Low Earth orbit (LEO) satellite communications play a critical role in achieving global connectivity, yet they face significant challenges due to high satellite mobility and incomplete channel state information (CSI). Moreover, the integration of reconfigurable intelligent surfaces (RIS) in certain scenarios introduces additional complexities. In this paper, we propose a novel MIMO channel prediction framework tailored for LEO satellite communications involving unmanned aerial vehicle-mounted RIS (UAV-RIS), employing a spatiotemporal-attention (ST-attention) mechanism to capture both the spatial correlations among antennas and the temporal dynamics of rapidly varying channels. Furthermore, we leverage masked pretraining to enhance the model’s robustness under scenarios of severe CSI incompleteness, enabling effective reconstruction of missing channel information. Comprehensive simulations demonstrate that our approach outperforms traditional model-based predictors, whether historical CSI is fully available or only partially observed.

Satellite edge computing networks are gaining momentum, necessitating efficient task scheduling and resource allocation to optimize performance. This paper focuses on collaborative computing among multiple edge satellites, addressing the dynamic nature of satellite networks and energy constraints. We propose the Delay-Optimal Task Scheduling Offloading and Resource Allocation (DO-TSO-RA) algorithm to minimize task processing delay and ensure network stability. By decomposing the problem into task scheduling, offloading, and resource allocation subproblems and employing alternating optimization and sequential task scheduling algorithm to solve them. Finally simulations demonstrates superior performance of DO-TSORA in reducing task delay and improving resource utilization compared to existing methods.

Inter-satellite links (ISLs) enable the dense satellite network to remarkably enhance the communication performance of the satellite system. However, the increasing number of LEO satellites and the widespread application of ISLs lead to severe inter-satellite interference (ISLI), restricting communication performance. Therefore, it is essential to study and further restrain the increasingly severe ISLI issue to make full use of the microwave spectrum resources. Meanwhile, considering the importance of satellite network to ground coverage, we construct the problem as a multi-objective optimization problem that maximizes inter satellite signal-to-noise ratio (SINR) and coverage rate. Furthermore, we adopt ε-constraint method to convert it into a single-objective optimization problem and propose a low-complexity maximum spatial uniformity constellation design method. Finally, the influencing factors of ISLI are analyzed through simulation, and the feasibility and effectiveness of the proposed method are verified.

The deployment of low-Earth orbit (LEO) constellations, combined with inter-satellite link (ISL) technology, will drive the development of future sixth-generation (6G) and satellite Internet of Things (SIoT). However, the deployment of a large number of LEO satellites and corresponding ISLs intensifies the spectrum resource scarcity in space, which may result in severe ISL interference (ISLI). Therefore, this paper proposes a theoretical framework for optimal design of LEO satellite constellations to mitigate ISLI. Specifically, the system average signal-to-interference-plus-noise ratio (SA-SINR) metric is introduced to depict ISLI, and deterministic analysis expression for SA-SINR is derived. Meanwhile, an integral merging algorithm (IMA) is proposed to accurately calculate the ground coverage area of satellite networks, considering the need to cover ground users in a wide distribution range. Then, the optimal constellation design issue is formulated into a mathematical multi-objective optimization problem, and an improved particle swarm optimization (PSO) scheme is designed to solve it. Simulation results show that the constellation designed based on the proposed approach outperforms the existing Starlink and OneWeb constellations in terms of ISLI and coverage performances, validating the effectiveness of our proposal.

Advancements in communication, manufacturing, and launch technologies have significantly accelerated the development of Low-Earth orbit (LEO) satellite communication constellations. Simultaneously, the rapid expansion of LEO satellite platforms presents new opportunities for navigation enhancements and substantial benefits for internet of things (IoT) applications. This survey highlights the latest advancements in the integrated communication and navigation (ICAN) technologies, emphasizing key research issues. Specifically, we discuss the motivation and feasibility of integrating communication and navigation on LEO satellite platforms, analyzing various LEO-based navigation paradigms while assessing their respective advantages and limitations. We further explore the mutual promotion of navigation and communication under the ICAN paradigm and discuss waveform design, analyzing the characteristics of different waveform design approaches. Finally, we highlight the unique challenges and identify open research topics within the ICAN framework for future exploration.

The advantages of Low Earth Orbit (LEO) satellite navigation in addressing the increasing demand for high-precision and universally accessible positioning have garnered widespread attention. Our work builds upon our previous development of the Zadoff-Chu non-orthogonal multiple access (ZC-NOMA) waveform for integrated communication and navigation (ICAN). Expanding on this foundation, we conceive the ICAN-oriented multi-satellite asynchronous transmission (ICAN-MSAT) framework, which allows users to concurrently receive communication and navigation signals from multiple satellites without synchronization, thus increasing communication flexibility and leveraging the geometric distribution and robust signals of LEO satellite constellations to enhance navigation performance. Within the ICAN-MSAT framework, we propose a novel multi-satellite asynchronous transmission oriented subcarrier and power allocation (MSASP) algorithm to manage mutual interference between communication and navigation components and inter-satellite interference (INSI) caused by asynchronous transmission, with the latter often overlooked in most existing work. Simulation results demonstrate that the proposed algorithm meets the performance requirements of both navigation and communication, achieving higher communication rates under the same navigation accuracy constraints compared to current benchmarks.

Upon completing the design and training phases, deploying a deep learning model to specific hardware becomes necessary prior to its implementation in practical applications. To enhance the performance of the model, the developers must optimize it to decrease inference latency. Auto-scheduling, an automated approach that generates optimization schemes, offers a feasible option for large-scale auto-deployment. Nevertheless, the low-level code generated by auto-scheduling closely resembles hardware coding and may present challenges for human comprehension, thereby hindering future manual optimization efforts. In this study, we introduce ASight, a visual analytics system to assist engineers in identifying performance bottlenecks, comprehending the auto-generated low-level code, and obtaining insights from auto-scheduling optimizations. We develop a subgraph matching algorithm capable of identifying graph isomorphism among Intermediate Representations to track performance bottlenecks from low-level metrics to high-level computational graphs. To address the substantial profiling metrics involved in auto-scheduling and derive optimization design principles by summarizing commonalities among auto-scheduling optimizations, we propose an enhanced visualization for the large search space of auto-scheduling. We validate the effectiveness of ASight through two case studies, one focused on a local machine and the other on a data center, along with a quantitative experiment exploring optimization design principles.

Hero drafting for multiplayer online arena (MOBA) games is crucial because drafting directly affects the outcome of a match. Both sides take turns to "ban"/"pick" a hero from a roster of approximately 100 heroes to assemble their drafting. In professional tournaments, the process becomes more complex as teams are not allowed to pick heroes used in the previous rounds with the "best-of-N" rule. Additionally, human factors including the team's familiarity with drafting and play styles are overlooked by previous studies. Meanwhile, the huge impact of patch iteration on drafting strengths in the professional tournament is of concern. To this end, we propose a visual analytics system, BPCoach, to facilitate hero drafting planning by comparing various drafting through recommendations and predictions and distilling relevant human and in-game factors. Two case studies, expert feedback, and a user study suggest that BPCoach helps determine hero drafting in a rounded and efficient manner.

The underwater sensor network plays a critical role in the ocean data collection owing to its capability of providing reliable and wide communication coverage. How to assure the lifetime performance of the network with a limited energy supply has always been a key research issue. In this article, we propose an underwater acoustic sensor network model which supports data delivery from each underwater sensor node (USN) to the sea surface sink node (SN) in either direct or relay-assisted transmission mode, i.e., transmission mode between each USN and the SN can be dynamically selected according to network’s current state. To optimize its lifetime performance, we model the mode selection and resource allocation issues jointly into a nonconvex and mixed-integer programming problem. In order to efficiently solve it, we further divide the original problem into two subproblems: 1) resource allocation subproblem and 2) joint mode and relay selection subproblem. We prove that the reformulated nonconvex resource allocation problem can be equivalent to a convex optimization problem, and its optimal solution can be found by using Lagrange dual decomposition method. For the second subproblem, some critical conditions are analyzed to determine optimal relays with the criterion of balancing energy consumption between USNs, and a matching approach is designed to obtain the mode and relay selection results based on USNs’ priorities. Simulation results validate that the proposed transmission scheme is effective and superior to the traditional time division multiple access scheme.

For the increasingly strong demand for low-orbit navigation enhancement, a novel navigation and communication integrated (NAVCOM) waveform for the LEO constellation is proposed, which can conduct communication and navigation simultaneously. Zadoff-Chu (ZC) sequence with controllable power is superimposed on the communication signal in the frequency domain as the positioning signal. Furthermore, positioning can be conducted with both time-domain correlation detection (TDCD) and frequency-domain phase estimation (FDPE) for higher ranging accuracy benefits from the Fourier invariance of ZC sequence. Interference between positioning and communication components is analyzed, and the Cramer-Rao low bound (CRLB) of ranging error is given. The performance evaluations show that the novel waveform can achieve high-precision positioning without signifilcantly affecting communication performance.

In this paper, we investigate the collaborative computation offloading method in satellite edge computing by allowing computation tasks to be executed by multiple satellites with computing capacity. The main purpose is to optimize the resource allocation to minimize the energy consumption of the network, which is formulated as a non-convex optimization problem. To solve it efficiently, we first provide the optimal task allocation scheme and then divide the original optimization problem into two subproblems based on an alternative optimization method. Although two subproblems are still non-convex, we can apply successive convex approximation method to deal with them and design an iterative algorithm to solve them. Finally, simulation results demonstrate the superiority and effectiveness of our proposed algorithm.

Enabling relay-assisted device-to-device (RA D2D) transmissions is a promising way to fully explore the benefits of developing D2D communications in cellular systems. However, taking relay-assisted D2D mode into account make the scheduling at the base station (BS) even more challenging. This paper focuses on the scheduling issues for the cellular system involving relay-assisted D2D communications. Aiming to maximize the cell-wise energy efficiency (EE), we formulate the issue of joint scheduling on D2D mode selection, channel allocation, and power coordination into a mathematical optimization problem. The formulated problem is then decomposed into two subproblems which can be solved efficiently by referring to our previous work. Particularly, in the context of maximizing the EE, we present a strategy to coordinate the transmit powers for the cellular user equipment (UE) and D2D UE when they are sharing the same channel. Simulation results validate the effectiveness of our proposals on Improving the overall EE.

This paper considered a data rate maximization problem of relay satellite network and aimed to overcome the difficulty of direct transmission between the satellites and ground station. The proposed optimization problem was first formulated as a mixed integer non-convex programming which is difficult to solve. Then, to design an efficient solving method, the proposed optimization problem were approximately decoupled into two subproblems including relay selection problem and power allocation problem. Through a node virtualization method, the relay selection problem was converted to a maximum weighted matching problem. However, the power allocation problem was still a non-convex problem but could be approximated as convex problem by using difference of convex (DC) programming. Moreover, an iterative algorithm was designed to acquire the optimal solution of approximated convex problem based on the Lagrangian dual method. Finally, simulation results are provided to demonstrate the superiority of the algorithm.

Underwater acoustic (UWA) communication plays a key role in the process of exploring and studying the ocean. In this paper, a modified non-stationary wideband channel model for UWA communication in shallow water scenarios is proposed. In this geometry-based stochastic model (GBSM), multiple motion effects, time-varying angles, distances, clusters' locations with the channel geometry, and the ultra-wideband property are considered, which makes the proposed model more realistic and capable of supporting long time/distance simulations. Some key statistical properties are investigated, including temporal autocorrelation function (ACF), power delay profile (PDP), average delay, and root mean square (RMS) delay spread. The impacts of multiple motion factors on temporal ACFs are analyzed. Simulation results show that the proposed model can mimic the non-stationarity of UWA channels. Finally, the proposed model is validated with measurement data.

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

To fully explore the benefits of developing device- to-device (D2D) communications in cellular systems, enabling relay-assisted (RA) D2D transmissions is a promising way. However, involving RA D2D mode will make the design of scheduling scheme at the base station (BS) side even more challenging. This work focuses on design of a scheduling scheme involving RA D2D mode for BS, which jointly considers power coordination, relay selection, mode selection, and resource allocation. Aiming to maximize the cell- wise throughput, we formulate such a scheduling issue into a mathematical optimization problem. We show how to decompose the formulated problem into two subproblems and solve them separately by using exiting algorithm and corresponding mathematical optimization theories. Particularly, the integer programming problem on mode and channel assignments is transformed into a linear programming problem to improve the solving efficiency. Simulation results validate the performance of the joint scheduling scheme in terms of cell-wise system capacity.

Smart metering utility network (SUN) is an emerging wireless technology for achieving intelligent control and information transfer in smart grid applications. However, the operation of SUNs on unlicensed bands is very sensitive to external interferences generated from other networks working on the same spectrum bands, such as wireless local area networks (WLANs). This paper aims to analyze packet error rate (PER) performance of a victim SUN receiver under the interferences of one or more WLAN transmitting nodes. To deal with a scenario leveraged by multiple random variables, such as offset between desired SUN data packet and WLAN packet, duration of WLAN data packet, and time interval between two consecutive WLAN packets, we propose a segmented packet collision model with the help of the probability theory. We first divide all the packets into small segments and then calculate the collision probability of each segment to acquire a more accurate bit error rate (BER). Then, based on different BERs in distinct segments of the desired SUN packet, average PER of the victim SUN receiver is obtained. Finally, the proposed analytical model is validated by simulations as an effort to evaluate PER performance of a SUN receiver under the WLAN interferences.

This paper aims to analyze coexistence performance and study the impact of WLAN interferences on SUNs. To quantitatively reveal packet error rate (PER) performance of SUNs under multiple WLAN interferers, a segmentation scheme for desired data packet with multiple interferers is developed, based on which a packet collision model in a multi-interferer environment is proposed. The new packet collision model captures interference scenarios in every time segment of a desired packet using the proposed segmentation scheme. It paves a way to make a precise estimation of the PER of SUNs under multiple WLAN interferers. Furthermore, we design a channel agility scheme for coexistence of SUNs and WLANs via improving the existing ones, and analyze its performance in interference identification and avoidance. The proposed scheme performs interference detection for every data packet, and thus, an SUN is able to avoid WLANs interferences in a timely manner.