MCML-BF: A Metal-Column Embedded Microstrip Line Transmission Structure with Bias Feeders for Beam-Scanning Leakage Antenna Design.

This article proposes a novel fixed-frequency beam scanning leakage antenna based on a liquid crystal metamaterial (LCM) and adopting a metal column embedded microstrip line (MCML) transmission structure. Based on the microstrip line (ML) transmission structure, it was observed that by adding two rows of metal columns in the dielectric substrate, electromagnetic waves can be more effectively transmitted to reduce dissipation, and attenuation loss can be lowered to improve energy radiation efficiency. This antenna couples TEM mode electromagnetic waves into free space by periodically arranging 72 complementary split ring resonators (CSRRs). The LC layer is encapsulated in the transmission medium between the ML and the metal grounding plate. The simulation results show that the antenna can achieve a 106° continuous beam turning from reverse -52° to forward 54° at a frequency of 38 GHz with the holographic principle. In practical applications, beam scanning is achieved by applying a DC bias voltage to the LC layer to adjust the LC dielectric constant. We designed a sector-blocking bias feeder structure to minimize the impact of RF signals on the DC source and avoid the effect of DC bias on antenna radiation. Further comparative experiments revealed that the bias feeder can significantly diminish the influence between the two sources, thereby reducing the impact of bias voltage introduced by LC layer feeding on antenna performance. Compared with existing approaches, the antenna array simultaneously combines the advantages of high frequency band, high gain, wide beam scanning range, and low loss.

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression.

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model's ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.

A Ka-band one-dimensional beam scanning leaky-wave antenna based on liquid crystal.

Fixed frequency beam-scanning leaky-wave antennas have been a focus of attention for many scholars in recent years, and numerous related results have been obtained. However, these antennas suffer from several issues such as small beam-scanning range, low gain, and unsatisfactory impedance matching. To address these problems, this paper proposes a microstrip line (ML) antenna unit based on liquid crystal (LC) materials etched Complementary Split Ring Resonator (CSRR). In a first-of-its-kind approach, the substrate integrated waveguide (SIW) structure and the ML transmission structure are combined to present the SIW-ML transmission structure. The antenna operates in the Ka-band with excellent resonance characteristics at 34.7 GHz, and the S11 parameters are below - 13 dB in the frequency range of 30-40 GHz, indicating outstanding impedance matching. By arranging 56 antenna units, a periodic leaky-wave antenna is created, enabling fixed-frequency beam-scanning at 34.7 GHz. Experimental results show that the antenna can achieve scanning of angles between - 53° and + 60° with a gain of up to 12.63 dB. Once single-beam scanning is achieved, a method combining LC and discrete amplitude weighting technique, as well as multi-beam theory, is proposed for multi-beam study. Experimental results reveal that the designed 56-unit beam-scanning antenna can effectively realize beam scanning in two directions.

Joint Multidimensional Pattern for Spectrum Prediction Using GNN.

In general, judging the use/idle state of the wireless spectrum is the foundation for cognitive radio users (secondary users, SUs) to access limited spectrum resources efficiently. Rich information can be mined by the inherent correlation of electromagnetic spectrum data from SUs in time, frequency, space, and other dimensions. Therefore, how to efficiently use the spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency, and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results showed that our model achieves better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirms the feasibility of the model.

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio.

Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time-frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN.

Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction.

A Joint Automatic Modulation Classification Scheme in Spatial Cognitive Communication.

Automatic modulation discrimination (AMC) is one of the critical technologies in spatial cognitive communication systems. Building a high-performance AMC model in intelligent receivers can help to realize adaptive signal synchronization and demodulation. However, tackling the intra-class diversity problem is challenging to AMC based on deep learning (DL), as 16QAM and 64QAM are not easily distinguished by DL networks. In order to overcome the problem, this paper proposes a joint AMC model that combines DL and expert features. In this model, the former builds a neural network that can extract the time series and phase features of in-phase and quadrature component (IQ) samples, which improves the feature extraction capability of the network in similar models; the latter achieves accurate classification of QAM signals by constructing effective feature parameters. Experimental results demonstrate that our proposed joint AMC model performs better than the benchmark networks. The classification accuracy is increased by 11.5% at a 10 dB signal-to-noise ratio (SNR). At the same time, it also improves the discrimination of QAM signals.

ResNet-AE for Radar Signal Anomaly Detection.

Radar signal anomaly detection is an effective method to detect potential threat targets. Given the low Accuracy of the traditional AE model and the complex network of GAN, an anomaly detection method based on ResNet-AE is proposed. In this method, CNN is used to extract features and learn the potential distribution law of data. LSTM is used to discover the time dependence of data. ResNet is used to alleviate the problem of gradient loss and improve the efficiency of the deep network. Firstly, the signal subsequence is extracted according to the pulse's rising edge and falling edge. Then, the normal radar signal data are used for model training, and the mean square error distance is used to calculate the error between the reconstructed data and the original data. Finally, the adaptive threshold is used to determine the anomaly. Experimental results show that the recognition Accuracy of this method can reach more than 85%. Compared with AE, CNN-AE, LSTM-AE, LSTM-GAN, LSTM-based VAE-GAN, and other models, Accuracy is increased by more than 4%, and it is improved in Precision, Recall, F1-score, and AUC. Moreover, the model has a simple structure, strong stability, and certain universality. It has good performance under different SNRs.

Texture and digestion properties of hybrid rice: A comparison between two cultivars released 18 years apart.

Field experiments were conducted to compare two hybrid rice cultivars-a recently released high-quality cultivar (Jingliangyou 1468, JLY1468) and a relatively older cultivar (Liangyoupeijiu, LYPJ). Results showed that hardness, springiness, cohesiveness, resilience, and chewiness of cooked milled rice were all lower in JLY1468 than in LYPJ, due to its lower amylose content and altered paste properties of milled rice flour. Active digestion duration of cooked milled rice was 26% shorter and the glucose production rate from starch digestion was 33% faster in JLY1468 compared with LYPJ. Texture and starch digestion properties of cooked milled rice as a factor of temperature during the grain-filling period were different between LYPJ and JLY1468 due to differing amylose contents and gel consistencies of milled rice flour in response to temperature. This study highlights that attention should be paid to potential health risks associated with the development of high-quality hybrid rice cultivars with soft texture.

Primary-tiller panicle number is critical to achieving high grain yields in machine-transplanted hybrid rice.

The development of machine-transplanted hybrid rice is a feasible approach to meet the needs of both high grain yield and high labor efficiency in China, but limited information is available on the critical plant traits associated with high grain yields in machine-transplanted hybrid rice. This study was carried out to identify which type of culms (i.e., main stems and primary and secondary tillers) and which yield components of this culm are critical to achieving high grain yields in machine-transplanted hybrid rice. Field experiments were conducted with two hybrid rice cultivars grown under two densities of machine transplanting in two years. Results showed that total grain yield of main stems and primary and secondary tillers was not significantly affected by cultivar but was significantly affected by density and year. Averaged across cultivars, densities, and years, main stems and primary and secondary tillers contributed about 15%, 50%, and 35% to total grain yield, respectively. Total grain yield was not significantly related to grain yields of main stems and secondary tillers but was positively and significantly related to grain yield of primary tillers. Approximately 85% of the variation in total grain yield was explained by grain yield of primary tillers, which was positively and significantly related to primary-tiller panicles per m2 but not to spikelets per panicle, spikelet filling percentage, or grain weight of primary tillers. Based on these results, it is concluded that primary-tiller panicle number is essential for achieving high grain yields in machine-transplanted hybrid rice.