Low Earth orbit (LEO) satellite communication (SATCOM) is a pivotal component of sixth-generation (6G) wireless communication. The dependability of satellite-to-ground communication is severely hampered by large-scale Doppler frequency offsets. We propose a carrier frequency offset (CFO) estimator applied to orthogonal time frequency space (OTFS) modulation. By inserting the pilot and maximum length sequences (MLS) for each OTFS frame in the delay-Doppler domain, the estimator can search for the corresponding equal-amplitude sequences and MLS along different dimensions in the delay-time domain. The fractional CFO, obtained by weighting the signal phase determined by the received sample’s correlation function, is the best linear unbiased estimate. The proposed estimator well enhances the estimation range by employing a correlation technique with MLS to estimate the large-scale CFO. Finally, we conduct numerous simulations on the time-varying channel. The results demonstrate a minimum of 53% reduction in frequency estimation error and a minimum 32-fold increase in estimation range compared with existing algorithms.

To further improve the transmission efficiency and link stability for free space optical (FSO)-based satellite communication (SatCom) systems when transmitting large-scale remote sensing images, a scheme based on the integration of FSO and semantic communication (FSO-SC) is proposed, which employs a vector quantized variational autoencoder with spatial normalization to extract essential semantic features of images while preserving intricate details. Additionally, the Máalaga distribution model is utilized to simulate FSO channels with diverse turbulence conditions. Moreover, a comparative evaluation between the FSO-SC and traditional systems is conducted through 28 GBaud satellite-ground simulation with three modulation formats considering various effects. Compared to the traditional systems, without incurring additional bits for error corrections, the FSO-SC system achieves a power gain of over 3 dB while enabling transmission at zenith angles over 60°. Moreover, it achieves performance on par with state-of-the-art 4-receiver spatial diversity technology, while offering superior hardware and transmission efficiency. Furthermore, we conduct 10 Gbps real-time satellite-ground equivalent experiments to validate the practicality of the FSO-SC, where it achieves a 60% reduction in communication overhead compared to existing solutions while maintaining comparable received image quality and can reach a minimum receiver sensitivity gain of 4 dB. Simulation and experimental results demonstrate that the proposed FSO-SC scheme achieves high system efficiency and stability, holding promise as a viable solution for future SatCom.

As an important technology for achieving 6G global access, low Earth orbit satellite (LEO-Sat) communication still encounters the challenges of scarce spectrum resources and the serious Doppler effect. To improve the performance of image transmission in the LEO-Sat system, this paper proposes an approach that combines semantic communication with orthogonal time-frequency space (OTFS) modulation to effectively mitigate the severe Doppler effect and enhance transmission efficiency. The NTN-TDL-D model in 3GPP is employed to simulate satellite channels and introduce noise into the training process of the semantic model. The performance of semantic-enhanced and traditional LEO-Sat systems is further evaluated under various conditions. Ultimately, simulation results demonstrate that the proposed solution achieves a minimum 6dB signal-to-noise ratio gain, reduces communication overhead by 50%, and ensures higher spectrum utilization efficiency in high-speed scenarios compared to existing solutions.

Accurate estimation of transmission parameters is indispensable in 5G satellite-to-ground communication systems. However, the tricky problem in multipath fading channels is to accurately estimate the average signal-to-noise ratio (SNR) within the operating frequency band. In this work, a novel SNR estimation method is proposed based on the channel frequency response (CFR) reconstruction. The noise variance is obtained based on the variance of CFR difference before and after the least squares discrete Fourier transform (LS-DFT) algorithm, while the signal power is calculated based on the power difference of CFR and the noise variance. In addition, a numerical simulation is performed to evaluate the effectiveness of the proposed algorithm in real satellite-to-ground communication scenarios. The analysis reveals that the proposed algorithm achieves the normalized mean square errors (MSE) of -13.32 dB observed under the ultra-low SNR condition of -15 dB for the non-terrestrial network (NTN) tapped delay line (TDL-B) channels. These results indicate a significant enhancement of 4 dB compared to the maximum likelihood (ML), Boumard’s and Zhang’s methods.