The development of Large AI Models (LAMs) for wireless communications, particularly for complex tasks like spectrum sensing, is critically dependent on the availability of vast, diverse, and realistic datasets. Addressing this need, this paper introduces the ChangShuoRadioData (CSRD) framework, an open-source, modular simulation platform designed for generating large-scale synthetic radio frequency (RF) data. CSRD simulates the end-to-end transmission and reception process, incorporating an extensive range of modulation schemes (100 types, including analog, digital, OFDM, and OTFS), configurable channel models featuring both statistical fading and site-specific ray tracing using OpenStreetMap data, and detailed modeling of realistic RF front-end impairments for various antenna configurations (SISO/MISO/MIMO). Using this framework, we characterize CSRD2025, a substantial dataset benchmark comprising over 25,000,000 frames (approx. 200TB), which is approximately 10,000 times larger than the widely used RML2018 dataset. CSRD2025 offers unprecedented signal diversity and complexity, specifically engineered to bridge the Sim2Real gap. Furthermore, we provide processing pipelines to convert IQ data into spectrograms annotated in COCO format, facilitating object detection approaches for time-frequency signal analysis. The dataset specification includes standardized 8:1:1 training, validation, and test splits (via frame indices) to ensure reproducible research. The CSRD framework is released at https://github.com/Singingkettle/ChangShuoRadioData1The dataset is designed to be fully reproducible using the provided framework, configurations, and configurable fixed random seeds to accelerate the advancement of AI-driven spectrum sensing and management.

Interference cancellation has always been a crucial task in wireless communications, especially in the presence of nonlinear distortions caused by power amplifier. However, when considering the ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) systems, this task becomes more challenging as the channel estimation will be severely impacted by the nonlinearity, thus leading to significant performance degradation. Driven by solving this problem, this paper proposed a novel nonlinear signal processing method, referred to as log-sum-minimization sparse channel estimation based nonlinearity cancellation (LSMSCE-NC). In detail, an optimization model based on the log-sum norm minimization and nonlinearity cancellation is first established and then its iterative solution is also presented. The numerical results reveal that the proposed LSMSCE-NC method achieves significant bit error rate (BER) and normalized mean square error (NMSE) advantages compared to the state-of-the-art algorithm.

Automatic Modulation Classification (AMC) using deep learning techniques has become a prominent area of research, showcasing considerable practical applications. However, the present AMC deep learning network, trained on a specific channel model, performs poorly in a new channel scenario. To deal with this, an adversarial semi-supervised domain adaptation method is proposed. Specifically, classification accuracy, cluster sensitivity, and distribution distance are optimized together, utilizing a three-stage iterative training approach. As a result, the proposed model has achieved robust performance with a limited amount of labeled data when shifting to a new channel.

The power amplifier (PA) is an indispensable component in wireless communication systems, while the nonlinearity induced by PA can lead to significant performance degradation. The conventional nonlinearity equalization (NLE) method can effectively mitigate the nonlinear effects and provide superior BER performance but requires intensive computational complexity. To this end, we propose a novel NLE method in the time domain, which can significantly reduce the computational complexity without sacrificing the BER performance. Specifically, we first propose a novel PA representation of the sum of products (SPs), which is a unified time-domain representation for several typical memory and memoryless PA models. On this basis, the SPs-iterative least square equalizer (SPs-ILSE) method is proposed to mitigate the impact of both the memory and memoryless PA’s nonlinear distortions at the receiver side. The computational complexity of complex multiplication (CCCM) in the proposed method isO(NlogN) for the memoryless PA models andO(KN2) for the memory PA models. Moreover, considering the commonly utilized PA models, we also derive the closed-form expression for the achievable SINR of the SPs-ILSE method in the ideal conditions. Numerical results show that (i) the closed-form SINR expression is valid for both the memory and memoryless scenarios (ii) the proposed method exhibits the superior bit error rate (BER) performance in comparison to several relevant nonlinear signal processing methods such as digital pre-distortion (DPD), and power amplifier nonlinearity cancellation (PANC) (iii) the proposed NLE method achieves the same BER performance as the previous NLE method, i.e., reconstruction of distorted signals (RODS), while the CCCM of the proposed method is much lower.

Radio frequency fingerprint identification (RFFI) is a physical layer security methodology to recognize individual devices by leveraging hardware imperfections inevitably induced in the manufacturing process. However, the performance degradation caused by the time-varying channel impacts and interferences has severely restricted the development of RFFI. To this end, we present a channel-agnostic RFFI system, which consists of three modules, i.e., signal preprocessing module, feature extraction module, and classification module. In the signal preprocessing module, we first propose a novel approach, referred to as limiter-based spectral circular shift bidirectional division (LB-SCSBD), to generate two parallel spectral quotient (SQ) sequences. Then, we define the spectral quotient constellation (SQC) symbols according to different modulation formats, and thereby transform the SQ sequences into four magnitude-based sequences in terms of two channel-robust signal representations, i.e., the SQ magnitude (SQM) and SQC error vector magnitude (SQC-EVM). In the feature extraction module, we present a moment-based statistical feature extractor (MB-SFE) to extract the device-specific information from the above four sequences. In the classification module, the extracted statistics are fed into the multi-class support vector machine (SVM) for training and testing. We take WiFi as a case study and evaluate the performance of the proposed RFFI system by classifying eight simulated device models and six universal software radio peripheral (USRP) transmitter radios. Experimental results show that (i) the proposed method achieves the accuracies of 99.84% and 98.26% with eight devices in QPSK and 16QAM cases, as well as the accuracy of 92.42% with six USRP devices (ii) the proposed method exhibits superior classification performance in comparison to some existing RFFI methods, leading to a significant accuracy improvement of at least 38.33%.

Automatic modulation classification (AMC) is a crucial technique for the design of intelligent transceivers and has received considerable research attention. Conventional feature-based (FB) methods have the advantage of low computational complexity. However, these methods are highly sensitive to the distribution shifts of the received signal caused by the variation of channel effects and have rarely been studied in orthogonal frequency division multiplexing (OFDM) systems under unseen synthetic channels with multipath fading effects, carrier frequency offset (CFO), phase offset (PO) and additive noise. To solve this problem, this paper proposes a novel FB method using the error vector magnitude (EVM) features for AMC tasks (termed as EVM-AMC), which can achieve reliable classification performance for the communication scenarios considering unseen synthetic channels in OFDM systems. Specifically, we first propose the axisymmetric mapping-based self-circulant differential division (AM-SCDD) algorithm to convert the received signal into the non-negative spectral quotient (NNSQ) sequence, deeply suppressing the synthetic channel effects. Subsequently, we derive the EVM features by analyzing the matched error vectors between the generated NNSQ sequence and the predefined NNSQ constellation symbol (NNSQCS) masks. During this process, a percentile-based filter is utilized to remove the outliers in each matched error vector. Finally, the feature samples collected from various channel conditions are sent to the multi-class support vector machine (SVM) classifiers for training and testing. Two candidate modulation type sets are employed to evaluate the performance of the proposed EVM-AMC method under both the constant and changing channel conditions. Our numerical results demonstrate that 1) the proposed method exhibits impressive robustness and generalization when dealing with unseen synthetic channels, 2) the proposed method yields the best classification performance when compared to the conventional FB AMC methods in the presence of channel effects.

Radio frequency fingerprint identification (RFFI) is a promising physical layer security technique that employs the hardware-introduced features extracted from the received signals for device identification. In this paper, we consider an RFFI problem in the presence of hybrid time-varying distortions (HTVDs) induced by multipath fading channel, carrier frequency offset (CFO), and phase offset. To solve this problem, an HTVDs-robust RFFI framework is proposed. Firstly, we derive that the residual HTVDs after CFO correction can be approximated as multiplicative interference in the frequency domain. Secondly, we define a novel signal analysis dimension named spectral quotient (SQ) representation and then present the spectral circular shift division (SCSD) method to generate the HTVDs-robust SQ signals, where the multiplicative interference can be suppressed. Thereafter, the statistics including root mean square (RMS), variance (VAR), skewness (SKE), and kurtosis (KUR) are extracted from the real and imaginary components of the SQ signals, respectively. Finally, the statistical features are used for the training and testing of the support vector machine (SVM) classifiers. To further enhance the performance of the proposed RFFI scheme, we also present the spectral circular multi-shift division (SCMSD) method, which increases the flexibility in the generation of the HTVDs-robust SQ signals. Given what we knew, this is the first time attempting to mitigate the HTVDs by leveraging the strong frequency correlation at the neighboring subcarriers in the multivariate hypothesis tasks. Compared to several handcraft feature-based RFFI methods, the proposed method exhibits superior identification accuracy and strong robustness. Experimental results show that the proposed RFFI scheme can achieve the accuracy of 91.3%with five devices and 86.4% with sixteen devices when the classifiers are trained with the additive white Gaussian noise but are tested with the Rayleigh channel.