This study introduces a compact, wideband circularly polarized (CP) antenna that features back-lobe suppression, customized for global navigation satellite system (GNSS) applications. The antenna design incorporates four rotationally symmetric dielectric blocks utilizing molded interconnect device (MID) technology. Each block supports a T-branch antenna with a spiral element and a parasitic folded monopole antenna, covering the frequency spectra of 1164-1299 MHz and 1525-1614 MHz. The design integrates two mechanisms for back-lobe suppression: the employment of monopole and dipole modes, and the inclusion of a microstrip resonator within a multilayer substrate. Performance assessments reveal that the boresight right-hand CP gain and front-to-back ratio (FTBR) range from -4.2 to 1.4 dBic and 2.1 to 12.9 dB, respectively, for the 1164 to 1299 MHz band, and 2.4 to 3.5 dBic and 8.6 to 17.8 dB, respectively, for the 1525 to 1614 MHz band. Both simulated and measured results confirm the antenna’s robust and consistent performance across its operational bandwidth, highlighting its suitability as a compact (53.5 mm × 53.5 mm × 29.6 mm) solution for GNSS applications.

Intelligent reflecting surface (IRS) technology is emerging as a pivotal solution for enhancing coverage and performance in sixth-generation wireless networks. In this study, we explore a downlink IRS-assisted non-orthogonal multiple access (NOMA) scheme that incorporates cooperative full-duplex relaying (CFR-NOMA) to improve coverage for cell-edge users. Specifically, both the IRS and user-relaying are employed to help the base station send signals to the cell-edge user over Nakagami-m fading channels. To assess the reliability of the considered IRS-assisted CFR-NOMA scheme, we first derive a closed-form expression for its outage probability. Subsequently, we conduct an asymptotic analysis on the outage probability, and it reveals an error floor for the cell-edge user at a sufficiently high BS transmit power. Our findings indicate that increasing the number of IRS reflecting elements can significantly reduce the outage probability and enhance the network coverage. Simulation results validate our analysis and reveal the superiority of our considered scheme over the conventional orthogonal multiple access (OMA) schemes for cell-edge users.

With the recent increase in mobile devices and Internet of Things (IoT), ensuring security against eavesdropping has become crucial. We focus on physical-layer security and propose chaos-coded modulation (CCM), which encrypts modulation in the physical layer. CCM is a type of symmetric key encryption method that achieves confidentiality against eavesdroppers by convolving multiple bits and the shared key during modulation, while enabling legitimate receivers to obtain coding gain. However, owing to the encryption in the CCM, in principle, no correlation exists between the Hamming distance of the bit sequence and the Euclidean distance of the modulated symbol sequence. Therefore, the bit labeling of CCM is imperfect when compared to the Gray labeling of linear modulation, and the log-likelihood ratio obtained at the receiver is degraded. Consequently, the CCM has significantly limited performance improvement when concatenated with external error correction codes (ECCs), despite its coding gain being uncoded. Additionally, performance degradation increases as the modulation rate increases. To solve this problem, we propose a CCM with a non-binary ECC that matches the code length to the CCM instead of a conventional binary ECC, thereby addressing the aforementioned demodulation problem and improving error-rate performance. Numerical results show that the CCM with non-binary low-density parity-check (NB-LDPC) codes with a code length of 1024 and a coding rate of 0.5 achieves a gain of 1.5 dB at a bit error rate of 10^-4 compared to binary phase-shift keying and a gain of 2.9 dB compared to the CCM with binary LDPC codes under the same conditions. Furthermore, the NB-LDPC codes are more effective when the modulation rate is two or higher.

This paper proposes methods for estimating the carrier frequency offset (CFO) and channel response using synchronization signals for the physical broadcast channel (PBCH) in the 3GPP New Radio specifications. In the proposed methods, we estimate and compensate for the CFO of the received PBCH by employing the primary synchronization signal followed by estimation of the channel response using the demodulation reference signal (DMRS) and the secondary synchronization signal (SSS). Computer simulation results show that the proposed CFO estimation method achieves almost the identical average block error rate (BLER) for the PBCH payload as that without CFO. We also show that the channel estimation method using the DMRS and SSS decreases the required average received signal-to-noise ratio at the average BLER of 10^-3 by approximately 0.4 dB compared to that with only the DMRS. Finally, we show the effect of successive cancellation decoding and cyclic redundancy check-aided successive cancellation-list decoding on the BLER of the PBCH payload when employing a low-rate polar code.

A high-precision attitude control system is essential for ensuring the connectivity and stability of satellite laser communication systems, directly influencing the reliability of communication links and the consistency of data transmission. This system relies on precise rotational mechanisms that continuously adjust satellite orientation to maintain alignment with laser communication links. This paper presents an innovative anomaly detection framework that integrates Periodic Dilated Convolutional Networks (PDCN) and an Enhanced Efficient Channel Attention Network (EECA) to identify potential anomalies in these critical rotational mechanisms within the attitude control system. The proposed method capitalizes on the periodic characteristics of attitude control data by extracting key periodic components and combining them with periodic dilated convolution, thus enhancing the model’s ability to capture essential time-series features and improving its accuracy in detecting fluctuations that may impact communication stability. Additionally, the inclusion of EECA further amplifies the model’s focus on key anomaly signals, particularly those that could disrupt the connectivity and stability of laser communication links. This enhanced attention mechanism enables the model to highlight critical data features, significantly improving predictive accuracy and anomaly detection capabilities. Experimental results on real satellite telemetry datasets demonstrate that, compared to conventional neural network models, the proposed method excels in identifying subtle fault signatures and providing early warnings, achieving improvements of several percentage points in both prediction accuracy and anomaly detection rates. Furthermore, this approach has extensive applicability for detecting anomalies in other systems with periodic variation characteristics, providing robust support for maintaining the reliability of satellite communication systems.

With the concept of air-space-ground network integration proposed, satellite communication systems have rapidly developed as an essential carrier of space networks. Limited by the scarce radio frequency resources, spectrum coexistence between different satellite systems is essential, especially in the collinear scenario between LEO satellites and other systems. This study focuses on modeling and analyzing collinear scenarios, introducing a novel approach by transforming both inter-system and intra-system interference constraints into gain constraints for transmitting beams, and proposing an interference avoidance strategy based on transmitting beamforming. By suppressing interference through beam nulling, the isolation angle can be reduced, thereby expending the serviceable range of satellites. Simulation results validate the effectiveness of the proposed algorithm, showcasing a significant reduction in interference from LEO satellites on other systems. This strategy offers a feasible solution to the coexistence challenges faced by large-scale constellations.

We introduce a target recognition scheme based on multiple signal reflections assuming a non-line-of-sight (NLOS) scenario for a millimeter wave (MMW) radar used in automotive sensor applications. For a pedestrian or a cyclist jumping out from the rear of an obstacle, such as a parked vehicle, a target recognition scheme in a NLOS region is highly required for collision prevention, especially in driving support and autonomous driving systems. Since the diffraction signal obtained from a NLOS region is too weak for identifying a specific target type, particularly at high frequencies and under full NLOS conditions, we introduce a target recognition approach based on multiple reflections. Two machine learning approaches, based on the support vector machine (SVM) and long-short-term memory (LSTM) algorithms are introduced with only single transmitter and receiver data. The experiments conducted using a 24-GHz band MMW radar in an actual road scenario demonstrated that the proposed approach can accurately recognize a real human body from an artificial object by exploiting the unique characteristics of reflected signals.