In the upcoming global-coverage 6G networks, high packet loss and long latency in long-distance transmissions exacerbate the trade-off between data timeliness and integrity, particularly in time-sensitive applications involving time-series data with stringent integrity requirements. This challenge exposes the limitations of existing transmission systems, such as source-channel coding and semantic communication, which fail to jointly address both dimensions. In this paper, we propose a deep learning (DL)-enabled generalized deduplication (GD)-based semantic transmission (DLGD-ST) mechanism for time-series data. By leveraging GD to address the impact of semantic ambiguity on data integrity, DLGD-ST exploits the semantic recovery and temporal discreteness of the data to effectively mitigate the conflict between integrity and timeliness. In particular, a well-designed long-short-term memory (LSTM)-based GD algorithm is developed to separate shallow semantic components and supplementary components, ensuring the integrity of semantic transmission. A deep semantic encoding process is then performed using a double-layer progressive dimension reduction (DPDR) and adaptive quantization (AQ) scheme, which capitalizes on the channel robustness of semantics to reduce transmission rounds and improve timeliness. Furthermore, an incremental dimension hybrid automatic repeat request (ID-HARQ) mechanism is introduced to improve semantic reliability by retransmitting high-dimensional semantics, thereby further minimizing end-to-end transmission rounds. To accurately evaluate performance, we introduce the Age of Semantic Information (AoSI), which incorporates integrity constraints into the generalized Age of Information (AoI) to jointly assess integrity and timeliness. Simulation results demonstrate that the proposed DLGD-ST mechanism, enabled by accurate data recovery and reduced transmission rounds, achieves better AoSI performance compared to existing communication systems under both high and low signal-to-noise ratio (SNR) conditions.

In the Satellite-integrated Internet of Things (SIoT), typical multi-access schemes, i.e., Contention Resolution Diversity Slotted ALOHA scheme (CRDSA), face with the obvious challenge of heavily conflicting packets regarding high channel traffic, which is not well addressed by the methods of Successive Interference Cancellation (SIC) due to the stubborn loop issues. In this work, therefore, we develop a Generalized Deduplication (GD) based Contention Resolution Diversity Slotted ALOHA scheme with a Compulsory Divorce mechanism (CD-CRDSA) by considering the correlative properties among the accessing data from the users. In particular, the proposed mechanism could effectively separate individual packets from conflicting slots to maintain the sustainability of SIC process, thus achieve better throughput. Moreover, we make the theoretical analysis on the throughput performance with the compression gain of the proposed mechanism. The simulation results indicate that compared to the typical CRDSA protocol and the Non-Orthogonal Multiple Access (NOMA) scheme, the proposed CDCRDSA significantly reduces the amounts of un-resolved slots and improves throughput performance, especially in high-load areas.

In long-span space communication, the current Licklider Transmission Protocol (LTP) confronts apparent challenges such as high packet loss rate and huge latency when carrying the bundles in the Disruption Tolerant Networks (DTN). These challenges incur obviously low freshness of satellite telemetry and instruction data with high timeliness requirements since the typical Automatic Repeat reQuest (ARQ) mechanism is exploited in the LTP for reliable transfer. To address this issue, in this paper, we propose an age-driven bundle delivery mechanism called as Semantic LTP (S-LTP) by considering the semantic correlations in the context-dependent data, which has excellent error-tolerant capability by a well-designed Semantic Supplement Hybrid Automatic Repeat reQuest (SS-HARQ), making it with high timeliness. In particular, a novel metric of semantic freshness of data called Age of Semantic Information (AoSI) is proposed to evaluate the timeliness contribution of information at the semantic level. The simulation results indicate that the proposed SS-HARQ scheme performs better in reducing the average AoSI and AoI by 62.24% and 64.52% respectively compared to the conventional LTP-ARQ with Cyclic Redundancy Check (CRC), 6.39% and 27.09% respectively compared to the Semantic Coding HARQ (SCHARQ) with a similarity detection network called Sim32.

In large-scale satellite networks, data delivery confronts obvious challenges such as high loss rate and long propagation delay leading to low Packet Delivery Ratio (PDR) and huge delivery latency over intermittent Inter-Satellite Links (ISLs), making the current routing algorithms exploiting typical Automatic Repeat reQuest (ARQ) mechanisms extremely inefficient and even incapable. To address this issue, in this paper, we propose a semantic-aware coding and routing joint mechanism called Semantic Adaptive Coding and Routing (SACR) by considering both the semantic correlations in the context-dependent data and the link status knowledge. In particular, the proposed SACR achieves excellent error-tolerant and routing-agile capabilities by an elaborately interactive design consisting of a customized routing-aware Semantic Adaptive Coding Hybrid ARQ (SAC-HARQ) mechanism and a Semantic Coding-based Routing Mechanism (SCRM). The simulation results indicate that the proposed SACR mechanism performs better in reducing the average delivery latency and improving the effective throughput compared with typical routing mechanisms such as Open Shortest Path First (OSPF) routing, Deep Q-Networks based Intelligent Routing (DQN-IR), and Real-Time Hop-by-hop routing (RTHop), integrating with typical semantic coding methods, i.e., Deep Learning-based Joint Channel-Source Coding (DL-JSCC), Deep learning-based Semantic Communication system (DeepSC), and Semantic Coding HARQ (SCHARQ), respectively.

Low orbit satellite constellation as a relay network provides a possible solution for remote real-time data gathering applications, in which freshness information updates will be forwarded via dynamical intersatellite links (ISLs). Modeling by a time-varying network, this article studies minimizing Age of Information (AoI) of delivering the data through a multihop path, in particular, focusing on the effect of frequent interruptions of ISLs. Subjected to two constraints of path and effective arrival rate, the minimizing AoI problem is formulated to find a pair of optimal transmission delay and arrival rate. In particular, the$\mathcal {H}$-approximate optimal algorithm, called a latest update routing (LUR) algorithm, is proposed with a well-designed continuous time-varying graph. Using LUR, a set of paths can be obtained with degraded transmission delay that satisfies a given arrival rate. By screening all the arrival rates satisfying the effective constraint, the maximum rate and a corresponding path set that minimizes age can be found. The simulation results verified that a degraded average AoI can be obtained by the proposed path selection mechanism compared with the typical shortest delay path (SDP) strategy, minimum spanning tree (MST) strategy, and MAoIG. In particular, the numerical findings show that the proposed LUR reduces average AoI by a maximum of 12.66% compared with SDP, 75.28% compared with MST, and 69.3% compared with MAoIG, respectively, under different scenarios.

In this article, we consider the data updating problem in the Satellite-integrated Internet of Things network for the time-critical scenarios, i.e., animal tracking and environmental monitoring. Due to the limited channel rate during contact of transmission, however, constantly updating data with huge volume over the uplink will incur obvious waiting and transmission delay bringing stale information to the satellite node. To address this issue, we propose a novel metric, spatially temporally correlative mutual information (STI), to characterize the information timeliness from perspective of information entropy by considering the correlations between the last update message and the status of the information source. By maximizing the averaged STI, we find the optimal allocation policy of channel slots with a fixed updating period by formulating the problem as a Markov decision process (MDP) with possibly infinite state space. Furthermore, we derive the optimal amounts of allocated time slots in a unit frame by solving a constrained range integer optimization problem with respect to the average STI. The simulation results show that the proposed periodically updating policy can significantly improve the information freshness compared with the original slot allocation strategy and current commonly used scheduled access strategies, i.e., slotted ALOHA and Threshold-ALOHA.

In a typical Satellite-integrated Internet of Things (SIoT), the limited transmission rate of a sensor causes a stale in data freshness due to unavoidable time waiting for transmission. Moreover, due to the high bit error rate (BER) of the satellite-to-ground link, data freshness will be further exacerbated by frequent retransmissions. Generally, this issue is partially solved using a powerful compression scheme that can reduce the data volume. However, conventional compression schemes will necessitate a significant amount of time to accumulate constant data to a certain quantity, posing a difficult challenge. Therefore, this study proposes an age-optimal hybrid temporal-spatial generalized deduplication and automatic repeat request (HARQ-GD) protocol for the high-sampling data collection in SIoT, considering data compression, and transmission collaboratively. A novel Age of Information (AoI) metric is developed for timeliness evaluation over a two-hop end-to-end link of SIoT, which is optimized to design the proposed HARQ-GD protocol by considering the temporal and spatial correlations of sampled data with specific encoding/decoding algorithms and packet formats. The simulation results indicate that the proposed HARQ-GD protocol performs better performance than typical generalized deduplication (GD) and hybrid automatic repeat request with chase combing (HARQ-CC) schemes in reducing AoI, because of its fewer transmission times and higher compression rate.

Multi-label text classification is generally more difficult for its exponential output label space and more flexible input document considering its corresponding number of labels. We observed that some long documents have only one or two labels while some short documents are related to much more labels. In this paper, we propose a dynamic Gated Neural Network architecture to simultaneously process the document through two parts, one to extract the most informative semantics and filter redundant information, the other to capture context semantics with most of the information in the document kept. And semantics from these two parts are dynamically controlled by a gate to perform subsequent classification. And to better the training we incorporate label dependencies into traditional binary cross-entropy loss by exploiting label co-occurrences. Experimental results on AAPD and RCV1-V2 datasets show that our proposed methods achieve state-of-art performance. Further analysis of experimental results demonstrate that the proposed methods not only capture enough feature information from both long and short documents assigned with various labels, but also exploit label dependency to regularize the proposed model to further improve its performance.