Low Earth Orbit (LEO) satellite constellations have experienced rapid growth in recent years, driven by their potential to deliver global, high-bandwidth Internet services with low latency. Beyond connectivity, LEO constellations also offer promising opportunities to enable in-orbit processing of space-native data to support a wide range of emerging space applications. In this context, the concept of space computing has been proposed, a paradigm that seamlessly integrates networking and computing to provide computing-as-a-service anytime and anywhere in space. However, the inherent characteristics of satellite constellations, such as dynamic network topologies, constrained system resources, and the harsh space environment, pose significant challenges in achieving this vision. This paper outlines the system architecture and the key enabling technologies for space computing, including spaceborne computers, laser communications, spaceborne router, distributed operating systems, and onboard AI. We also present the implementation of an open space computing platform, the 3-Body computing constellation, along with the in-orbit experimental results that demonstrate the advantages of multi-satellite distributed computing. Furthermore, we outline future research directions essential for advancing toward a truly interconnected, autonomous, and intelligent space computing system.

Integrated sensing and communication (ISAC) is an emerging paradigm in the sixth-generation mobile communication systems (6G) to address the spectrum scarcity and realize the vision of the Internet of Everything (IoE). In this article, we consider a multiple-input-multiple-output (MIMO) ISAC system where the dual-functional radar-communication (DFRC) base station (BS) detects the targets and communicates with multiple downlink users. To meliorate the severe interference management and improve the transmission performance of the ISAC system, we identify a specific transceiver design that splits each independent message into multiple layers at the transmitter and employs a partial group successive interference cancelation scheme at the receivers. To coordinate the communication and radar performance, we formulate an optimization problem to approximate the beamformers to the desired radar beampattern subject to the achievable rate regions. Since, the formulated problem is nonconvex and NP-hard, we propose an iterative algorithm based on the semi-definite programming relaxation, which optimizes the beamformers and rate vectors alternatively to yield near-optimal solutions. Numerical results demonstrate the superior performance of the proposed transceiver design in improving the achievable transmission rate and obtaining better interference management in the ISAC system.

This article studies an intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) network, where the ground users (GUs) desire to receive information from a UAV. Downlink nonorthogonal multiple access (NOMA) is considered typically with two GUs being selected according to whether a Line-of-Sight (LoS) link between GUs and UAV exists. As the accurate channel information of LoS or Non-LoS (NLoS) links for multiple GUs is difficult to acquire, an approximate LoS region-based method is designed to select GUs as an alternative. In order to enhance the communication quality of the far GU, an IRS is deployed to assist the NLoS transmission. For such a system, we evaluate its outage performance in Nakagami-m fading. First, the central limit theorem (CLT) and Laplace transform (LT) are employed to derive the channel statistics of the UAV- IRS-user link. Then, asymptotic closed-form expressions of the outage probabilities are derived for the selected GUs based on Gaussian–Chebyshev quadrature approximation. Monte Carlo simulations validate the validness of our derived outage probabilities. It shows that the approximate LoS region-based scheme provides similar outage performance laws as the accurate LoS region-based one. Moreover, the outage probabilities of selected GUs in terms of NOMA-based protocol and orthogonal multiple access (OMA)-based protocol are analyzed. Simulation results confirm that the proposed NOMA-based protocol is capable of achieving superior performance compared with the OMA-based protocol by setting power allocation factor and targeted acrlong SINR thresholds of near GU and far GU properly. Specifically, when the rate threshold of near GU is relatively large or the rate threshold of far GU is relatively small, the outage performance derived by NOMA-based protocol performs better than OMA-based protocol in most of cases.

Intelligent reflecting surface (IRS) cooperated with the simultaneous wireless information and power transfer (SWIPT) can reinforce the desired signal and deal with the energy supply problem effectively. By leveraging the on-demand mobility of unmanned aerial vehicles (UAVs), the IRS cooperated SWIPT can be deployed in more flexible and reliable scenarios. In this article, we investigate the UAV-mounted IRS-assisted SWIPT for Internet of Things (IoT) networks. In particular, a UAV-mounted IRS is deployed to assist the information transmission and power transfer from the access point to several IoT devices simultaneously. Taking full advantage of the UAV-mounted IRS in attending multiple IoT devices flexibly, a time division multiple access (TDMA)-based scheduling protocol is proposed to serve different IoT devices alternatively during the UAV flying along an optimized trajectory with the information and power transfer executed. Then, an optimization problem of maximizing the minimum average achievable rate of multiple devices is formulated with the specific energy harvesting requirement guaranteed. To solve the nonconvex problem, we leverage the successive convex approximation and block coordinate descent methods to develop an iterative algorithm. Simulation results demonstrate that with the help of the more flexible and reliable UAV-mounted IRS, the minimum achievable rate of the IoT network can be significantly improved.

A UAV-aided wireless power transfer and data collection network is studied, where it is assumed that when the harvested energy at the sensor node (SN) cannot surpass its circuit activation threshold or the received data rate at UAV falls below a minimal required rate threshold, the information outage occurs. The closed-form expressions of energy outage probability and rate outage probability are derived at first, and then the overall outage probability and coverage performance of the system are analyzed. Based on which, an optimization problem is formulated to minimize the overall outage probability by optimizing UAV's elevation angle and the time splitting (TS) factor. Since the problem is non-convex and has no known solution, an alternating optimization (AO)-based algorithm with Golden-section (GS) based linear search method is designed to find the global optimal solution. In order to explore the maximum coverage area of the UAV for a given tolerable outage probability, another optimization problem is also formulated to maximize the coverage range by optimizing UAV's elevation angle. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution of the optimal elevation angle for maximizing the coverage area is derived. Monte Carlo simulations verify the accuracy of the derived closed-form expression of the overall outage probability and the semi-closed-form expressions of the optimum UAV's elevation angle and TS factor. It shows that there exist a unique optimum elevation angle and the TS factor to achieve the minimum overall outage probability, and significant performance gain can be obtained by using our proposed optimization scheme. The developed theoretical results can be useful to the design of UAV-aided wireless communication systems with wireless power transfer.

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV's trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV's CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV's computing energy can also be greatly reduced.