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.

A design methodology is proposed to optimize the minimum weight and error coefficient of polarization-adjusted convolutional (PAC) codes, enhancing their maximum likelihood (ML) performance based on a theoretical analysis of code asymptotic behavior. Employing an adapted multilevel list search algorithm to identify minimum-weight codewords, the methodology comprises three deterministic optimization algorithms. First, an iterative rate-profiling optimization algorithm substantially reduces the number of minimum-weight codewords through efficient pairwise exchanges of information and frozen indices. Second, a tree search optimization algorithm progressively extends the convolutional impulse response, exploring superior solutions within a theoretically constrained search space. Third, a joint optimization algorithm synthesizes the two algorithms, alternately refining the rate-profiling and convolutional pre-transform. Complexity analysis underscores the computational efficiency of these algorithms for short PAC codes, while optimization results confirm the strong capability of the proposed methodology in improving the minimum weight and error coefficient. With moderate-to-large list decoding for code lengths of 64 to 256, the proposed PAC codes consistently outperform state-of-the-art polar code variants, attaining or approaching the random coding union (RCU) bound. Additionally, the proposed PAC codes demonstrate the capability to exceed the normal approximation (NA) bound at low-to-moderate code rates.

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 paper proposes an asynchronization-aided iterative detection and decoding (AIDD) scheme for sparse code multiple access (SCMA) in satellite-terrestrial communication scenario, where the messages between the detector and decoder are iteratively exchanged with an additional degrees- of-freedom (DoF) in terms of delay. We first propose a parallel expectation propagation algorithm (P-EPA) for asynchronous multiuser detection, where a new initialization method is introduced by efficiently utilizing the prior information to enhance the performance of the detector. Furthermore, a universal soft-output decoder (S-OLD) is proposed based on the ordered likelihood decoder (OLD), which can generate the soft information with high reliability and serve as the input of P-EPA in the proposed AIDD. The iteration between the P-EPA and S-OLD is terminated when the maximum iteration number of the outer loop is achieved or the decoding results of all the users are converged. Simulation results show that the proposed P-EPA has better performance and lower latency compared to its counterparts, and the proposed AIDD also has better performance and fewer iterations than the synchronous IDD and joint detection and decoding (JDD) schemes.

To assist the design and optimization of low-density parity-check (LDPC) codes via density evolution (DE) on binary input additive white Gaussian noise (BIAWGN) channels, we propose a novel mixed Gamma approximation (MGA) scheme to obtain more accurate distribution of messages updated and output by the check nodes during DE iterations. Firstly, we highlight the inaccuracy of existing Gaussian approximation (GA) methods in approximating the distribution of check node output messages, especially when the messages from variable nodes are small with high probability (i.e. low signal-to-noise ratio), and the check nodes have a large degree, which leads to inexact results in GA methods. Then, we establish the MGA scheme by utilizing the statistical properties of Gamma distribution and combine it with GA, which outperforms the existing GA methods in the metrics of error of output mean and Kullback-Leibler (KL) divergence of output distribution for a wide range of parameters. Simulation and analysis validate that our MGA scheme has the potential for the design and optimization of LDPC codes, which can provide adequately accurate estimation of check node outputs with moderate complexity for a variety of approximation methods, such as Gaussian capacity approximation, and significantly reduce the computational complexity by sacrificing minor accuracy.

The pull-based transmission initiates the generation and updating of status to the destination as needed, potentially reducing unnecessary energy costs and maintaining data freshness for satellite-integrated Internet with limited resources. In this paper, we introduce a semantic-empowered metric called utility loss of information (UoI) for a multi-state Markov source to assess the freshness and value of information, and the synchronization of transceivers, which can simultaneously quantify the age of information (AoI), value of diversity states, and the mismatch of transceivers. Then, we propose a utility-critical prompt (UP) transmission scheme for terrestrial Internet of Things (IoT) sensors with multi-state Markov source to transmit status update to the satellite efficiently, and derive the average UoI (AUoI) in both periodic and stochastic queries. Simulation results demonstrate that the UP scheme can achieve an optimal tradeoff between freshness, value, and synchronization of transceivers in both periodic and stochastic queries, and outperforms than state-of-the-art schemes.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

This paper proposes a concatenated multi-belief ordered likelihood decoding (MB-OLD) algorithm for rate-compatible (RC) short low-density parity check (LDPC) codes, where the output log-likelihood ratios (LLRs) of belief propagation (BP) are sent to a well-designed bit-flipping decoder, which we called ordered likelihood decoder (OLD). In contrast to conventional ordered statistic decoder (OSD), the test error patterns (TEPs) sequence of OLD is generated from most likely to least likely, where the ordered reliability sequence associated with the most reliable basis (MRB) is approximated as multiple lines, and a stopping criterion (SC) is taken to reduce the decoding complexity. Furthermore, we analyze the LLR behavior of BP decoder in short block-length regimes, and propose an optimal iteration number. Based on these analyses, the output LLRs of BP within the optimal number of iterations are well combined and sent to OLD. Simulation results show that the proposed MB-OLD has the superior decoding performances in terms of error-rate and decoding complexity than its counterparts.

In the upcoming sixth-generation (6G) networks, semantic communication has made remarkable strides, where the transceivers utilizing local knowledge bases (KBs) to encode and recover semantic information. In this paper, we propose a universal weighted-KB (UW-KB) endowed with a sample confidence function for an end-to-end (E2E) task-unaware semantic communication system, where both the KB and semantic coding networks at the transceivers are incomplete in the initial stages. This intelligent UW-KB is shaped by receiver feedback during training, autonomously assigning weights to samples to mitigate biases in KB data, which significantly improves the efficiency of semantic coding networks. Simulation results demonstrate the effectiveness of our UW-KB in addressing KB data bias, providing valuable insights to bolster the robustness of task-unaware semantic communication systems.

In this paper, towards the ultra-reliable low-latency requirements of industrial Internet of Things (IIoT), we design a low decoding complexity ordered statistic decoder (OSD) for short analog fountain codes (S-AFCs). We first propose a concatenated decoder named soft-OSD (S-OSD) for S-AFCs, where the S-AFCs are concatenated with LDPC codes. Then, we analyze the log-likelihood ratio (LLR) output of inner decoder via the density evolution (DE), the DE results provide the theoretical guidelines to design the discarding criterion (DC) of test error patterns (TEPs) and stopping criterion (SC) to lower the complexity of S-OSD. Simulation results show that the S-OSD can achieve the same error performance with existing decoding algorithms for S-AFCs, and the complexity of S-OSD is greatly decreased, in terms of the average re-encoding number of OSD and operations number per information bit.

Rateless codes can adapt to the wireless channel conditions without accurate channel state information (CSI) at the transmitter side, avoiding CSI feedback and retransmission, and thus are a promising channel coding approach to meet the stringent requirements of ultrareliable low-latency communications (uRLLCs). This article investigates a self-adaptive ordered statistics decoder (S-OSD) scheme for finite block length nonbinary Raptor code (NBRC). Aiming at minimizing the decoding complexity, some strategies for our S-OSD scheme are designed, including the segmentation rules of most reliable basis, the generating and discarding rules of test error patterns, and the stop criteria, respectively. In addition, an upper bound of block error rates (BLERs) for the NBRC under OSD is derived, which can be used to estimate the number of NBRC symbols required to successfully decode the input information via the S-OSD. Simulation results show that the complexity of our S-OSD scheme is greatly reduced comparing to the existing OSD schemes, while achieving very low BLER in the short block length regime.

Short length channel coding and low complexity decoding is essential for 5G ultra-reliable low latency communications (uRLLC). In this paper, an efficient ordered statistics decoder (E-OSD) scheme is proposed for finite length non-binary Raptor code (NBRC) towards uRLLC. The segmentation and discarding rules of test error patterns, and the stop criteria are designed for the proposed E-OSD scheme to reduce the decoding complexity. A block error rate (BLER) upper bound of the NBRC under OSD is derived to estimate the number of NBRC symbols required for achieving the desired BLER performance. Simulation results show that the complexity of the proposed E-OSD scheme is greatly reduced compared to the existing OSD schemes, and it can achieve the BLER lower than 10−5in the finite length regime (<256 bits), satisfying the requirements of uRLLC.

Raptor codes can approach the capacity of era-sure channel without accurate channel state information at the transmitter side, which is viewed as a potential channel coding approach to meet stringent requirements of ultra-reliable low latency communications (uRLLC) (block error rate (BLER) ≤ 10-5, and end-to-end latency ≤1 ms). This letter investigates a modified ordered statistics decoder (mOSD) algorithm for finite length non-binary Raptor code towards uRLLC. The upper bound of BLER for the non-binary Raptor code under OSD is derived, which can estimate the required block length under certain reliability requirement. Simulation results show that the BLER can be lower than 10-5in the finite length regime (-5, which is reduced by up to 50% of the average decoding complexity than the conventional OSD.

In this paper, a theoretical framework for the design and evaluation of finite block-length analog fountain codes (AFC) towards ultra-reliable low latency communications (URLLC) is proposed. First, based on the achievable rate analysis and extrinsic information transfer (EXIT) analysis for AFC, we propose a weight adaptive (WA) AFC transmission scheme by introducing a limited feedback link, which can realize the lowest complexity AFC over a wide range SNRs. Further, by combining the conventional EXIT analysis and the dispersion perspective of mutual information, we propose a modified weight selection scheme for short block length WA-AFC (SWA-AFC) scheme. Simulation results show that our SWA-AFC scheme can achieve a superior performance than the existing AFC schemes, and approaching to the Polyansky-Poor-Verdu (PPV) bound.

Raptor code is the current standard of 4G long term evolution (LTE) evolved multimedia broadcast and multi-cast services (eMBMS), which is viewed as a potential approach in the design of ultra-reliable low latency communications (uRLLC) for 5G. This paper analyzes the performance of finite length non-binary (over finite field of order q, GF(q)) Raptor codes under ordered statistics decoder (OSD) towards uRLLC, where the non-binary Raptor code ensembles by a non-binary low density parity-check (LDPC) code as pre-code and a non-binary inner Luby transform (LT) code. Moreover, by investigating the property of code structure and decoding algorithm, an upper bound of decoding failure probability (DFP) of finite length non-binary Raptor code under OSD is derived. Simulation results validate the accuracy of our derived upper bound, and demonstrate that our non-binary Raptor codes can achieve 10â'5 DFP with block length 128 bits at SNR 3.6 dB.

In this paper, towards the fifth generation (5G) massive machine type communications (mMTC), a theoretical framework of the design and evaluation model for analog fountain codes (AFC) is proposed. Motivated by the capacity analysis of AFC, we propose a weight adaptive (WA) AFC transmission scheme by introducing a limit feedback link, which can realize the optimal AFC in theoretical. Simulation results reveal that our WA-AFC coding scheme can approach the Shannon capacity in a wide range of SNRs over AWGN channel.

Machine-to-Machine (M2M) communications are expected to support extremely harsh requirements on both latency and reliability, which is characterized by the ultra-reliable, low-latency coding (uRLLC) technology in physical layer. In this paper, motivated by the recent development on the finite-blocklength information theory, we propose an ultra-reliable short blocklength analog fountain code (AFC) for M2M communications. First, we use the extrinsic information transfer (EXIT) chart to analyze the AFC compressive sensing belief propagation (CS-BP) decoding algorithm, by tracking the mutual information of AFC CS-BP decoding process, which related to the channel dispersion for the short blocklength AFC. Then, based on the EXIT chart analysis, we propose a Weight-set optimization progressive edge-growth (WO-PEG) encoding algorithm for the short blocklength AFC. Simulation results show that the proposed WO-PEG AFC scheme can effectively improve block error rate (BLER) in the short blocklength regime.