Ye Wang

In this paper, we propose a rate-compatible partially-coupled staircase low density parity check (PS-LDPC) coding scheme for high speed inter-satellite communications. First, we introduce the encoding process and sliding window decoding (SWD) algorithm of PS-LDPC codes, and we investigate the error floor of component codes, which validate that the PS-LDPC codes with short block-length component code can maintain the reliability, and significantly reduce the decoding latency. Then, we analyze the density evolution (DE) of PS-LDPC codes based on the multi-edge type (MET)- LDPC framework under the Gaussian approximation, and derive its decoding thresholds of SWD. Further, we propose an optimized coupling pattern (OCP) encoding algorithm that achieves the optimal coupling patterns with the minimized threshold by introducing two-stage column permutations, and modify the message exchanges in SWD algorithm according to this encoding algorithm. Moreover, we design a new decoding algorithm, named cascaded SWD (C-SWD) algorithm, which reduces the error floor and enhances decoding performance by pre-decoding, reliability enhancement, and cascading belief propagation (BP) decoder or ordered likelihood decoder (OLD) due to the error floor. Simulation results demonstrate that our PS-LDPC coding scheme outperforms the existing rate-compatible spatially coupled LDPC (SC-LDPC) coding schemes in terms of bit error rate and complexity.

This letter proposes a wideband vertical transition for multilayer PCBs based on a fuzz button. The discontinuity in the vertical transition is analyzed, and an etched aperture is introduced in the ground plane beneath the connecting pad to compensate for the parasitic capacitance arising from the intentionally oversized pad. This technique extends the concept of chamfering from 2-D microstrip bends to a 3-D chamfer-like design in vertical interconnects. A three-stage transformer is further employed to enhance the impedance matching, enabling the wideband operation from DC to 35 GHz. A back-to-back measurement is conducted for verification, demonstrating a return loss below −10 dB from DC to 33.8 GHz and an insertion loss below 0.5 dB at 30 GHz. The proposed vertical transition offers a promising solution for high-density, removable interconnects in multilayer PCB applications.

This paper proposes an iterative detection and decoding (IDD) scheme for asynchronous sparse code multiple access (aSCMA), referred to as aIDD, in satellite-terrestrial uplink communication scenario with the low earth orbit (LEO) satellite equipped with uniform planar array (UPA) antenna. In detector design, we first develop the extended factor graph for aSCMA by considering the memory induced by asynchronous transmission, and an asynchronous message passing algorithm (A-MPA) is proposed. In A-MPA, the noise whitening on the sampled symbols is performed to mitigate the correlation among the noise samples due to the matched filtering, and the updating rules are then designed to achieve superior performance. Furthermore, we propose an asynchronous expectation propagation algorithm (A-EPA) by exploiting the diversity gains induced by UPA, where the means and variances of the transmitted SCMA codewords are updated with high reliability. Simulation results show that the proposed A-EPA can achieve the same performance as that of A-MPA but with lower complexity at a high number of receive antennas. In decoder design, a soft-output ordered likelihood decoder (S-OLD) is proposed to generate the soft information with high reliability compared with the belief propagation (BP) decoder under low-density parity check (LDPC) code. By combining the proposed A-EPA/A-MPA and S-OLD, the proposed aIDD scheme iteratively exchanges the messages between the detector and the decoder until the maximum number of iterations of the outer loop is achieved or the decoding results of all the users are converged. Simulation results show that the proposed aIDD/A-EPA and aIDD/A-MPA have the same performance and are better than that of the synchronous IDD and joint detection and decoding (JDD) schemes.

Remote sensing data contains vast amounts of geographic structural information and fine-grained textural details, making compression essential and necessitating efficient processing for downstream interpretation tasks. Generative models have shown great potential in learned image compression, offering high compression rates and exceptional generation quality. Among these, diffusion models stand out for their ability to produce high-quality reconstructions. However, they may hallucinate small structures and textures due to significant information loss at limited bitrates. Furthermore, compression based on the diffusion process suffers from long inference times caused by numerous denoising steps, which hinders practical deployment. We propose Spatio-Temporal Hierarchical Codebook Diffusion (STHCD), a training-free framework that dynamically allocates resources to align with this hierarchy. STHCD introduces three synergistic components: (1) Spatial Residual Transformation via parameter-free unshuffle operations to adapt spatial granularity; (2) Channel-wise Codebook Splitting for flexible bit allocation without memory explosion; and (3) a Time-Dependent Hierarchical Policy to orchestrate these operations across timesteps. Extensive experiments on nine datasets show that STHCD significantly outperforms state-of-the-art methods in perceptual metrics (LPIPS, DISTS, FID). At equivalent bitrates (∼0.12 bpp), STHCD achieves an 8∼12.5× speedup (80∼125 vs. 1000 steps) over standard temporal codebook diffusion while optimizing LPIPS by 29.5%. Furthermore, STHCD can achieve a remarkable 15-step diffusion at the same bitrate, with only a minor LPIPS drop, establishing a new paradigm for hierarchical remote sensing compression. Our project is available at https://sthcd.github.io.

In this letter, a dual-band, dual-beam shared-aperture antenna combining a variable inclination continuous transverse stub (VICTS) antenna and a patch antenna array (PAA) is proposed for simultaneous connections with two satellites. The VICTS generates a Ku-band scanning beam, whereas the PAA provides a K-band fixed-direction beam. By mounting the PAA on the surface of the radiating layer of the VICTS antenna, a shared-aperture configuration is achieved. The feeding network of the PAA is specially designed, and long slots are etched on both the substrate and the ground plane to effectively mitigate the interference. To verify the proposed design, a prototype was fabricated and measured, showing good agreement between the measured and simulated results.

Network coding (NC) has proven to offer significant benefits in long-distance and broadcast transmissions, enhancing both throughput and energy efficiency. Recent studies have incorporated partial packet recovery (PPR) into packet-level NC, using syndromes from coded packets to correct bit errors and thereby reduce completion delay. Motivated by recent breakthroughs in deep learning, this article introduces a novel neural network-based decoding framework for packet-level NC, referred to as Learned-PPR. The proposed framework incorporates a bilateral efficient self-attention network (Bi-ESANet) architecture, which leverages a bilateral network structure to effectively capture both inter and intrapacket information. Furthermore, we introduce an ESA module to mitigate the GPU memory overhead compared with traditional Transformer attention modules. To handle rateless NC, we propose a “rateless masking” training strategy that enables efficient decoding of rateless codes within the Bi-ESANet framework. Simulation results across various transmission scenarios demonstrate that the proposed approach significantly outperforms existing PPR schemes, achieving lower completion delay. Specifically, compared to existing methods, the proposed approach reduces completion delay by more than 25%. However, the introduced framework incurs higher computational complexity due to the integration of the Bi-ESANet architecture.

Satellite-integrated Internet is capable of providing extensive coverage for massive terrestrial sensing user equipment (UE), facilitating access that satisfies the diverse requirements of multi-type services. However, existing proactive multiple access schemes often induce severe UE collisions, particularly hindering the performance of services with stringent requirements, and thus degrading overall system utility. In this paper, we investigate a utility-oriented satellite-queried system, where rate-splitting multiple access (RSMA) is adopted to support multi-type services with utility guarantees. The utility characteristic is captured by a semantic-empowered metric, termed utility loss of information (UoI), which comprehensively integrates timeliness, service priority, transceiver matching status, and energy consumption. To minimize the average UoI, we propose an adaptive RSMA (A-RSMA) scheme that dynamically adjusts the number of sub-data and power allocation according to the number of accessing UEs. To further improve UoI, we propose an adaptive grouped RSMA (Ag-RSMA) scheme, where the covered UEs are grouped according to their diverse utility requirements. We also introduce a reinforcement learning approach to optimize the dynamic resource scheduling. Simulation results demonstrate that our A-RSMA scheme achieves a lower UoI compared to the state-of-the-art schemes, and the Ag-RSMA scheme satisfies diverse UoI requirements than its non-grouped counterpart.