Performance Analysis of the Packet-Based PNT Service in NGSO Broadband Satellite Communication Systems

Providing alternative PNT service to GNSS-challenged users will be an important function of next-generation NGSO broadband satellite communication systems.Herein,a packet-based PNT service architecture in NGSO broadband systems is proposed in which a primary satellite and selected assistant satellites work together to provide PNT service to requesting users.Its positioning performance bounds are mathematically formulated by rigorously analyzing the bounds constrained by different waveforms.Simulations are conducted on different configurations of Walker Delta MEO constellations and Walker Star LEO constellations for corroboration,revealing the following:(1)Both MEO and LEO constellations achieve sub-meter-level positioning precision given enough satellites.(2)Compared to the GNSS Doppler-based velocity estimation method,the position advance based velocity estimation algorithm is more precise and applicable to the PNT service in NGSO broadband systems.(3)To provide PNT service to users in GNSS-challenged environments,the primary and each assistant satellite need only∼0.1‰of the time of one downlink beam.

INTEGRATED TERRESTRIAL-SATELLITE NETWORKS：PART ONE

The rapid growing data traffic brings more and more pressure to the wireless networks,which is predicted to increase by over 10,000 times in the next 20 years.However,currently,there is still large number of population without coverage of mobile service.In addition

INTEGRATED TERRESTRIAL-SATELLITE NETWORKS:PART Two

The rapid growing data traffic brings more and more pressure to the wireless networks,which is predicted to increase by over 10,000 times in the next 20 years.However,currently,there is still large number of population without coverage of mobile service.In addition to the issue of coverage,future wireless networks also need to guarantee the service continuity for emerging services such as Machine-to-Machine and Internet of Things.

Adaptive subsequence adjustment with evolutionary asymmetric path-relinking for TDRSS scheduling

Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.

Low-Complexity Joint Channel Estimation and Symbol Detection for OFDMA Systems

In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.

QoE-Driven Social Aware Caching Placement for Terrestrial-Satellite Networks

In remote terrestrial-satellite networks, caching is a very promising technique to alleviate the burden of space cloudlet(e.g., cache-enabled satellite user terminal) and to improve subscribers' quality of experience(Qo E) in terms of buffering delay and achievable video streaming rate. In this paper, we studied a Qo E-driven caching placement optimization problem for video streaming that takes into account the required video streaming rate and the social relationship among users. Social ties between users are used to designate a set of helpers with caching capability, which can cache popular files proactively when the cloudlet is idle. We model the utility function of Qo E as a logarithmic function. Then, the caching placement problem is formulated as an optimization problem to maximize the user's average Qo E subject to the storage capacity constraints of the helpers and the cloudlets. Furthermore, we reformulate the problem into a monotone submodular optimization problem with a partition matroid constraint, and an efficient greedy algorithm with 1-1 e approximation ratio is proposed to solve it. Simulation results show that the proposed caching placement approach significantly outperforms the traditional approaches in terms of Qo E, while yields about the same delay and hit ratio performance compare to the delay-minimized scheme.

Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.

Beam Scheduling with Various Mission Demands in Data Relay Satellite Systems

Beam scheduling is one of the most important issues regarding data relay satellite systems,which can improve the utilization efficiency of limited system resources by programming beam allocation for relay missions.The ever-increasing relay missions create a substantial challenge for beam scheduling due to an increase in different mission demands.The cooperative usage of different beams further increases the complexity of this problem.Therefore,we develop a novel optimization method to solve the beam scheduling problem for the scenario of various mission demands in the data relay satellite system(DRSS).Based on the analysis of mission demands and resource features,we first construct a heterogeneous parallel machines scheduling model to formulate the beam scheduling problem in the DRSS.To solve this complicated model,we investigate the matching method between mission demands and beam resources,and introduce two concepts,the loose duration and the number of available beams,to make the matching process more effective.Then,the following three algorithms are proposed.Our first approach,the maximized completion probability algorithm(MCPA),applies a greedy strategy based on the new concepts to allocate beams for missions;and two improved versions of this algorithm are also presented,which employ the strategies of mission insertion optimization and mission sequence optimization,respectively.Our simulation results show that the proposed algorithms are superior to the existing algorithms in terms of the scheduled missions,the weight of scheduled missions,and the processing time,which significantly improves the performance of beam scheduling in the DRSS.

LEO constellation design methodology for observing multi-targets

This paper is based on the second problem of the 8th China Space Trajectory Design Competition(CTOC8).The background is LEO constellation design strategy for monitoring discrete multi-targets with small stellite under J model.The difculty is that the small satellite is equipped with low-cost cameras with limited coverage ability and the targets are distributed separately in a key area,which result in long revisit time or large pumber of satelites based on traditional design method.In this paper,a speciflc LEO consellation design method is proposed to cope with the problems.First,grid search and numerical method are performed to construct a database consisting of repeating ground track orbits.Then several orbits are carefully selected by pruning method to visit each target.Finally,repeating ground track costellation is constructed to meet the maximun revisit time constraint.The present method provides a systematic constellation design methodology of remote sensing observation with limited coverage ability,and demonstrates the resulting constellation can obtain rapid revisit frequency over discrete multi-targets with the least number of satellites.