduce new complexities in CR-NOMA networks. In this work, we investigate the transmission strategy in HIs and imperfect CSI built-in cooperative NOMA networks. First, a system model with HIs and imperfect CSI is designed, in which multiple CR relays coexist to achieve the incremental relaying. Second, a multi-relay incremental relaying strategy is proposed, where all relays (successfully decoding the information to the destination) can cooperate to forward the information, thereby improving the network performance. More especially, the exact and asymptotic expressions of outage probability as well as the throughput are explicitly derived. Third, to get a comparable strategy, a best-relay incremental relaying strategy is further constructed, selecting the one with the highest signal-to-noise ratio (SNR) among all successful relays to assist the destination. Finally, the correctness of theoretical analyses for both strategies are verified by simulations, and the impact of different network parameters on performance are validated through simulations.

Unmanned aerial vehicle (UAV) communications are susceptible to malicious compound interference signals due to the complexity and variability of the electromagnetic environment and the openness of the air-to-ground wireless channels, leading to degradation of communication quality. Therefore, effective detection and accurate recognition of compound interference are the key to ensuring secure UAV communication in complex environments. However, existing deep learning-based interference recognition algorithms suffer from fewer recognizable compound interference types, a large number of model parameters, and lower interference recognition accuracy under low interference-to-noise power ratio (INR) conditions. This paper proposes a malicious compound interference recognition method for UAV communication based on multi-modal multi-label learning and designs a lightweight multi-modal interference recognition network. By introducing a multi-label learning mechanism and making full use of the complementary information between different modalities of the signal, the method can achieve more flexible, accurate and stable recognition of compound interference signals under low INR. We construct both simulation and real measured datasets containing 31 classes of compound interference signals, and conduct simulation experiments with sufficient samples, insufficient samples, and different training strategies. The results demonstrate that the proposed method enhances the recognition accuracy of UAV communication compound interference under low INR and across different training datasets, all while maintaining a small number of model parameters.

Achieving high-precision interference recognition for autonomous aerial vehicle (AAV) swarm communications in complex electromagnetic environments is of great significance for developing efficient anti-interference schemes and improving the security of AAV swarm communication. Although deep learning-based interference recognition methods for AAV swarm communication can achieve good recognition performance, they usually rely on a large number of high-quality labeled samples and only consider a single representation of the interference signal as input. This leads to low accuracy and poor robustness of interference recognition in scenarios with changing electromagnetic environments or insufficient samples. To address these issues, this article proposes a data-knowledge-driven method for AAV swarm communication interference recognition. A dual-input interference recognition network (DIRNet) with a few model parameters is designed, incorporating deep features extracted based on the data-driven approach and manual features designed based on expert knowledge. Simulation experiments are conducted under sufficient-sample, cross-environment, and insufficient-sample scenarios. The results demonstrate that the proposed AAV swarm communication interference recognition method not only improves the recognition accuracy of interference signals under these conditions. Moreover, it also shows good robustness in complex dynamic environments.

Automatic modulation recognition plays a crucial role in the domain of electromagnetic situational awareness. Early recognition methods predominantly relied on expert experience and prior knowledge, demanding a high level of professional background and experience from practitioners, and usually underperformed in complex signal environments. In recent years, the continual development of deep learning (DL) technologies has introduced solution ideas to address the challenge of modulation recognition in complex electromagnetic environments. However, DL methods heavily depend on large volumes of high-quality labeled data and face challenges in real electromagnetic environments with limited samples. To fully leverage the respective strengths of expert knowledge in the radio domain and data-driven approaches, this article proposes a hybrid-driven neural network (HDNet) framework for radio signal recognition. HDNet integrates deep features extracted through data-driven methods with manual features extracted based on expert knowledge, aiming to enhance recognition performance in few-shot scenarios. Experimental results on both simulated and real measured datasets demonstrate that HDNet achieves high-recognition accuracy and robustness.

Received signal strength (RSS)-based localization techniques have gained much attention in location-based services (LBSs). However, the coexistence of unknown path loss exponent (PLE), uncertain sensor positions, and impulsive noise poses serious challenges to localization accuracy. To address the problem, we first model the impulsive noise as a Mixture of Gaussian (MoG) distribution with unknown parameters. Thus, the noise model and the channel model can be refined using the observed data under the variational Bayesian inference (VBI) framework, which is defined as the model refinement learning. We then propose a corresponding online target localization procedure with the refined noise distribution, PLE and sensor positions. The Bayesian Cramer-Rao bound (BCRB) is finally derived in terms of all unknown parameters. Simulation results together with real experiment demonstrate that the proposed VBI algorithm can effectively learn the true noise distribution, and the developed localization method exhibits robust localization performance in various scenarios.

Automatic modulation classification (AMC) plays an increasingly crucial role in intelligent spectrum management and dynamic spectrum access, which can effectively support the reallocation of low-utilization spectrum resources in wireless communication systems. While deep learning approaches have been widely employed in AMC, most deep learning-based AMC methods focus on signal classification as a singular task. Therefore, this paper proposes a multi-task learning-based method for radio signal recognition aimed at enhancing AMC performance. This method utilizes the designed multi-task collaborative learning network (MCLNet) model to achieve complementary gains across different tasks. By sharing parameters, it enhances the learning capability of crucial signal features, thereby acquiring more discriminative signal features and improving classification accuracy. Experimental results demonstrate that the proposed method outperforms other benchmark models on two benchmark datasets and exhibits greater performance gains in few-shot scenarios.

Integrated sensing and communication (ISAC), by combining the communication and sensing functions in shared frequency bands, emerges as a promising technology for future wireless networks. However, the performance of ISAC may be affected by the channel fading and massive connections. In this paper, we propose a non-orthogonal multiple access (NOMA) aided ISAC scheme via intelligent reflective surface (IRS) to set up virtual line-of-sight links for multi-user communication and target sensing. Specifically, our goal is to maximize the sum rate through the joint optimization of active transmit beamforming at the base station and passive phases for reflecting at the IRS, while satisfying the sensing requirement for the target user. Since the original optimization problem is non-convex, we first decompose it into two subproblems, which are converted into convex ones by applying the successive convex approximation. Then, an alternating optimization algorithm is proposed to derive a solution to the original problem. Simulation results validate that the proposed scheme can effectively enhance the multi-user NOMA communication performance while guaranteeing the sensing quality by introducing IRS.

Orthogonal frequency division multiplexing (OFDM) is regarded as one of the key technologies in wireless communications, particularly in the integration of space and ground networks. Nevertheless, the performance of OFDM communication systems will degrade significantly in complex scenarios, which brings severe challenge to reliable information recovery at the receiver. To address this issue, we propose an intelligent receiving method for OFDM communication based on dual-channel convolutional neural network (DCNet) from the perspective of combining knowledge and data-driven, which introduces the domain knowledge of channel estimation to assist the stability of OFDM signal recovery. The experimental results under various simulation conditions demonstrate that the proposed method can effectively enhance the performance of information recovery in OFDM communication systems.

Abstract Herein , one partial repetition extension method is proposed to construct the rate‐compatible spatially coupled low‐density parity‐check (RC‐SCLDPC) codes. For each position of the base SCLDPC code, a certain proportion of the variable nodes are first selected randomly and then repeated a certain number of times. By adjusting the selection proportions and the repetition times, a family of RC‐SCLDPC codes with arbitrary rates from 0 to the rate of the base SCLDPC code can be obtained and the rate‐compatibility is realized. Threshold analysis results show that all member codes in the proposed RC‐SCLDPC code family display capacity‐approaching thresholds over the binary erasure channel and additive white Gaussian noise channel. Finite length simulation results also confirm their excellent thresholds. Moreover, the decoding complexity can be significantly decreased using this partial repetition extension method.

Industrial Internet of Things (IIoT) usually utilizes 2.4-GHz unlicensed frequency band, which is also heavily used by many other communication systems, such as ZigBee, WiFi, Bluetooth, etc. Therefore, the lack of spectrum resources has become a key technical bottleneck to restrict the development of IIoT. Integrating cognitive radio (CR) into IIoT, Cognitive IIoT (CIIoT) can cope with the spectrum resource shortage by accessing the frequency bands licensed to primary user (PU). However, spectrum sensing and access control must be performed to avoid bringing severe interference to the PU. In this article, an integrated cooperative spectrum sensing (CSS) and access control model is proposed to improve the transmission performance of the CIIoT while guaranteeing the CSS’s detection probability and controlling the interference to the PU. This model is optimized to maximize the total throughput of IIoT in each frame by jointly optimizing sensing time, the number of sensing nodes and the transmit power for each node under the constraints of the minimum detection probability, the total power control, the interference control, and the minimum rate for each node. The optimization problem is solved by the joint optimization of spectrum sensing and access control. A simultaneous CSS and access control model is also proposed to increase the communication time by using one time slot to perform CSS and access control simultaneously. The simulation results show that there exist optimal sensing and control parameters to maximize the total throughput of CIIoT.

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.

Artificial intelligence technology can be used to solve some problems that are difficult to be solved by traditional wireless communication. OFDM system has been widely used at present, but the resource consumption of pilot module is very high. Therefore, this paper reviews the OFDM communication system from the point of view of signal processing. At the receiving end of OFDM system, the deep learning method is adopted to jointly optimize each communication module of the receiving end. A intelligent receiver of OFDM communication system based on the Densenet neural network structure is designed and by optimizing the structure of Densenet neural network, the intelligent receiver is realized. This method can recover information at the receiving end and avoid complex pilot operation and signal error accumulation. The simulation results show that the intelligent receiver improves the receiver performance of OFDM communication system.