Low Earth orbit (LEO) satellite networks are expected to support heterogeneous services, including enhanced mobile broadband (eMBB) communications, massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). However, existing coexistence schemes, such as puncturing and superposition, struggle to achieve an effective trade-off among reliability, latency, and spectral efficiency due to their limited degrees of freedom (DoF). To address this challenge, we propose a novel rate-splitting grant-free (RS-GF) transmission scheme that integrates rate-splitting multiple access (RSMA) with grant-free random access (GF-RA) to efficiently support heterogeneous quality of service (QoS) requirements. The high-rate eMBB user employs single-layer rate splitting (RS) over the entire slot, while short-packet Internet-of-Things (IoT) devices associated with URLLC and mMTC adopt GF-RA via single mini-slot transmissions. Building on this RS-GF framework, we analyze the outage performance of the proposed scheme. Specifically, we derive the average packet error probability (PEP) of IoT devices in the finite blocklength (FBL) regime and analyze the eMBB user’s outage probability under imperfect successive interference cancellation (SIC) and mini-slot collisions. On this basis, we present simplified analytical solutions for sparse and dense IoT deployment scenarios, and Monte Carlo simulations validate our analytical derivations. Simulation results demonstrate that the proposed RS-GF scheme outperforms state-of-the-art solutions for service coexistence in LEO satellite networks.

We introduce Toto, a time series forecasting foundation model with 151 million parameters. Toto uses a modern decoder-only architecture coupled with architectural innovations designed to account for specific challenges found in multivariate observability time series data. Toto's pre-training corpus is a mixture of observability data, open datasets, and synthetic data, and is 4-10$\times$ larger than those of leading time series foundation models. Additionally, we introduce BOOM, a large-scale benchmark consisting of 350 million observations across 2,807 real-world time series. For both Toto and BOOM, we source observability data exclusively from our own telemetry and internal observability metrics. Extensive evaluations demonstrate that Toto achieves state-of-the-art performance on both BOOM and on established general purpose time series forecasting benchmarks. Toto's model weights, inference code, and evaluation scripts, as well as BOOM's data and evaluation code, are all available as open source under the Apache 2.0 License.

Low earth orbit (LEO) satellite-based Internet of Things (IoT) has great potential to provide seamless global coverage, but the large propagation delay and severe Doppler shift in terrestrial-satellite link (TSL) will become the most challenging problem. To handle these challenges and facilitate massive grant-free random access, we propose an orthogonal time frequency space-based scramble-division multiple access (OTFS-SDMA) scheme, where the scrambling technique is used to tackle the correlated TSL channels between neighboring devices. At the receiver, we first propose a user activity detection (UAD) method based on capturing the dominant line-of-sight (LoS) path, without relying on the assumption of a static TSL. To facilitate accurate channel estimation (CE) against severe Doppler shifts, we exploit prior information about satellite velocity to detect the angles of arrival (AoAs) of active devices with the two-dimensional multiple signal classification (2D-MUSIC) algorithm, and further estimate the Doppler shifts. Building on the Doppler estimation, the orthogonal matching pursuit (OMP) algorithm is used to estimate the sparse TSL channel in the time-delay (TD) domain. In accordance with the OTFS-SDMA scheme, we propose a cross-domain elementary signal estimator (CD-ESE) for multi-user detection (MUD). In the CD-ESE MUD structure, both bit-level and symbol-level scrambling sequences help to distinguish neighboring active devices with correlated TSL channels, and the channel decoder works in conjunction with the CD-ESE to enhance MUD accuracy. Simulation results are provided to demonstrate the superior performance of the proposed OTFS-SDMA scheme over the state-of-the-art solutions.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks due to its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The proposed scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. We formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based methods are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner but also it improves the delay performance and maintains the delay stability.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered to be a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large transmission delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing to improve intelligent transportation systems (ITS). HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities, which is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

To cope with the unprecedented surge in demand for data computing for the applications, the promising concept of multiaccess edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for mobile devices (MDs). Since enormous workloads need to be migrated, and MDs always remain resource-constrained, data offloading from devices to the MEC server will inevitably require more efficient transmission designs. The integration of nonorthogonal multiple access (NOMA) technique with MEC has been shown to provide applications with lower latency and higher energy efficiency. However, the existing designs of this type have mainly focused on the transmission technique, which is still insufficient. To further advance offloading performance, in this work, we propose an application-driven NOMA-enabled computation offloading scheme by exploring the characteristics of applications, where the common data of the application is offloaded through multidevice cooperation. Under the premise of successfully offloading the common data, we formulate the problem as the maximization of individual offloading throughput, where the time allocation and power control are jointly optimized. By using the successive convex approximation (SCA) method, the formulated problem can be iteratively solved. Simulation results demonstrate the convergence of our method and the effectiveness of the proposed scheme.