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) systems with cell-free massive multiple-input-multiple-output (CFMM) frameworks can offer significant performance improvements for both sensing and communication functions. However, there is an obstacle to the system supporting an extremely large number of users in explosive terminal access scenarios. In this article, we propose a novel nonorthogonal multiple access (NOMA)-aided CFMM-ISAC architecture to enhance the connectivity and efficiency of the system. First, we consider a commonly used single-radar sensing scenario and jointly design user pairing and beamforming to maximize the minimum achievable communication rate while ensuring the sensing requirement. To solve the nonconvexity of the formulated optimization problem, the binary variables denoting the feasible pairing of users are first relaxed into continuous variables. An iterative optimization programming algorithm is then developed using the semidefinite relaxation (SDR), rank-one constraint omission (RCO), and successive convex approximation (SCA) approaches. To obtain feasible initial points of the iterative algorithm, we propose an initial point construction algorithm. Based on the obtained user pairing strategy, a corrective beamforming scheme is devised. We further extend our proposed algorithms to a generalized multiradar sensing scenario. Simulation results validate that the proposed joint user pairing and beamforming design achieves noticeable performance improvements compared to the existing methods.

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology to address user service inequity and frequent cell re-selection. However, most of the existing distributed detectors are based on the assumption that all users are served by each access point (AP) with simple linear detection methods, resulting in extremely high fronthaul overhead and performance degradation. In this paper, we propose a novel hybrid distributed detector for the user-centric cell-free massive MIMO system. Each AP pre-defines a load threshold to determine whether to use the low-complexity maximum ratio combining (MRC) algorithm or the high-performance expectation propagation (EP) algorithm to ensure detection accuracy while reducing the computational complexity. Moreover, we propose a sparsified EP (SEP) algorithm to further improve the detection performance, from the perspective of user interference suppression via channel sparsified transformation. Based on the properties of the distributed MRC-EP/MRCSEP detection scheme, an efficient hybrid fusion approach is proposed. Numerical results illustrate that the proposed hybrid distributed detectors achieve a good performance-complexity trade-off compared to the existing distributed detectors.

The challenge of evaluating and modeling Quality of Experience (QoE) for content streaming video services is discussed in this paper. Based on the QoE evaluation methodology proposed by the EU-funded TRIANGLE project, a novel no-reference QoE model is presented to predict the user Mean Opinion Score (MOS) for videos streamed services such as YouTube. The study has been performed in a testbed that can control radio and network parameters, enabling real-life scenarios for mobile applications and devices in a controllable and repeatable manner. This work takes advantage of Amazon Mturk as a crowdsourcing platform to collect user opinions at scale, efficiently and quickly harvesting opinion scores from a much more diverse population than it would be possible from a local human panel. The performance of the proposed no-reference model is compared with PEVQ-S, a full-reference standardized algorithm used in the industry, showing better performance but with a much simpler approach. The performance has also been evaluated in SQoE-III, a publicly available adaptive video streaming database, to ensure the robustness of the model.

This paper addresses the challenges associated with modeling and evaluation of Quality of Experience (QoE) for content streaming video services. A novel full-reference QoE model is introduced, aiming to predict the user Mean Opinion Score (MOS) for videos streamed from any content streaming platform, although focusing on YouTube. Screen captures of streamed videos were used for an immersive user-experience investigation, avoiding reliance solely on packet analysis. FFmpeg, a widely-used open-source video processing software, and VMAF, an open-source video quality metric by Netflix, are the primary tools used by the proposed approach. The study is carried out in a testbed that allows full control of the radio and network parameters, enabling the emulation of realistic scenarios of mobile networks and establishing actual 5G connections with commercial mobile devices. To gather a diverse range of user opinions and conveniently scale up the dataset, Amazon MTurk is used as a crowdsourcing platform. Data from real users scoring 150 distorted video sequences have been used to train the machine learning algorithm based on QoE model developed in this work. The performance of the proposed model is compared with PEVQ-S, which is a standardized full reference algorithm showing better performance in a comprehensive set of performance metrics.

The Industrial Internet of Things (IIoT) is a typical future application for the mobile networks. Unmanned aerial vehicle (UAV) has attracted a great deal of interest in relay assisted wireless communication systems due to its benefits of high mobility, rapid deployment and high probability of line-of-sight (LoS) transmissions. This paper proposes the three-dimensional (3-D) geometry-based non-stationary channel models for wireless communication underlaying UAV-based relay assisted industrial internet of things (IIoT) networks in the sub-terahertz (sub-THz) band. In order to accurately describe the propagation characteristics of the UAV-based wireless channels in the sub-THz band, the propagation gain and atmospheric absorption gain in free space, LoS path, single UAV-based relay and double UAVbased relay propagation paths are considered in the proposed channel models. The channel impulse response (CIR) expressions of different propagation paths are derived respectively. The statistical properties of the channel models including path loss, channel capacity, temporal auto-correlation function (T-ACF), and Doppler power spectral density (DPSD) at 140 GHz band are investigated and analysed, with a performance comparison at 60 GHz band.

In this paper, we investigate delay-optimized edge caching in the integrated satellite-terrestrial network with diverse content popularity distribution and user access modes. Based on the cooperation among the base stations, the satellite and the gateway, we propose a three-layer caching architecture to provide content service for both base station access users and satellite access users. Considering diverse content preferences for users in different areas, we formulate the content placement problem with the objective to minimize the average content retrieving delay of the network. By introducing the concept of the delay reduction gain and the caching benefit, we first derive the optimal caching strategy for base stations in different areas separately. Then, we propose two algorithms to calculate the cooperative caching strategy of the network, in which reduced search space is applied based on theoretical analysis. While the dynamic programming algorithm can achieve the optimal solution of the content placement problem, the submodular optimization based algorithm can provide guaranteed performance with relatively low complexity. Simulation results show that the proposed caching strategies can effectively improve the network delay performance.

The integration of unmanned aerial vehicles (UAVs) and terahertz (THz) technology can provide high data rate for air-to-ground (A2G) communications. In this paper, a non-stationary geometric sub-THz multiple-input multiple-output (MIMO) channel model is proposed for UAV A2G communication based on a three-dimensional (3D) semi-spherical model. The detailed channel modeling of reflection fading and scattering fading on the rough surface in sub-THz band is considered. According to the proposed geometry-based stochastic model (GBSM), the space-time correlation function (STCF) has been derived and the impact of several important UAV-related parameters on the STCF has been investigated and compared to the millimeter-wave (mm-wave) bands. The results indicate that the stationary time separation of channel and the dependency of spatial cross-correlation function (S-CCF) on antenna element spacing in sub-THz band of 140GHz are both smaller than those in mm-wave band of 28GHz.

Internet of Things (IoT) is one of the main features in 5G. Low-power wide-area networking (LPWAN) has attracted enormous research interests to enable large-scale deployment of IoT, with the design objectives of low cost, wide coverage area, as well as low-power consumption. In particular, a long battery lifetime is essential since many of the IoT devices will be deployed in hard-to-access locations. Prediction of the battery lifetime depends on the accurate modeling of energy consumption. This article presents a comprehensive power consumption model for battery lifetime estimation, which is based on user equipment (UE) states and procedures, for two cellular IoT technologies: 1) Narrowband IoT (NB-IoT) and 2) long-term evolution for machines (LTE-M). A measurement testbed has been set up and the proposed model has been tested and validated via extensive measurements under various traffic patterns and network scenarios, achieving the modeling inaccuracy within 5%. The measurement results show that the battery lifetime of an IoT device can reach up to ten years as required by 3GPP, with proper configuration of the traffic profile, the coverage scenario, as well as the network configuration parameters.

Frequency bands above 6GHz are receiving great attention for 5G mobile radio communications due to the potential availability of enormous channel bandwidths. Various multi-carrier and single-carrier waveforms are being considered as potential candidates for 5G radio interface. When evaluating the waveform performance at high carrier frequency and large bandwidths, the inclusion of realistic hardware impairments is of great importance. In this paper, various 5G candidate waveforms (OFDM, DFTS-OFDM, W/P-OFDM, UF-OFDM, FBMC) are evaluated under hardware impairments (i.e., oscillator phase noise and non-linear amplifier) for above 6GHz mobile communications. It is observed that low out-of-band emission advantage of frequency localized waveforms (W/P-OFDM, UF-OFDM, FBMC) over OFDM and DFTS-OFDM may degrade or even vanish when hardware impairments are considered.

The effects of oscillator phase noises (PNs) on multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems are studied. It is shown that PNs of common oscillators at the transmitter and at the receiver have the same influence on the performance of (single-stream) beamforming MIMO-OFDM systems, yet different influences on spatial multiplexing MIMO-OFDM systems with singular value decomposition (SVD) based precoding/decoding. When each antenna is equipped with an independent oscillator, the PNs at the transmitter and at the receiver have different influences on beamforming MIMO-OFDM systems as well as spatial multiplexing MIMO-OFDM systems. Specifically, the PN effect on the transmitter (receiver) can be alleviated by having more transmit (receive) antennas for the case of independent oscillators. It is found that the independent oscillator case outperforms the common oscillator case in terms of error vector magnitude (EVM).