In GNSS-denied environments, signals of opportunity (SOP) offer an efficient and passive solution for navigation and positioning by utilizing ambient signals. Nevertheless, conventional SOP techniques face significant challenges in real-time processing, especially under sub-Nyquist sampling conditions, due to high data acquisition rates and off-grid errors. To address this, this paper proposes the signal reconstruction and kernel sparse encoding (SRKSE) model, a novel general framework for high-precision parameter estimation. By combining compressed sensing with a deep unfolding network, the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors. Key innovations of SRKSE include dual cross-attention mechanisms for enhanced feature extraction, sinc sparse kernel encoding to minimize quantization errors, and a custom loss function for balanced optimization. With these advancements, SRKSE achieves up to a 650-fold improvement in time of arrival (TOA) estimation accuracy while operating at just 1% of the Nyquist sampling rate. The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency, especially when operating under sub-Nyquist sampling conditions. Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.

Spectrum map construction, which is crucial in cognitive radio (CR) system, visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation. Traditional reconstruction methods are generally for two-dimensional (2D) spectrum map and driven by abundant sampling data. In this paper, we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional (3D) spectrum map under multi-radiation source scenarios. We firstly design a maximum and minimum path loss difference (MMPLD) clustering algorithm to detect the number of radiation sources in a 3D space. Then, we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm. Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data. Finally, the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources. Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment, but also achieves high spectrum construction accuracy even when the sampling rate is very low.

Recently, uniform circular array (UCA) based orbital angular momentum (OAM) beam steering schemes have been proposed to overcome the limitations of coaxial transmission. Unlike the traditional multiple-input-multiple-output (MIMO) beam steering, OAM beam steering includes both the OAM generation and the beam steering. Generally, the true time delay (TTD) or the phase shifter (PS) are required for beam steering in the radio domain. Previous studies suggest that TTD is preferred for wideband MIMO beam steering to avoid beam squint caused by PS. However, in this paper, we theoretically prove that to generate the OAM beam ideally, PS should be used, while TTD deteriorates the mode orthogonality, which is influenced by the relative bandwidth. Once the ideal OAM beam is generated, TTD is required to prevent beam squint. Based on this analysis, we propose to use the two-stage phase-shifting (TSPS) architecture for OAM beam steering: PS for OAM generation and TTD for beam steering. Simulation results suggest that compared to the spectrum efficiency (SE) of PS based OAM communication, the SE based on the TTD significantly declines as the relative bandwidth increases. Furthermore, OAM beam steering using the TSPS architecture greatly outperforms systems that adopt a single TTD or PS network.

This paper studies wireless vehicular com-munication (VehCom) in intelligent transportation systems using an orthogonal frequency division multiplexing with index modulation (OFDM-IM). In the concept of IM, data is transmitted not only through the modulated symbols but also via the indices of the active subcarriers. In contrast to the original OFDM, OFDM-IM activates only non-zero subcarriers, increasing energy efficiency. However, the pilot-assisted channel estimation (CE) method is a significant challenge in OFDM-IM, where the desired pilot subcarrier interval is related to the OFDM-IM subblock length. This paper proposes a walsh-scattered pilot-assisted CE for OFDM-IM VehCom. The optimum walsh-scattered pilot assignment is proposed to improve the transmission efficiency. Furthermore, a space-time block code with a high transmit diversity gain is employed for OFDM-IM VehCom to enhance VehCom's signal quality. The results show that the proposed method performs higher CE accuracy and better bit-error rate with significant spectral and energy efficiencies than conventional methods.

The acquisition of position information of legitimate users and jammers plays an important role in the emerging non-geostationary synchronous orbit (NGSO) satellite communications. In this paper, we study the multi-signal localization problem in an uplink NGSO satellite communication system.
We propose an onboard localization scheme based on multiple observations from the satellite, together with the geometric constraints of the satellite postions, the signal positions, the attitude of the satellite, and the angle-of-arrival (AoAs) of the signals. We develop a massage-passing algorithm, termed the Bayesian blind multi-signal localization (BMSL), to jointly estimate the AoAs and the signal positions. The Cramér-Rao lower bound (CRLB) is derived to characterize the fundamental performance limit of the considered localization problem. Simulation results show that the proposed BMSL algorithm can perform close to the derived CRLB and significantly outperforms its counterpart algorithms.

Residual loop-interference (LI) poses a significant challenge for the full-duplex (FD) unmanned aerial vehicle (UAV). To address the issue of residual LI, this paper proposes an amplify-and-forward (AaF) FD-UAV relay system based on a novel orthogonal frequency division multiplexing (OFDM) technique, in which a signal model of infinite impulse response (IIR) is established, instead of the classical finite impulse response (FIR). In the proposed scheme, the residual LI is considered a useful signal and can be combined with the novel OFDM to establish the IIR signal model. Meanwhile, the guard interval (GI) is designed to maintain the circular convolution structure, which differs from the cyclic prefix (CP) applied by the classical OFDM. At the receiver, the IIR signals are influenced only by Gaussian white noise. The proposed FD-UAV relay system can maintain a satisfactory bit error rate (BER) even in the presence of significant residual LI, compared to conventional solutions for suppressing LI on FD-UAV relay. Numerical simulations validate that our proposed scheme offers a fresh solution to the residual LI problem in FD-UAV communication.

In this article, a graphic design method for broadband Doherty power amplifier (DPA) is proposed based on the basic principle of impedance matching with the help of Smith chart. The proposed graphic method avoids the complex formula derivation in the traditional amplifier circuit design process, and the design process is more simple and intuitive. Besides, it only takes three steps to build the load modulation network (LMN) of two power amplifiers (PA) of the DPA. Besides, a capacitor is used to replace the parasitic parameters of the transistor, and the LMN designed in the two modes is used for exploration and comparison.
Further more, the output impedance of the peaking PA is introduced to make the reflection coefficient trajectory on Smith chart low-frequency dispersion so as to expand the bandwidth of the DPA at the output power back-off (OBO) level. It would not affect the performance of DPA in the saturation (SAT) state. In this way, a broadband DPA can be implemented easily. To validate the proposed design method, a broadband DPA operating from 1.9 to 2.6 GHz is designed and measured based on the proposed method.~Under the continuous-wave excitation, the fabricated DPA has a 6 dB OBO efficiency of 48%-56% and a SAT efficiency of 64%-73% from 1.75 to 2.45 GHz, and the peak output power is 48.9-49.8 dBm.

Blind recognition of low-density parity-check (LDPC) codes has gradually attracted more attention with the development of military and civil communications. However, in the case of the parity-check matrices with relatively high row weights, the existing blind recognition algorithms based on a candidate set generally perform worse. In this paper, we propose a blind recognition method for LDPC codes, called as tangent function assisted least square (TLS) method, which improves recognition performances by constructing a new cost function. To characterize the constraint degree among received vectors and parity-check vectors, a feature function based on tangent function is constructed in the proposed algorithm. A cost function based on least square method is also established according to the feature function values satisfying the parity-check relationship. Moreover, the minimum average value in TLS is obtained on the candidate set. Numerical analysis and simulation results show that recognition performances of TLS algorithm are consistent with theoretical results. Compared with existing algorithms, the proposed method possesses better recognition performances.

In the dispersed computing environment d-riven by intelligent networks, intrusion detection faces significant challenges. This paper proposes a multilayer decentralized federated learning framework based on mean field game theory (MFG-DFL). The framework organizes networked computing points (NCPs) into a three-layer collaborative architecture, and innovatively introduces MFG theory to model the complex dynamic interactions, which among large-scale NCPs as a game between a representative NCP and the mean field. By solving the coupled HJB and FPK equations, we design a dynamic incentive mechanism to fairly quantify and reward NCP contributions, thus aligning individual rationality with the global objectives of the system. The simulation results on the CICIoT2023 data set demonstrate the outstanding performance of the proposed framework. Specifically, it achieves an intrusion detection accuracy of 81.09% in highly non-IID scenarios, showcasing a well-balanced trade-off between computational efficiency and performance enhancement.

Cognitive unmanned aerial vehicle (UAV) is promising to tackle the spectrum scarcity problem faced by UAV communications. However, the secure information transmission is challenging due to the open nature of the spectrum sharing. In order to tackle this issue, a cognitive UAV network with cooperative jamming is studied in this paper. A robust resource allocation and trajectory joint optimization problem is formulated by considering the practical case that the channel state information (CSI) cannot be accurately obtained. An iterative algorithm is proposed to address this challenging non-convex problem. Simulation results demonstrate that the worst case robust resource allocation design can realize the secure communications even under the imperfect CSI. Moreover, compared with other benchmark schemes, the proposed scheme can achieve secure performance improvement.

In this paper, we analyze the physical layer security (PLS) performance of a free-space optical (FSO) communication system composed of a transmitting satellite and ground users. Specifically, the FSO fading channels follow the Málaga distribution. Further, we scrutinize the influence of non-zero boresight pointing errors and angle-of-arrival fluctuations on the PLS performance for the first time. We derived the probability density function and cumulative density function of the FSO link, followed by the closed-form expressions of the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC). The asymptotic SOP expression at the high signal-to-noise ratio regime and diversity order are also provided to reveal the physical mechanism of the PLS of the considered system. Finally, Monte Carlo simulation results are presented to verify the correctness of the analytical expressions. The results afford helpful insights for the future design of satellite FSO communication systems.

The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services. However, the complex network environment and high level of dynamism pose challenges to network selection decisions. Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition, resulting in problems such as handover failure and frequent handover, ultimately impacting the quality of the user communication service. To address these problems, we propose an intelligent switching method, iMALSTM-DQN, which integrates an improved Multi-level Associative Long Short-Term Memory model (iMALSTM) with Deep Reinforcement Learning (DRL). The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region, thereby enhancing the prediction accuracy of network states. Subsequently, based on the predicted network state, we employ the Deep Q Network (DQN) model to make handover decisions, adaptively determining the optimal switching and network selection strategy through interaction with the environment. Experimental results demonstrate that the proposed algorithm enhances decision timeliness, significantly reduces the number of switch failures, and alleviates the problem of frequent handovers resulting from network dynamics.

Frequency hopping (FH) communication has good anti-fading, anti-jamming and anti-eavesdr-opping capabilities, so it is one of the main ways to combat electronic jamming. In order to further improve the anti-jamming capability of FH communication, the parameters such as fixed frequency interval, hopping rate and hopping frequency in conventional FH can be assigned with time-varying characteristics. In order to set appropriate hopping parameters to improve the performance of the system in the electromagnetic environment with various types of jamming, a heuristically accelerated Q-learning (HAQL) method is proposed in this paper. Firstly, a theoretical model for the parameter decision-making of FH system is made, and the key parameters affecting the energy efficiency of the system are analyzed. Secondly, a Q-learning model in complex electromagnetic environment is proposed, which includes setting states, actions and rewards, as well as a HAQL-based decision-making algorithm is put forward. Lastly, simulations are carried out under different jamming environments, and simulation results show that the average energy efficiency of HAQL algorithm is higher than that of the SARSA algorithm, the ∊-greedy QL algorithm and the HQL-OSGM algorithm, respectively.

Future mobile networks in the sixth generation (6G) are poised for a paradigm shift from conventional communication services toward comprehensive information services, driving the evolution of radio access network (RAN) architectures toward enhanced cooperation, intelligence, and service orientation. Building upon the concept of centralized, collaborative, cloud, and clean RAN (C-RAN), this article proposes a novel cooperative, intelligent, and service-based RAN (CIS-RAN) architecture. Focusing on cooperation, CIS-RAN extends the traditional cooperative communication paradigm by further integrating cooperative sensing and cooperative artificial intelligence (AI). To improve both performance and effectiveness across diverse application scenarios, CIS-RAN enhances network cooperation throughout the entire process of acquisition, transmission, and processing, thereby enabling efficient information acquisition, diverse cooperative interactions, and intelligent fusion decision-making. Key technologies are discussed, with network cooperative multiple-input multiple-output (MIMO) examined as a case study, demonstrating superior performance over traditional architectures, as demonstrated by numerical results. Future research directions are outlined, emphasizing the continued exploration and advancement of the CIS-RAN architecture, particularly in enhancing network cooperation.

Reconfigurable intelligent surface (RIS) have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz (THz) communications. Owing to large-scale array elements at transceivers and RIS, the codebook based beamforming can be utilized in a computationally efficient manner. However, the codeword selection for analog beamforming is an intractable combinatorial optimization (CO) problem. To this end, by taking the CO problem as a classification problem, a multi-task learning based analog beam selection (MTL-ABS) framework is developed to implement cooperative beam selection concurrently at transceivers and RIS. In addition, residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features. Finally, the network convergence is analyzed from a blockwise perspective, and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.

This article studies the problem of image segmentation-based semantic communication in autonomous driving. In real traffic scenes, the detecting of objects (e.g., vehicles and pedestrians) is more important to guarantee driving safety, which is always ignored in existing works.
Therefore, we propose a vehicular image segmentation-oriented semantic communication system, termed VIS-SemCom, focusing on transmitting and recovering image semantic features of high-important objects to reduce transmission redundancy.
First, we develop a semantic codec based on Swin Transformer architecture, which expands the perceptual field thus improving the segmentation accuracy.
To highlight the important objects' accuracy, we propose a multi-scale semantic extraction method by assigning the number of Swin Transformer blocks for diverse resolution semantic features.
Also, an importance-aware loss incorporating important levels is devised, and an online hard example mining (OHEM) strategy is proposed to handle small sample issues in the dataset.
Finally, experimental results demonstrate that the proposed VIS-SemCom can achieve a significant mean intersection over union (mIoU) performance in the SNR regions, a reduction of transmitted data volume by about 60% at 60% mIoU, and improve the segmentation accuracy of important objects, compared to baseline image communication.

Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks (SGCN) must overcome. To address these problems, we provide a combined optimization approach that makes use of cognitive radio (CR) and non-orthogonal multiple access (NOMA) technologies. Our work focuses on using user pairing (UP) and power allocation (PA) techniques to maximize energy efficiency (EE) in SGCN, particularly within neighbourhood area networks (NANs). We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting. This problem is NP-hard, nonlinear, and nonconvex by nature.
To address the computational complexity of the problem, we use the block coordinate descent (BCD) method, which breaks the problem into UP and PA subproblems. Initially, we proposed the zebra-optimization user pairing (ZOUP) algorithm to tackle the UP problem, which outperforms both orthogonal multiple access (OMA) and non-optimized NOMA (UPWO) by 78.8% and 13.6%, respectively, at a SNR of 15 dB. Based on the ZOUP pairs, we subsequently proposed the PA approach, i.e., ZOUPPA, which significantly outperforms UPWO and ZOUP by 53.2% and 25.4%, respectively, at an SNR of 15 dB. A detailed analysis of key parameters, including varying SNRs, power allocation constants, path loss exponents, user density, channel availability, and coverage radius, underscores the superiority of our approach. By facilitating the effective use of communication resources in SGCN, our research opens the door to more intelligent and energy-efficient grid systems. Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.

In hybrid beamforming design using the conventional gradient projection (GP) algorithm, it is common to use a fixed step size, which results in a slow convergence rate and unsatisfactory achievable rate performance. This paper employs a deep unfolding algorithm within a small fixed number of iterations to tackle the hybrid beamforming optimization problem. The optimal step size is obtained by combining the conventional GP algorithm with the deep learning technique, and every step in deep learning is explainable. Simulation results show that the proposed deep unfolding algorithm demonstrates a lower computational time and superior achievable rate performance than the conventional GP algorithm.

Integrated sensing and communication (IS-AC) is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC. In this paper, we investigate the beamforming design problem for multiple-input multiple-output (MIMO) ISAC systems and propose to maximize the radar beampattern gain of the target direction while ensuring the signal-to-interference-plus-noise ratio (SINR) constraints of communication users. Particularly, we discuss two cases of ISAC transmit beamforming, i.e., Case-I and Case-II, which do not have and do have the dedicated probing signal, respectively. For these two cases of transmit beamforming design problems, we start from the single-user scenario and provide the closed-form solutions for MIMO ISAC beamforming vectors. Then, we consider the multi-user scenario and utilize the semidefinite relaxation technique to convert the beamforming design problems into convex semidefinite programming problems. Furthermore, we investigate the impact of the channel correlation between radar and communication on the performance gain of MIMO ISAC systems and characterize the performance tradeoff. Numerical results validate that the dedicated probing signal is unnecessary in the single-user scenario, whereas it has a slight improvement in target detection performance at low SINR thresholds in the multi-user scenario. It is also shown that the stronger the correlation between radar and communication channels, the greater the performance gain of the system.

In massive multiple-input multiple-output (MIMO) systems utilizing frequency division duplexing, optimizing system performance requires user equipment (UE) to compress downlink channel state information (CSI) and transmit it to the base station (BS). As the number of antennas increases, there is a significant rise in the overhead related to CSI feedback, posing considerable challenges to the precise acquisition of CSI by the BS. Existing approaches to CSI feedback utilizing deep learning techniques face challenges such as significant feedback overhead and limited precision in the reconstruction process. This study presents a novel lightweight CSI feedback framework known as the dual attention neural network (DANet). Within the DANet architecture, a dual attention module (DAM) is designed to enhance the network's performance. This DAM includes both channel attention blocks and spatial attention blocks. The channel attention blocks direct the model's focus toward channel features rich in information content while simultaneously suppressing less significant features. This approach enables the extraction of temporal correlations within the CSI matrix. The spatial attention block aids in extracting the correlation between the delay domain and the angle domain in the CSI matrix. By enhancing neural network performance, the DAM reduces information dispersion while enhancing the representation of global interactions. Simulation results demonstrate that DANet exhibits superior normalized mean square error and cosine similarity with comparable complexity compared to existing advanced CSI feedback methods.

Text semantic extraction has been envisio-ned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future sixth-generation (6G) wireless networks. In this paper, we propose a Chinese text semantic extraction model, namely T-Pointer, to improve the quality of semantic extraction by integrating the Transformer with the pointer-generator network. The proposed T-Pointer model consists of a semantic encoder and a semantic decoder. In the encoding stage, we use the multi-head attention mechanism of the Transformer to extract semantic features from the input Chinese text. In the decoding stage, we first use the Transformer to extract multi-level global text features. Then, we introduce the pointer-generator network model to directly copy the keyword information from the source text. The simulation results demonstrate that the T-Pointer model can improve the bilingual evaluation understudy (BLEU) and recall-oriented understudy for gisting evaluation (ROUGE) by 14.69% and 14.87% on average in comparison with the state-of-the-art models, respectively. Also, we implement the T-Pointer model on a semantic communication system based on the universal software radio peripheral (USRP) platform. The result shows that the packet delay of semantic transmission can be reduced by $52.05\%$ on average, compared to traditional information transmission.

As the types of traffic requests increase, the elastic optical network (EON) is considered as a promising architecture to carry multiple types of traffic requests simultaneously, including immediate reservation (IR) and advance reservation (AR). Various resource allocation schemes for IR/AR requests have been designed in EON to reduce bandwidth blocking probability (BBP). However, these schemes do not consider different transmission requirements of IR requests and cannot maintain a low BBP for high-priority requests. In this paper, multi-priority is considered in the hybrid IR/AR request scenario. We modify the asynchronous advantage actor critic (A3C) model and propose an A3C-assisted priority resource allocation (APRA) algorithm. The APRA integrates priority and transmission quality of IR requests to design the A3C reward function, then dynamically allocates dedicated resources for different IR requests according to the time-varying requirements. By maximizing the reward, the transmission quality of IR requests can be matched with the priority, and lower BBP for high-priority IR requests can be ensured. Simulation results show that the APRA reduces the BBP of high-priority IR requests from 0.0341 to 0.0138, and the overall network operation gain is improved by 883 compared to the scheme without considering the priority.