Physical Communication最新文献

Due to the fact that the BeiDou navigation satellite system (BDS) signal is easily blocked when the unmanned air vehicle (UAV) shuttles between urban buildings, the positioning accuracy is limited or the positioning cannot be completed. Therefore, the 5th generation mobile communication technology (5G) positioning is introduced to establish a hybrid positioning system of BDS pseudo-range combined with 5G time of arrival (TOA) and angle of arrival (AOA). The noise distribution of the observation data has strong randomness, which leads to the contamination of the update of the prior covariance matrix in the Kalman filter (KF) prediction step, and affects the optimal estimation of the UAV position. Therefore, an adaptive variational Bayesian (VB) localization algorithm is proposed. The algorithm first uses the least squares (LS) solution of the positioning observation as the observation input of the KF, and judges the distribution type of the original observation noise according to the Grubbs criterion. Then, the VB update factor of the covariance matrix is adaptively adjusted according to the Gaussian or heavy-tailed non-Gaussian noise distribution to optimize the position estimation. The simulation results show that the proposed algorithm can achieve high-precision positioning and anti-interference performance under different states of UAV, different degrees of satellite occlusion, and different probability of random interference.

The ever-growing demand for increased data rates, reduced latency, and more reliable connectivity has driven the emergence of the fifth-generation (5G) wireless communication network, necessitating a significant shift in our approach to channel modeling. To achieve these ambitious goals, channel models must adopt various key enabling technologies, such as massive multiple input multiple outputs (MIMO), beamforming, and mobile edge computing, for various scenario-based applications, and adhere to developed channel standards. Our work comprehensively reviews various wireless channel models, emphasizing their applications and challenges. A concise overview of channel models for 5G and beyond provides important information about various channel modeling approaches, their standards, and protocols that are significant to their development for diverse applications in real-world scenarios. A complete list of standard channel models used in the industry, such as the third-generation partnership project, METIS, QuaDRiGa, and mmMAGIC, will help researchers and application developers understand the needs of different fields to achieve their Key Performance Indicators (KPIs). The paper also highlights important features of each channel model with a comparison of important channel characteristics and identified channel modeling issues reported in the current literature. This paper also explores the connections between channel models and other revolutionary (cutting-edge) technologies, including the use of soft computing tools (machine learning), data handling tools (cloud computing and big data analytics), and massive MIMO for use-case realization. The paper concludes that there is a need for further advancements in channel modeling to meet the requirements of the next generation by effectively addressing the challenges of the current generation. Extreme scenario channel models such as aeronautics, UAVs, deep space exploration, and massive MIMO channels require the inclusion of advanced machine learning techniques for improved performance.

This work proposes a heterogeneous automated frequency coordination network (HAFCN) to enhance reliability and enable dynamic spectrum allocation for unlicensed Wi-Fi devices (UWDs) within the 6 GHz band. This process relies heavily on spectrum sensing within the HAFCN. However, the widespread implementation of spectrum sensing by multiple UWDs in an automated frequency coordination (AFC) network using conventional fusion schemes poses computational challenges at the AFC provider. In response to this challenge, we present a selective soft-information (SSI) fusion scheme for the proposed HAFCN. First, we present generic mathematical expressions of false-alarm and missed detection probabilities for the HAFCN using an SSI fusion scheme. Second, a generalized AFC SSI fusion problem (GASFP) is formulated to minimize the system’s total error probability. Further, to mitigate the AFC provider’s overhead in solving the GASFP, this work presents the swift-sensing problem, determining the minimum antennas at UWD required to achieve a desired total error probability. Finally, comparative numerical results demonstrate that the HAFCN with the SSI fusion scheme shows a significant performance improvement over conventional fusion schemes in terms of the total error probability.

Identification of intra-pulse modulation (IPM) of radar signals is a crucial part of contemporary electronic support systems and electronic intelligence reconnaissance. Artificial intelligence (AI)-based methods can be very effective in recognising the IPM of radar signals. In this direction, an automatic method is proposed for recognising a few IPMs of radar signals based on continuous wavelet transform (CWT) and a hybrid model of self-attention (SA)-aided convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Firstly, time–frequency attributes of different radar signals are obtained using CWT, and thereafter CNN-SA-BiLSTM is utilised for feature extraction from the 2D scalograms formed by the time–frequency components. The CNN extracts features from the scalograms, SA enhances the discriminative power of the feature map, and BiLSTM detects radar signals based on these features. Additionally, the study addresses real-world data imbalance issues by incorporating a generative AI model, namely the Variational Autoencoder (VAE). The VAE-based approach effectively mitigates challenges arising from data imbalance situations. This method is tested at varying noise levels to give a proper representation of the actual electronic warfare environment. The simulation results demonstrate that the best overall recognition accuracy of the proposed method is 98.4%, even at low signal-to-noise ratios (SNR).