In cognitive heterogeneous networks, multiple secondary transmitters (STs) co-exist with primary users (PUs) on the same frequency band channel through spectrum sensing. Due to inaccurate sensing of whether the channel is occupied, STs can cause interference to PUs, thereby affecting the transmission performance of PUs. This paper proposes a constrained deep reinforcement learning-based joint adaptive modulation coding and power selection (CDRL-JAMCPS) algorithm. The proposed CDRL-JAMCPS learns the interference patterns of STs to PUs through interaction with the environment and selects the modulation coding scheme and transmit power for future frames of PUs based on the learned patterns, aiming to maximize the transmission rate while reducing energy consumption. Furthermore, addressing the issue where existing optimization algorithms solely consider network transmission rates while neglecting data transmission quality, this paper proposes a reward function in Lagrangian form based on frame error rate (FER) constraints. By optimizing this reward function in its dual domain, the problem of poor data transmission quality is resolved. The simulation results demonstrate that the proposed algorithm achieves better transmission performance compared to other reinforcement learning algorithms in environments where signal interference is difficult to perceive. Meanwhile, compared to algorithms that do not consider transmission quality, our algorithm exhibits significant advantages in meeting FER requirements and improving data transmission quality.

This paper proposes a radio map reconstruction method based on the Neural Architecture Search Enhanced Deep Regularization Model. Traditional radio map reconstruction algorithms face many challenges, such as extremely sparse measurement data and complex wireless signal environments. To solve the problem of unstable output, an external regularizer is introduced to provide additional regularization input in the prediction process to assist the neural network in capturing the explicit prior information from the sparse measurements and performing restoration. Moreover, to ensure the efficiency of capturing implicit prior information, neural architecture search is adopted to optimize the structure of the completion network, further enhancing the robustness of the neural network and the accuracy of reconstruction. The experimental results show that our proposed method is more accurate and stable than the traditional completion methods, especially in the circumstance of a low sampling rate, and can better adapt to the situation of interrupting the probability distribution in unknown regions without a dataset.

Specific emitter identification (SEI) is a crucial technique for recognizing individual emitters based on the unique characteristics of their radio frequency signals. Deep learning (DL)-based SEI has become the dominant method for identifying and authenticating wireless devices. However, in real-world applications, electromagnetic signals are subject to dynamic channel conditions and variations across different receivers, leading to significant performance degradation when models trained on specific datasets are applied to new, unseen environments. This variability challenges traditional DL methods, making domain generalization (DG) an essential approach to tackle this issue. In this paper, we propose a robust SEI method, latent distribution mixing (LDM), to enhance model generalization in receiver-agnostic scenarios. Our approach first applies the Fourier transform to convert time-domain signals into frequency-domain representations. Then, it mixes latent feature distributions across domains in the feature space to improve robustness, enabling the model to adapt to domain shifts effectively. We evaluated our method on a cross-receiver dataset, achieving a peak performance of 88.52%, surpassing other domain generalization methods. The experimental results demonstrate that the proposed LDM method offers a promising solution for SEI tasks in cross-receiver scenarios. Our code can be downloaded from https://github.com/frownean/LDM.

In this paper, we propose a new scheme for uplink transmission of low bit rate local data in wireless systems assisted by reconfigurable intelligent surface (RIS) arrays. With an incoming user signal that has a plane polarization (horizontal or vertical), the RIS array dynamically changes the reflected signal polarization and maps the local data on the polarization state. The base station employs two antennas, one with horizontal polarization and the other with vertical polarization, and detection of the RIS data is performed using a simple power comparator. Our results show that a bit error rate (BER) floor appears when the local data is transmitted at the user symbol rate, but the BER floor vanishes when the local data rate is reduced. The proposed technique thus turns out to be particularly suitable for transmission of low bit rate local data, and it features strong robustness to imperfections of the signal polarization.

Unmanned Aerial Vehicle (UAV) recognition using Deep Learning (DL) is critical for ensuring the safety of low-altitude airspace. However, the limited availability of labeled UAV signal data poses significant challenges to achieving high recognition accuracy and robustness. To address this, we propose a novel method, Self-Supervised learning with Self-Adaptive Pseudo-Labeling (SS-SAPL), designed to enhance UAV recognition performance. The method operates in two stages: a self-supervised pre-training stage and a semi-supervised fine-tuning stage. In the pre-training stage, contrastive learning with weak and strong data augmentations is employed to extract generic feature representations from all UAV signal samples. In the fine-tuning stage, Pseudo-Labeling (PL) is combined with a Self-Adaptive Threshold (SAT) and Self-Adaptive Fairness (SAF) mechanism to improve the accuracy of PSeudo-Labels (PSLs) and leverage both labeled and unlabeled data for refining feature representations. Simulation results demonstrate the effectiveness of our method. For UAV signals at 2.4 GHz with only 30 labeled samples, our approach achieves a recognition accuracy of 82.38%, outperforming state-of-the-art methods by at least 6.63%. In mixed-frequency scenarios (2.4 GHz and 5.8 GHz) with only 10 labeled samples, our method exceeds 92.13% accuracy, surpassing competitors by at least 4.63%. These results highlight the robustness and practical value of the proposed method in challenging environments.

In smart wireless communication environments, specific emitter identification (SEI) technology has become a crucial means to ensure the security and stability of the wireless communication system. With the rapid increase in the number of Internet of Things (IoT) devices, traditional closed-set identification methods are no longer adequate to handle dynamic and complex wireless environments, particularly for unknown and rogue device intrusions. Consequently, open set SEI (OS-SEI) methods have emerged, which not only identify known devices but also effectively detect previously unseen rogue devices, thereby providing enhanced security and reliability. Therefore, this paper proposes an OS-SEI method based on reciprocal points learning and deep reconstruction learning (RPDRL). Firstly, by introducing an attention-based convolutional autoencoder (ACAE) with skip-layer connections (SC), which is used for deep reconstruction learning, along with reciprocal points learning (RPL), the extracted features become more robust. Furthermore, we design a classification algorithm that combines an appropriate fingerprint metric and extreme value theory (EVT), effectively achieving the detection of rogue devices and the classification of known devices. An open-source automatic dependent surveillance-broadcast (ADS-B) dataset and an intercom dataset are used to evaluate the RPDRL-based OS-SEI method. Experimental results indicate that the proposed method achieves an accuracy of 94.88% on the ADS-B dataset and 96.00% on the intercom dataset. Ablation experiments demonstrate the effectiveness of the efficient channel attention (ECA) modules and SC in the proposed network structure, as well as the efficacy of each loss function.

In this article, a novel multiuser multiple-input-multiple-output (MIMO) communication system for Internet of Things (IoT) is proposed, where the aerial reconfigurable intelligent surface (ARIS) and nonorthogonal multiple access (NOMA) are used as the sum rate enhancement pathway. The base station (BS) has multiple antennas that transmit superimposed signals to multiple users. The passive ARIS serves as a flexible transmit relay to reduce path loss and improve channel gains. Users are divided into several groups based on their channel status, each sharing a radio frequency (RF) chain. To maximize the sum rate of all users, the placement of ARIS, the passive/active beamforming design and the power allocation among users are jointly optimized. As the joint optimization for user grouping, passive/active beamforming and power distribution is formulated as a mixed-integer nonlinear program (MINLP) which is nonconvex and coupled and hence, obtaining an optimal solution is challenging. In this article, the problem is decoupled into three subproblems and solved alternately efficiently. The numerical results demonstrate that the suggested MIMO-ARIS-NOMA system can achieve higher sum rate performance than traditional schemes.

In millimeter-wave and terahertz band communication systems, precise beam prediction is crucial for optimizing network performance and enhancing signal transmission efficiency. Traditional beam prediction methods have primarily relied on single-modal data, which often fails to capture the comprehensive environmental information necessary for optimal accuracy. In contrast, multi-modal data-based approaches offer a more promising solution by leveraging the strengths of diverse data sources. However, many existing fusion methods are static, inadequately accounting for variations in information content across different modalities, which can hinder the full utilization of each modality’s advantages. To address these limitations, this paper proposes an advanced multi-modal beam prediction method that integrates multipath-like data augmentation (MLDA), cross-modal feature enhancement (CMFE), and an uncertainty-aware dynamic fusion mechanism. Our approach combines image and radar data to predict beam indices, dynamically adjusting the weights of different modalities to accommodate varying information densities. The proposed method employs ResNet34 for feature extraction from the multi-modal data, followed by a cross-modal feature enhancement module that aggregates complementary information from the image and radar data. Finally, the dynamic fusion mechanism integrates the predictions from the single-modal data. Experimental results demonstrate that our method significantly improves the accuracy and robustness of beam prediction, achieving an overall accuracy of 89.72%. The performance of the proposed method is further validated through comparisons with various existing methods and comprehensive ablation studies, highlighting its superiority in multi-modal assisted beam prediction scenarios.

Accurate road network extraction from remote sensing images (RSIs) is essential for applications such as urban planning, map updates, and autonomous navigation. However, challenges such as complex backgrounds, varying spatial resolutions, and occlusions hinder traditional single-modality approaches, which often fail to capture comprehensive contextual information. To address these limitations, we propose DEFNet, a novel Dual-Layer Evidential Fusion Network for robust multimodal road extraction. DEFNet features two key modules: Cross-Attention Feature Interaction (CAFI) and Dual-Layer Evidential Fusion (DEF). The CAFI module facilitates adaptive multimodal interaction at both pixel and superpixel levels, enhancing feature fusion while mitigating noise. The DEF module, leveraging the Dirichlet framework and Dempster-Shafer Theory, performs uncertainty-aware fusion, improving prediction reliability and robustness. Extensive experiments on multiple benchmark datasets demonstrate that DEFNet consistently outperforms state-of-the-art methods in both accuracy and robustness, making it highly effective for multimodal road extraction in remote sensing applications. The codes can be downloaded from GitHub1.

In this paper, we investigate the effect of spatial correlation on the performance of wireless systems with reconfigurable intelligent surface (RIS) arrays using pilot-aided channel estimation. The RIS array is partitioned into several tiles, a pilot is inserted at or near the center of each tile, and the phase of the channel coefficient estimate at the pilot location is used to determine a common phase shift for configuring all RIS elements of that tile. The analysis shows that while a large number of pilots are needed in the absence of spatial correlation to approach the performance of optimally configured RIS arrays, a small number is sufficient in the presence of spatial correlation. The implication of this is that spatial correlation between the RIS array elements appears as a desirable feature, which not only reduces the number of pilots needed for channel estimation at the base station and the receiver complexity, but also the overhead involved in the feedback of the phase information for configuring the elements of the RIS array.

In the context of emerging 6G technology challenges, this paper introduces the LSMFF-AMC approach, leveraging multimodal feature fusion (MFF) with Long-Short range attention (LSRA) to enhance automatic modulation classification(AMC). The method significantly boosts classification accuracy by employing convolutional neural networks (CNN) for diverse modal feature extraction and integrating LSRA for comprehensive feature combination. Our experiments demonstrate an increase in accuracy from 88% to nearly 97%, outperforming traditional single-modal approaches. Additionally, a convergence analysis of the training loss function reveals LSMFF-AMC's superior and faster convergence compared to standard AMC methods.

Deep learning-based recognition of Unmanned Aerial Vehicles (UAVs) has become a critical tool for enhancing UAV control through improved accuracy and efficiency. However, the practical deployment of these systems is often hampered by the costly acquisition and scarcity of annotated data, which challenges the generalizability of the models. To address this bottleneck, our study employs semi-supervised (SS) learning strategies to exploit the untapped potential of unlabeled data effectively. We introduce a novel semi-supervised approach for UAV recognition that utilizes a self-adaptive threshold mechanism. This technique features Self-adaptive Threshold (SAT) and Self-adaptive Fairness (SAF) mechanisms, designed to dynamically optimize threshold values and guarantee a balanced distribution of labels among various classes. Our method is rigorously evaluated against a comprehensive, open-source UAV dataset. The findings indicate that our semi-supervised model significantly outperforms existing supervised learning models, static threshold SS approaches, and generative models, especially in scenarios with a limited amount of labeled data. These results underscore the effectiveness of our approach in enhancing the practicality and applicability of UAV recognition systems.

With the development of wireless technology and the Internet of Things (IoT), managing limited spectrum resources has become crucial. As the IoT landscape grows, more effective wireless technique classification (WTC) is imperative. Traditional deep learning (DL) methods for WTC, while robust, suffer from high computational complexity, making them less practical for real-time applications. Addressing this, our article introduces a novel solution, the multifeature fusion broad learning network (MFBLN) for WTC, which employs broad learning (BL). Here, several features of the wireless technique are inputted into a multibranch module to obtain classification information from different perspectives. Then, those features are integrated, which performs better than the typical BL structure. Our simulation results show that our proposed MFBLN method performs well on the classic WTC data sets in the intelligent transportation system (ITS) band. The performance of MFBLN at a 25 Msps sampling rate shows an improvement of approximately 0.67%, coupled with a significant reduction in floating-point operations by 81.93%, and 72.53% decrease in training time. Additionally, the ablation studies further affirm the necessity of each module within the MFBLN framework, underscoring their collective contribution to its enhanced efficiency and effectiveness.

Deep learning (DL), renowned for its superior feature extraction capabilities, has remarkably succeeded in specific emitter identification (SEI), especially when supported by high-quality labeled data. However, obtaining accurate signal labels in complex electromagnetic environments is challenging, and manual labeling is prone to errors, underscoring the need for robust DL-based SEI methods that can handle label noise. These methods prevent neural networks from overfitting noisy labels, thereby boosting identification performance. Yet, research in this area is still limited. Our study introduces a robust label-noise SEI approach and the sample selection and regularization (SSR) method. This involves a two-stage adaptive sample selection (ASS) driven by confidence learning. The first stage entails coarse-grained separation of true and false labels through direct deep neural network (DNN) training. In the second stage, semi-supervised learning (SSL) utilizes a regularization-inspired loss, incorporating label smoothing regularization (LSR) and entropy minimization (EM), for fine-grained sample selection. The DNN is ultimately trained on precisely selected true-labeled samples. Comparative experiments on the automatic dependent surveillance-broadcast (ADS-B) and Wi-Fi data sets demonstrate that our SSR method outperforms the existing methods in identification accuracy, particularly at a 20% label-noise ratio, achieving 86.00% accuracy with the ADS-B data set, and 99.38% with the Wi-Fi data set. The code is available at:https://github.com/sleepeach/SSR-SEI.

Automatic modulation classification (AMC) holds a significant position in physical-layer security, offering an innovative method to enhance the security of data transmission and anti-interference ability. Recently, deep learning (DL) has seen extensive application in radar and communication signal classification, which requires sufficient labeled training data to ensure great classification performance. However, obtaining a significant amount of labeled samples is extremely challenging in complex and ever-changing electromagnetic environments. Therefore, we propose a novel few-shot AMC method using architecture search and knowledge transfer. This method first utilizes an advanced neural architecture search algorithm,$\Lambda $-DARTS, to automatically search for the optimal network structure (i.e., Auto-MCNet) based on the auxiliary sample set. Then, the Auto-MCNet model is pretrained on the auxiliary data set to explore prior knowledge about signal classification. Finally, we transfer the knowledge to a few-shot training data set and fine-tune the Auto-MCNet model to enhance its generalization ability. The simulation results indicate that when the signal-to-noise ratio (SNR) is greater than 0 dB and the shot of each class is 3 and 10, the average accuracy of the proposed Auto-MCNet is higher than 81% and 90%, respectively. Moreover, compared to advanced competitors, Auto-MCNet achieves higher classification performance with lower model complexity.

This article proposes a new framework of aerial reconfigurable intelligent surface (ARIS) enhancing the nonorthogonal multiple access (NOMA) system. The base station (BS) transmits superimposed signals to multiple users with different channel gains through ARIS which can flexibly change channel conditions and perform intelligent NOMA operations. It ensures that our system can perform well in providing services to multiple users simultaneously. In this system, the placement of the unmanned aerial vehicle (UAV) is jointly optimized along with the AIRS passive beam and the multiuser power allocation in order to maximize the communication sum rate. Since the joint optimization problem is nonconvex and coupled, it is hence disintegrated into three subproblems and it is solved alternately through the successive convex approximation (SCA). Moreover, semi definite programming (SDP) is used to deal with the rank one constraint of RIS reflection matrix and comparisons are made using particle swarm optimization (PSO). The numerical results show that the proposed ARIS-NOMA framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

Reconfigurable intelligent surfaces (RIS) are currently drawing a lot of attention in the research community as a key technology for future wireless networks. In addition to boosting the signal-to-noise ratio and improving coverage for cellular users, they can also be used to transmit locally collected data either by partitioning the RIS array into tiles and mapping the local data to the indexes of the activated tiles (or groups of tiles) following the concept of spatial modulation (SM), or by activating all RIS elements and mapping the local data onto a set of common phase shifts. The problem of the first technique, which we refer to as RIS-SM, is that spatial correlation between elements of the RIS array strongly degrades the bit error rate performance. In this paper, we focus on the second technique, and we investigate the transmission of low bit rate local data using the concept of hierarchical transmission. The basic idea behind this technique is that the magnitude of the common phase shifts must be maintained at a very small value in order to keep the performance degradation caused by the local data on the user data within an acceptable limit. For the local RIS data, this constraint leads to a small minimum Euclidean distance in the signal constellation plane, but despite this small minimum distance, the desired performance is achieved by limiting the speed of the local RIS data to a fraction of the user symbol rate. Analytic minimum distance calculations and simulation results are provided to demonstrate the efficiency of the proposed hierarchical transmission technique.

To reduce the outage of the connection between unmanned aerial vehicles (UAVs) and cellular networks in complex real-time channel state, and reduce the energy consumption of UAV during flight mission, an online trajectory optimization scheme of UAV based on outage probability knowledge map reconstruction is proposed. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers. The UAV first samples sparsely from the target area and calculates the outage probability of the sampling point, and then uses the Kriging algorithm to reconstruct the outage probability knowledge map. Based on the reconstructed outage probability knowledge map, with the goal of minimizing the energy consumption of UAV task execution, the UAV trajectory optimization problem is established, and a trajectory optimization algorithm based on deep reinforcement learning (DRL) is proposed to solve it. Numerical results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

Abstract Energy efficiency (EE) is an important indicator in ultra‐reliable and low‐latency communication (URLLC). Power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem is non‐convex, it is difficult to obtain the analytical solution efficiently. Moreover, to ensure reliable and low‐latency communication within a finite blocklength, the Shannon formula becomes impractical for URLLC. Therefore, finite blocklength coding theory is used to meet the requirements of URLLC. In this paper, the EE problem of URLLC is formulated and the power allocation function is parameterized to be optimized through a deep neural network (DNN). The DNN is trained through the primal‐dual iterative algorithm offline in the unsupervised manner, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.

This paper analyzes the power imbalance factor on the uplink of a 2-user Power-domain NOMA system and reveals that the minimum value of the average error probability is achieved when the user signals are perfectly balanced in terms of power as in Multi-User MIMO with power control. The analytic result is obtained by analyzing the pairwise error probability and exploiting a symmetry property of the error events. This result is supported by computer simulations using the QPSK and 16QAM signal formats and uncorrelated Rayleigh fading channels. This finding leads to the questioning of the basic philosophy of Power-domain NOMA and suggests that the best strategy for uncorrelated channels is to perfectly balance the average signal powers received from the users and to use a maximum likelihood receiver for their detection.

This paper studies a new framework of aerial reconfigurable intelligent surface (ARIS) assisted non-orthogonal multiple access (NOMA) for wireless communication systems. The base station transmits superimposed signals to multiple users with different channel gains through ARIS which can be deployed flexible. The placement of the unmanned aerial vehicle (UAV) and the passive beamforming of the ARIS are jointly optimized to maximize the sum rate. The non-convex problem is decomposed into two subproblems and solved alternately through the successive convex approximation (SCA). The numerical results show that our proposed NOMA-ARIS framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

The ultra-reliable and low-latency communication (URLLC) is one of the critical scenarios in future communications. Energy efficiency (EE), as an important indicator in URLLC, has attracted more and more attention especially in the fields of industrial internet and automation control, etc. At present, power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem in URLLC is usually formulated in the form of fractions with several statistical constraints, it is difficult to obtain the real time analytical solution. Moreover, the traditional expression based on Shannon formula is no longer applicable. In this paper, we formulate the EE problem of URLLC and adopt an unsupervised learning method to parameterize the power allocation function to be optimized through a deep neural network (DNN). The DNN is trained through the primal-dual iterative algorithm offline, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.

Energy-efficiency (EE) is a critical metric within wireless optimization. Power control over fading channels is considered as a promising EE-improving technique, but requires optimization of a series of fractional functional optimization problems which are hard to handle by existing optimization techniques. In this paper, we propose a novel EE power control method with unsupervised learning. Firstly, the original fractional problems are decomposed into sub-problems by Dinkelbach and quadratic transformations. Then, these sub-problems are reformulated into unconstrained forms through Lagrange dual formulation. Furthermore, unsupervised primal-dual learning method is applied to handle these unconstrained problems with strong duality. Finally, The unsupervised primal-dual learning is implemented by the deep neural network (DNN) with low computational complexity. Simulation results verify the effectiveness of the proposed approach on a number of typical wireless optimizing scenarios. It is shown that compared to conventional algorithms our method achieves better performance in cognitive radio networks, interference networks, and OFDM networks.

Reconfigurable intelligent surfaces (RIS) are currently drawing a lot of attention in the research community as a key technology for future wireless networks. In addition to boosting the signal-to-noise ratio, they can also be used to transmit data in the same way spatial modulation (SM) transmits data by mapping it to the activated antenna indices in MIMO systems. The problem of this transmission technique which we refer to as RIS-SM is that spatial correlation between elements of the RIS array strongly degrades bit error rate performance. In this paper, we analyze this degradation and introduce two techniques to compensate for spatial correlation. The first employs tile-specific phase shifts in the RIS elements and the second employs dynamic phase shifts that are specific to the RIS patterns activated by the information bits to be transmitted. The analysis and the simulation results show that the proposed techniques provide substantial performance improvements and make RIS-SM transmission reliable even in the presence of very strong spatial correlation.

Millimeter wave (mmWave) and massive multiple input multiple output (MIMO) are recognized as key technologies in the forthcoming beyond the fifth-generation (B5G) and the sixth-generation (6G) wireless networks. In this paper, a multibeam MIMO non-orthogonal multiple access (NOMA) scheme with weighted beam superposition for mmWave is proposed to enhance the system sum rate while ensuring fairness of users as much as possible. Specifically, a method of power allocation is adopted to guarantee the minimum demanded for the quality of service (QoS) of the weak users (lower channel gain)in each group. Furthermore, to improve further the sum rate after the QoS of the weak users is satisfied, the coefficient of strong users' beam gain are set to the largest value. In system simulations, we compare the performance of three multi-beam schemes, i.e, beam splitting, beam superposition and the proposed scheme, together with a single beam scheme, and a TDMA scheme at different levels of the SNR. The simulation results demonstrate that the system sum rate of the proposed method is much higher than TDMA scheme and competitive compared to the best scheme.

Intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) multiple-input single-output (MISO) is considered one of the promising techniques in next-generation wireless communication. However, existing beamforming methods for IRS-aided mm Wave MISO systems require high computational power, so it cannot be widely used. In this paper, we combine an unsupervised learning-based fast beamforming method with IRS-aided MISO systems, to significantly reduce the computational complexity of this system. Specifically, a new beamforming design method is proposed by adopting the feature fusion means in unsupervised learning. By designing a specific loss function, the beamforming can be obtained to make the spectrum more efficient, and the complexity is lower than that of the existing algorithms. Simulation results show that the proposed beamforming method can effectively reduce the computational complexity while obtaining relatively good performance results.