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.

Satellite-integrated Internet can provide multi-type intelligent services for ubiquitous user equipments (UEs) in the next-generation networks. Considering that most existing multicast systems in satellite-integrated Internet cannot accommodate the diverse requirements of heterogeneous services with limited resources, we propose a utility-optimal multi-type services multicast system based on multiple-input multiple-output rate-splitting multiple access (MIMO-RSMA). Specifically, we consider three types of semantic services classified based on their timeliness, reliability, and semantic characteristics. To support these heterogeneous services coexistences under constrained resources, we design three tiered priority scheduling (TPS) policies with progressively increasing inter-service resource coupling, and demonstrate their advantages under different operating conditions. Considering diverse demands of semantic services, we propose the utility loss of information (UoI) to capture the requirements of each service, and formulate a multi-constrained UoI-minimization problem within each transmission stage of the TPS policies, and transform it via Lyapunov framework with the proposed exponentially-weighted virtual queue (EWVQ). Further, we design a soft actor-critic (SAC)-based power allocation and rate control (PARC) scheme, and propose an adaptive weighted priority scheduling (AWPS) function to solve the non-convex UoI-minimization problem under differentiated reliability requirements. Simulation results validate the effectiveness of the proposed TPS policies and demonstrate that our SAC-PARC scheme outperforms state-of-the-art schemes in minimizing UoI.

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.

Semantic status update (SSU) communication is envisioned to provide semantic-aware and energy efficient semantic information (SI) delivery in future intelligent Internet of Things (IoT) applications. In this paper, we integrate the knowledge base (KB)-enabled semantic network into a discrete time Markov chain, and introduce a new metric in the SSU communication system, named semantic utility loss (SUL), which captures the timeliness and estimation accuracy of SI. The transmitter samples and extracts SI from the physical process, and sends the SSU. To combat semantic noise, the receiver can update KB at the cost of energy consumption to keep semantic match with the transmitter, i.e., inferring informative SI from received SSU. To minimize the weighted sum of SUL and overall energy cost incurred by transmitting SSU and updating KB, we formulate an infinite horizon average cost Markov decision process. We prove that the optimal joint transmission and updating (JTU) policy has a double threshold structure concerning SUL. Simulation results show the superiority of the proposed JTU policy over the zero-wait and sample-at-change baseline policies. In addition, we reveal that under the optimal JTU policy, the SSU communication framework outperforms the non-SSU framework in providing informative and energy efficient SI delivery.

Satellite-integrated Internet can provide pervasive intelligent services for ubiquitous terrestrial equipments (TEs) in the forthcoming sixth generation network. Consider that the most of existing multicast systems in satellite-integrated Internet are content independent, which may result redundant data transmission in satellites with limited resources, we propose a semantic empowered rate splitting multiple access (RSMA) downlink system. First, we propose a semantic empowered metric, named Utility Loss of Information (UoI), which can simultaneously capture freshness, mismatch of transceivers, and environment ingredient for the RSMA downlink system. Then, we design a joint content- and environment-aware sampling policy for discrete multistate Markov sources to achieve minimum UoI, and provide rigorous proof to show the policy has a threshold structure and derive the closed-form transmission ratio. Further, we formulate a joint optimization of power allocation and rate control for the RSMA downlink system to minimize long-term average UoI with limited onboard resources, and utilize the Lyapunov optimization framework to transform the above problem to minimize the upper bound of corresponding drift-plus-penalty expression, and solve via a deep reinforcement learning-based algorithm. Simulation results validate that our scheme achieves the minimum long-term average UoI, under optimal tradeoff among timeliness, reliability, and environment-aware importance, and outperforms the state-of-the-art schemes.

In this paper, we propose three unequal timeliness (UT) protection massive access (UTMA) schemes in satellite-based Internet of Things (S-IoT) for mission critical communications (MCC) user equipments (UEs) with three types of timeliness requirements: independent successive UTMA (IS-UTMA), extended cognitive offloading UTMA (ECO-UTMA), and independent cognitive offloading UTMA (ICO-UTMA). First, MCC UEs are grouped according to their timeliness requirements, and a multi-dimensional codebook is introduced to resolve the UE collisions in massive access. Then, the IS-UTMA exclusively allocates time slots and pilots to different MCC UE groups to perform massive access, while the ECO- and ICO-UTMA allow timeliness critical group to share resources with timeliness tolerant group to improve the system timeliness. To capture the timeliness evaluation of each MCC UE group, we utilize age of information (AoI) to model the information freshness and derive closed-form expressions of average AoI (AAoI) by tracing the access failure probability (AFP) and instantaneous AoI. Furthermore, we establish the parameter optimization problems to minimize AAoI under desired AFP requirements. Extensive simulations validate the accurate of theoretical derivations, and demonstrate the effectiveness of the proposed UTMA scheme with joint optimized parameters, which can achieve minimum AAoI under desired AFP than the state-of-the-art schemes.

Satellites can provide timely status updates to massive terrestrial user equipments (UEs) via non-orthogonal multiple access technology (NOMA) in satellite-based Internet of Things (S-IoT) network. However, most of the existing downlink NOMA system are content-independent, which may result redundant transmission in S-IoT with limited resources. In this paper, we design a content-aware sampling policy via a semantic-empowered metric, named Age of Incorrect Information (AoII) to evaluate the freshness and value of status updates simultaneously, and formulate a long-term average AoII minimization problem with three constraints, including average/peak power constraint, network stability and freshness requirement. By regarding the long-term average AoII and three constraints as Lyapunov penalty and Lyapunov drift, respectively, we transform the long-term average AoII minimization problem to minimize the upper bound of Lyapunov drift-plus-penalty (DPP). Then, we utilize the deep reinforcement learning (DRL) algorithm Proximal Policy Optimization (PPO) to design our AoII minimization resource allocation scheme, and solve the non-convex Lyapunov optimization problem to enable the semantic-empowered downlink NOMA system. Simulation results show that our proposed SAC-AMPA scheme can achieve the optimal long-term average AoII performance under less power and bandwidth consumption than state-of-the-art schemes.

To satisfy the diversified timeliness requirements in massive machine-type communications (mMTC) for satellite Internet of Things (S-IoT), we propose two unequal timeliness protection (UT) schemes based on the grant free age-optimal (GFAO) random access protocol, where the number of access slots in a frame can be adjusted according to the system load to achieve the required age of information (AoI) performance. We first propose the independent UT protection (IUT) scheme, where the different groups of user equipments (UEs) are successively access according to their AoI priority. Then, we propose the expanded UT protection (EUT) scheme, where the lower priority groups are allowed to offloading access with the higher priority groups. By exploiting Markov analysis through tracing the instantaneous AoI evolution of UE from each priority group, we derive the closed-form expressions to the average AoI (AAoI) of different priority groups and the system AAoI for multitype services coexistence mMTC in practical S-IoT. Simulation results show that both of IUT and EUT schemes can satisfy the AAoI of the higher priority groups, and the EUT scheme can improve the AAoI of the lower priority group, thus improve the system AAoI.

In satellite-based Internet of Things (S-IoT) system, the timely status updating of terrestrial sensing user equipments (UEs) to satellite could be hampered by the long propagation delay, especially in massive machine type communications (mMTC). To guarantee the information freshness in S-IoT, a new performance indicator called age of information (AoI) is exploited to analyze the average AoI (AAoI) in the overload case of mMTC, and a grant free age-optimal (GFAO) random access protocol is proposed to lower the AAoI. Specifically, the closed-form expression of AAoI is derived by tracing the instantaneous AoI evolution of each UE through Markov analysis. Then, the proposed GFAO random access protocol is proved to achieve a minimum AAoI and a maximum throughput in S-IoT, by adjusting the number of access time slots in each transmission frame in the overload case of mMTC. Extensive simulations are conducted to validate the theoretical analysis, and show that there exists different optimal value of access time slots in system load region from 0.2 to 3, which can minimize AAoI and maximize throughput in the proposed GFAO random access protocol.

Due to ubiquitous coverage inherited from the satellites, space-air-ground integrated networks (SAGIN) has been viewed as a promising enabler to provide “anywhere and anytime” broadband access for the next generation of mobile network. Nevertheless, the status updating to the satellite of terrestrial sensing devices could be hindered by the propagation delay. As a result, it becomes crucial to investigate the timeliness of information for massive machine type communications (mMTC) random access in SAGIN. In this paper, we analyse the timeliness of information for the mMTC random access scenario via a new performance metric named age of information (AoI), and propose an age-critical pilot allocation (ACPA) random access protocol aiming to lower the system average AoI (AAoI). By tracking the AoI evolution of each device via Markovian analysis, the closed-form expression of the system AAoI is derived, then we conduct an optimal number of slots to achieve the lowest system AAoI with the increasing of the system load. Simulation results validate the accuracy of our theoretical analysis, and also show that our ACPA protocol can significantly outperform other relevant random access protocols in terms of reducing the AAoI in overload cases.

The timeliness of information is important for massive machine type communications (mMTC) random access in satellite internet of things (S-IoT), where the propagation delay would hinder the terrestrial sensing devices update their timely status to the satellite. In this paper, we analyse the timeliness of information for the mMTC random access scenario via a new performance metric named age of information (AoI), and propose an age-optimal multi-slots pilot allocation random access (AMSPA) protocol, which aims to lower the system average AoI with a certain access failure probability (AFP) requirement. We derive the closed-form expression of the system average AoI by tracking the AoI evolution of each device via Markovian analysis, then we conduct an optimal number of slots to achieve the lowest system average AoI with the increasing of the system overload. Simulation results validate our theoretical analysis and show that we can minimize the system average AoI via choosing an optimal number of slots under diversity system load for our AMSPA protocol.

Space information network (SIN) is regarded as an effective solution to enable ubiquitous connectivity in a global coverage and a cost-effective manner for massive machine type communications (mMTC) in the future internet of things (IoT). In this paper, we study an uplink code-domain non-orthogonal multiple access (CD-NOMA) mMTCs system for SINs, and introduce an uncoordinated code-domain NOMA protocol. Considering the dominant traffic in uplink mMTC communications is short packet, where the fixed length control overhead becomes inefficient due to the short length of payload. To address this challenge, superimposed pilots (SP) scheme is adopted for synchronization and channel estimation. Moreover, we utilize successive interference cancellation (SIC) and successive joint decoding (SJD) to recover the signals in collisions under the shadowed-Rician fading and path loss satellite-ground channel, and the expressions of the outage probability and maximum system throughput of SP with SIC and SJD decoding methods are derived, respectively. Simulation results validate our analytical results and show that the maximum system throughput of SP with SJD can outperform that of SIC in SIN for a short packet transmission.