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19 pages, 1304 KiB

Open AccessArticle

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

by Xiangqiang Gao, Yingzhao Shao, Yuanle Wang, Hangyu Zhang and Yang Liu

Electronics 2024, 13(7), 1216; https://doi.org/10.3390/electronics13071216 - 26 Mar 2024

Viewed by 518

Abstract

Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided [...] Read more.

Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided for users by satellite networks, there will be high capital expenditures in terms of communication delay and bandwidth usage. To this end, in this paper, a cooperative-caching and resource-allocation problem is investigated in integrated satellite–terrestrial networks. Popular contents, which are cached on satellites and ground data centers, can be accessed via inter-satellite and satellite–terrestrial networks in a cooperative way. The optimization problem is formulated to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. A cooperative caching and resource allocation (CCRA) algorithm based on a neighborhood search is proposed to address the problem. The simulation results demonstrate that the proposed CCRA algorithm outperforms Greedy and BFS in reducing the deployment costs. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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22 pages, 15502 KiB

Open AccessArticle

TOR-GAN: A Transformer-Based OFDM Signals Reconstruction GAN

by Yuhai Li, Youchen Fan, Shunhu Hou, Zhaojing Xu, Hongyan Wang and Shengliang Fang

Electronics 2024, 13(4), 750; https://doi.org/10.3390/electronics13040750 - 13 Feb 2024

Viewed by 728

Abstract

Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences [...] Read more.

Reconstruction techniques for communication signals represent a significant research focus within the field of signal processing. To overcome the difficulty and low precision in reconstructing OFDM signals, we introduce a signal reconstruction technique called TOR-GAN (Transformer-Based OFDM Signal Reconstruction GAN). Reconstructing IQ sequences using a CNN and RNN presents challenges in capturing the correlations between two signals. To tackle this issue, the VIT (vision in transformer) approach was introduced into the discriminator network. The IQ signal is treated as a single-channel, two-dimensional image, divided into blocks of 2 × 2 pixels, with absolute position embedding added. The generator network maps the input noise to the same dimension as the IQ signal dimension × embedding vector dimension and adds two identical position embedding data points to the network learning. In the transformer network, prob sparse attention is employed as a replacement for multi-head attention to tackle the issue of high computational complexity. Finally, combined with the MLP structure, the transformer-based generator and discriminator are designed. The signal similarity evaluation index was constructed, and experiments showed that the reconstructed signal under QPSK and BPSK modulation had good reconstruction quality in the time-domain waveform, constellation diagram, and spectrogram at a high SNR. Compared with other reconstruction algorithms, the proposed algorithm improved the quality of the reconstructed signal while reducing the complexity of the algorithm. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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14 pages, 5487 KiB

Open AccessArticle

A Novel Complex-Valued Hybrid Neural Network for Automatic Modulation Classification

by Zhaojing Xu, Shunhu Hou, Shengliang Fang, Huachao Hu and Zhao Ma

Electronics 2023, 12(20), 4380; https://doi.org/10.3390/electronics12204380 - 23 Oct 2023

Viewed by 975

Abstract

Currently, dealing directly with in-phase and quadrature time series data using the deep learning method is widely used in signal modulation classification. However, there is a relative lack of methods that consider the complex properties of signals. Therefore, to make full use of [...] Read more.

Currently, dealing directly with in-phase and quadrature time series data using the deep learning method is widely used in signal modulation classification. However, there is a relative lack of methods that consider the complex properties of signals. Therefore, to make full use of the inherent relationship between in-phase and quadrature time series data, a complex-valued hybrid neural network (CV-PET-CSGDNN) based on the existing PET-CGDNN network is proposed in this paper, which consists of phase parameter estimation, parameter transformation, and complex-valued signal feature extraction layers. The complex-valued signal feature extraction layers are composed of complex-valued convolutional neural networks (CNN), complex-valued gate recurrent units (GRU), squeeze-and-excite (SE) blocks, and complex-valued dense neural networks (DNN). The proposed network can improve the extraction of the intrinsic relationship between in-phase and quadrature time series data with low capacity and then improve the accuracy of modulation classification. Experiments are carried out on RML2016.10a and RML2018.01a. The results show that, compared with ResNet, CLDNN, MCLDNN, PET-CGDNN, and CV-ResNet models, our proposed complex-valued neural network (CVNN) achieves the highest average accuracy of 61.50% and 62.92% for automatic modulation classification, respectively. In addition, the proposed CV-PET-CSGDNN has a significant improvement in the misjudgment situation between 64QAM, 128QAM, and 256QAM compared with PET-CGDNN on RML2018.01a. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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18 pages, 711 KiB

Open AccessArticle

A Low-Cost High-Throughput C-Band Communication System for CubeSats

by Rafał Krenz, Paweł Sroka, Michał Sybis, Ilia Zainutdinov and Krzysztof Wesołowski

Electronics 2023, 12(20), 4374; https://doi.org/10.3390/electronics12204374 - 23 Oct 2023

Viewed by 1042

Abstract

This paper presents the physical layer of a proprietary broadband communication system for CubeSats. The system operates in the C band (5.8 GHz), delivering at least 10 Mbps of the net user throughput. Operation at low elevation angles (and therefore low SNRs) and [...] Read more.

This paper presents the physical layer of a proprietary broadband communication system for CubeSats. The system operates in the C band (5.8 GHz), delivering at least 10 Mbps of the net user throughput. Operation at low elevation angles (and therefore low SNRs) and high Doppler shifts is made possible thanks to a sophisticated synchronization subsystem. The system can be adapted to propagation conditions experienced during a given visibility window by changing the signal bandwidth and coding rate. It is implemented using Software Defined Radio (SDR) technology. The system will be used in two missions that are scheduled for 2023 and 2024 and are planned in cooperation with the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” and SatRev S.A. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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20 pages, 698 KiB

Open AccessArticle

Active STARS-Assisted Rate-Splitting Multiple-Access Networks

by Jin Xie, Xinwei Yue, Zhihao Han, Xuliang Liu and Wei Xiang

Electronics 2023, 12(18), 3815; https://doi.org/10.3390/electronics12183815 - 9 Sep 2023

Viewed by 762

Abstract

The active simultaneously transmitting/reflecting surface (ASTARS) is considered a promising technique to achieve full spatial coverage and overcome multiplicative fading caused by cascaded paths. This paper investigates the performance of ASTARS-assisted rate-splitting multiple-access networks (ASTARS-RSMA) with multiple transmission users (TUs) and reflection users [...] Read more.

The active simultaneously transmitting/reflecting surface (ASTARS) is considered a promising technique to achieve full spatial coverage and overcome multiplicative fading caused by cascaded paths. This paper investigates the performance of ASTARS-assisted rate-splitting multiple-access networks (ASTARS-RSMA) with multiple transmission users (TUs) and reflection users (RUs). The energy-splitting configurations of ASTARS and the effects of imperfect/perfect successive interference cancellation (SIC) on ASTARS-RSMA networks are considered in the analysis. We derive new exact and asymptotic expressions of the outage probability with imperfect/perfect SIC for TUs and RUs. On this basis, we further calculate the diversity orders of TUs and RUs. Moreover, the system throughput and energy efficiency (EE) of ASTARS-RSMA are evaluated in the delay-limited mode. The simulation results confirm the accuracy of the theoretical expressions and show that (i) the outage probability and system throughput with imperfect/perfect SIC of ASTARS-RSMA exceed that of passive simultaneously transmitting/reflecting surface (PSTARS)-assisted RSMA when the number of elements is not too large; (ii) although ASTARS increases power consumption compared to PSTARS, it can bring further EE improvements to RSMA networks. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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16 pages, 968 KiB

Open AccessArticle

Enhancing Physical Layer Security of Cooperative Nonorthogonal Multiple Access Networks via Artificial Noise

by Dan Jiang, Yuanyuan Gao, Guoxin Li, Nan Sha, Xu Bian and Xiaoyu Wang

Electronics 2023, 12(10), 2224; https://doi.org/10.3390/electronics12102224 - 13 May 2023

Cited by 2 | Viewed by 1134

Abstract

The massive connectivity requirement and security issues have become major factors restricting the further development of the Internet of Things. Nonorthogonal multiple access (NOMA) can be combined with physical layer security (PLS) to achieve massive connectivity and secure transmission. This article investigates the [...] Read more.

The massive connectivity requirement and security issues have become major factors restricting the further development of the Internet of Things. Nonorthogonal multiple access (NOMA) can be combined with physical layer security (PLS) to achieve massive connectivity and secure transmission. This article investigates the PLS performance for a downlink communication system over Nakagami-m fading channels, with full-duplex (FD) cooperative NOMA transmission aided by artificial noise (AN). While direct communication is built between the base station and two NOMA users, the strong user is employed as an FD relay as well as the jammer to enhance the PLS of the legitimate transmission in the presence of a passive eavesdropper (Eve). Closed-form analytical expressions in terms of the outage probability of the legitimate users and the intercept probability of Eve are derived to evaluate the security–reliability trade-off (SRT) of the proposed scheme. Monte Carlo simulations are provided to validate the veracity of the theoretic analyses and illustrate that the proposed scheme is superior in terms of SRT to the benchmark schemes in the low SNR region. Furthermore, the results reveal that the SRT performance of the two NOMA users can be enhanced through a proper AN-bearing ratio and power allocation optimization. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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20 pages, 8688 KiB

Open AccessArticle

Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network

by Shunhu Hou, Youchen Fan, Bing Han, Yuhai Li and Shengliang Fang

Electronics 2023, 12(2), 422; https://doi.org/10.3390/electronics12020422 - 13 Jan 2023

Cited by 3 | Viewed by 2542

Abstract

Automatic modulation recognition (AMR) plays an essential role in modern communication systems. In recent years, various modulation recognition algorithms based on deep learning have been emerging, but the problem of low recognition accuracy has not been solved well. To solve this problem, based [...] Read more.

Automatic modulation recognition (AMR) plays an essential role in modern communication systems. In recent years, various modulation recognition algorithms based on deep learning have been emerging, but the problem of low recognition accuracy has not been solved well. To solve this problem, based on the existing MCLDNN algorithm, in this paper, we proposed an improved spatiotemporal multi-channel network (IQ-related features Multi-channel Convolutional Bi-LSTM with Gaussian noise, IQGMCL). Firstly, dividing the input IQ signals into three channels, time sequence feature extraction is carried out for route I, route Q, and route IQ, respectively. For route IQ, convolution kernel (2,1) is first used to extract relevant features. Two layers of the small convolution kernel (1,3) are used to extract time sequence features further, and the three channels are used to extract features further. Then, a two-layer short-length memory network is used to extract features from time and space more effectively. Through comparison experiments, Bi-LSTM is introduced to replace one layer of LSTM, and a fully connected layer is removed to prevent overfitting. Finally, multiplicative Gaussian noise is introduced to naturally corrode the feature parameters, further improving the robustness and accuracy of the model. Experiments are carried out on three public datasets RML2016.10a, RML2016.10b, and RML2016.04C. The experiments show that the IQGMCL network has higher recognition accuracies on all datasets, especially on the RML2016.10a dataset. When the SNR is 4 dB, the recognition accuracy reaches 93.52%. When the SNR is greater than 0 dB, the average recognition accuracy reaches 92.3%, 1.31%, and 1.2% higher than the original MCLDNN network, respectively. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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Review

Jump to: Research

31 pages, 10478 KiB

Open AccessReview

Optimization Design for Sparse Planar Array in Satellite Communications

by Yuanzhi He and Changxu Wang

Electronics 2023, 12(8), 1763; https://doi.org/10.3390/electronics12081763 - 7 Apr 2023

Cited by 3 | Viewed by 2406

Abstract

The antenna is one of the key components of satellite communication load. To address the evolving requirements of future satellite communication systems, the sparse planar array has become an important device for transmitting and receiving electromagnetic waves in emerging antenna systems. The advantages [...] Read more.

The antenna is one of the key components of satellite communication load. To address the evolving requirements of future satellite communication systems, the sparse planar array has become an important device for transmitting and receiving electromagnetic waves in emerging antenna systems. The advantages of this technology include low cost, low system complexity, and robust anti-interference ability, which have attracted widespread attention within the industry. In this paper, we investigate an optimization design of sparse planar arrays in satellite communication scenarios. Firstly, we introduce the mathematical foundation of the array antennas and establish the optimization design model of the sparse planar array. Secondly, we analyze and compare the impact of different array layout methods on the sparse planar array antenna pattern, and then introduce the latest design trend of array material design. Thirdly, we review some classical optimization methods for optimizing sparse planar arrays and the recent research advancements in promising and novel methods. Lastly, on the basis of the present research status, we propose three future research directions and two critical challenges for optimal design of sparse planar arrays in satellite communication scenarios, which can facilitate the development and realization of array technology under future B5G and 6G wireless networks. Full article

(This article belongs to the Special Issue Recent Advances and Challenges of Satellite and Aerial Communication Networks)

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