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.

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.

In this letter, we consider the construction of quasi-cyclic (QC) low-density parity-check (LDPC) codes by jointly optimizing the girth g and the number of g-cycles. We first propose a sub-array detection method to accelerate the detection of cycles, and by detecting the non-repeated automatic enumerative cycles in the frame of sub-arrays, cycle detection time can be greatly reduced. Furthermore, a local optimal searching algorithm combined with pre-masking is presented, by orderly searching the local optimal p j,l for each circulant permutation matrix (CPM), a class of QC-LDPC codes with possibly enlarged g, less number of g-cycles, and arbitrary attainable degree distribution could be constructed. Simulation results show that the codes constructed by the proposed method perform well in error floor region.

Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial–temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP’s trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.