#  Tiansuan Constellation: An Open Research Platform

Shangguang Wang, Qing Li, Mengwei Xu, Xiao Ma, Ao Zhou, Qibo Sun  State Key
Laboratory of Networking and Switching Technology
Beijing University of Posts and Telecommunications
Beijing, China
<EMAIL_ADDRESS>2nd Shangguang Wang
State Key Laboratory of Networking and Switching Technology
Beijing University of Posts and Telecommunications
Beijing, China
@bupt.edu.cn  3rd Given Name Surname  State Key Laboratory of Networking and
Switching Technology
Beijing University of Posts and Telecommunications
Beijing, China
@bupt.edu.cn  4th Given Name Surname  State Key Laboratory of Networking and
Switching Technology
Beijing University of Posts and Telecommunications
Beijing, China
@bupt.edu.cn  5th Given Name Surname  State Key Laboratory of Networking and
Switching Technology
Beijing University of Posts and Telecommunications
Beijing, China
@bupt.edu.cn

###### Abstract

Satellite network is the first step of interstellar voyages. It can provide
global Internet connectivity everywhere on earth, where most areas cannot
access the Internet by the terrestrial infrastructure due to the geographic
accessibility and high cost. The space industry experiences a rise in large
low-earth-orbit satellite constellations to achieve universal connectivity.
The research community is also urgent to do some leading research to bridge
the connectivity divide. Researchers now conduct their work by simulation,
which is far from enough. However, experiments on real satellites are blocked
by the high threshold of space technology, such as deployment cost and unknown
risks. To solve the above dilemma, we are eager to contribute to the universal
connectivity and build an open research platform, Tiansuan constellation to
support experiments on real satellite networks. We discuss the potential
research topics that would benefit from Tiansuan constellation. We provide two
case studies that have already deployed in two experimental satellites of
Tiansuan constellation.

###### Index Terms:

Satellite Internet, Satellite Edge Computing, 6G, Testbed

##  I Introduction

Human society is facing development bottlenecks such as global warming,
prominent social contradictions, the coexistence of industrial surplus, and
resource depletion. Would human need interstellar voyages and expand the
boundaries of human existence like the age of discovery? If yes, the satellite
network will play a major role at the beginning of the era of interstellar
voyages. Moreover, there still be more than 80% of the land area and more than
95% of the ocean area without network access
111https://www.ccidgroup.com/info/1096/21569.htm. It is impossible for the
terrestrial network to provide ubiquitous broadband internet access because of
geographic accessibility and the high cost of building infrastructure.

Low Earth Orbit (LEO) satellites are promising to provide global internet and
service due to the reduced cost of satellite component manufacturing and ride-
sharing launch. Multiple space companies are gearing up to deploy LEO
constellations to provide global low-latency high-bandwidth Internet. Dated to
November 2021, SpaceX has now launched 1,844 Starlinks and London-based OneWeb
has launched 358 internet satellites. Besides, Microsoft and Amazon offer
“Ground station as a service” for space customers
222https://aws.amazon.com/cn/ground-station/.

This exciting development is taking shape rapidly in the industry, while the
research community is urgent to do some leading research to bridge the
connectivity divide. Large-scale constellations feature hundreds or thousands
of low-volume satellites, each with an orbital period of 100 minutes. This new
type of infrastructure brings inherent challenges. Recent work has highlighted
the challenges the dynamic connectivity could create at various fields such as
network (e.g., topology [1], routing[2], congestion control [3]), earth
observation [4], in-orbit edge computing [5]. While it is useful to explore
these problems using simulation tools, ultimately, we would like to conduct
experiment evaluations on the real satellite constellation. However, due to
the high technical threshold of space technology, such as deployment cost,
unknown risks, most scientific research researches are blocked. SatNetLab
calls researchers to conduct some network experiments on commercial satellite
Internet as users [6], while experiments changing the network settings or to
be deployed on satellites may be not allowed.

Figure 1:  Tiansuan Constellation Framework.

To address these limitations, we are eager to contribute to the universal
connectivity and propose Tiansuan constellation, an open research platform
illustrated in Fig. 1. Tiansuan constellation plans three stages, the first
stage with 6 satellites, the second stage with 24 satellites, and the third
stage with stage 300 satellites. Except for satellites owned by our institute,
satellites carrying our payloads and satellites joining our Tiansuan plan
together form the Tiansuan constellation. This global research platform would
enable 1) experiments on real satellites and 2) testing practical solutions to
improve the performance of the constellation. Thus, it helps studying the
impacts of the real physical environment (e.g., weather conditions), which is
hard to fully capture in simulations. Moreover, real experiments on satellite
constellations can uncover any pitfalls to the solutions for commercial
deployment potential.

TABLE I: Main parameters of Tiansuan Constellation Stage 1. Number | Orbital Altitude | Mass | Battery Capacity | Spectrum | Uplink Rate | Downlink Rate | ISLs | Processors
---|---|---|---|---|---|---|---|---
1 | 500$\pm$50km |  $\leq$ 30kg | 118Wh – 236Wh | X-band | 0.1Mbps – 1Mbps | 100Mbps – 600Mbps | NO | CPU/NPU
2 | 500$\pm$50km |  $\leq$ 30kg | 118Wh – 236Wh | X-band | 0.1Mbps – 1Mbps | 100Mbps – 600Mbps | NO | CPU/NPU
3 | 500$\pm$50km |  $\leq$ 30kg | 118Wh – 236Wh | X-band | 0.1Mbps – 1Mbps | 100Mbps – 600Mbps | NO | CPU/NPU
4 |  $>$ 500km |  $>$ 50kg |  $>$ 360Wh | X, Ku, Ka bands |  $\geq$ 200Mbps |  $\geq$ 1Gbps | YES | CPU/NPU/GPU
5 |  $>$ 500km |  $>$ 50kg |  $>$ 360Wh | X, Ku, Ka bands |  $\geq$ 200Mbps |  $\geq$ 1Gbps | YES | CPU/NPU/GPU
6 |  $>$ 500km |  $>$ 50kg |  $>$ 360Wh | X, Ku, Ka bands |  $\geq$ 200Mbps |  $\geq$ 1Gbps | YES | CPU/NPU/GPU

In the following, we introduce Tiansuan constellation design and how it could
help the research community and LEO satellite industry in Section 2, discuss
research directions Tiansuan could support in Section 3, and provide case
studies deployed on Tiansuan constellation in Section 4. We conclude in
Section 5.

##  II Tiansuan Constellation Design

###  II-A Overview

Tiansuan constellation aims to build an open platform that supports
experiments including but not limited to 6G core network system, satellite
Internet (Satellite Digital Networking), satellite operating system, federated
learning and AI acceleration, and on-board service capability opening. It
plans three stages, the first stage with 6 satellites, the second stage with
24 satellites, and the third stage with 300 satellites. In all three stages of
Tiansuan constellation, there are three satellite types. The first type
satellites belong to our institute. The second type of satellites are owned by
others institutes and carry our payloads which can collaborate with other
payloads on the computing platform. The third type of satellites also belong
to others but can join our open computing platform through a unified
interface.

The first satellite of Tiansuan constellation will be launched in May 2022.
Satellites in Tiansuan are manufactured according to the standard [7] and the
parameters of satellites in the first stage are in Table I. Most satellites
will be launched into sun-synchronous orbit. The first three satellites
combine edge computing capabilities with remote sensing applications. The last
three satellites are mainly to explore communication capabilities with inter-
satellite links. Control functions are provided by the onboard computer while
the majority of the computing power is provided by the payloads as listed in
Table I. As shown in Fig. 1, ground stations receive data from satellites and
distribute them through the Internet. Data is only transmitted by satellites
during their passing of ground stations, with an average of one track per day,
every 6-8 minutes. We have deployed many ground stations, mainly in Changsha
and Xinjiang. The ground stations also serve as gateways to the cloud
computing platform. All of these together form Tiansuan Constellation.

###  II-B Operation Mechanism

The constellation operates according to the open principle. For research
institutes, it provides a platform for scientific research. Institutions can
join Tiansuan constellation either by submitting research requirements or by
enhancing the constellation in collaboration. For satellite companies,
Tiansuan constellation is a complement to their ecosystem. Most companies find
it inconvenient to complete some research work for profit reasons. Tiansuan
constellation fills this gap by gathering researchers to work on cutting-edge
exploratory problems. As a result, the overall cost for companies and research
institutes can be reduced to better stimulate industry development. Other
organizations can take advantage of Tiansuan constellation for testing new
devices or payloads.

##  III Potential Spectrum of Experiments

It is difficult to envision the full spectrum of experiments creative
researchers may run using Tiansuan constellation, several main research
directions that it would support are as follows.

###  III-A Networking

The ambitious plan for providing the Internet through satellite constellations
has attracted the attention of both industry and academia [8]. Satellite
communication, as an enabling technology of 6G networks, is crucial to
achieving global coverage of mobile networks. Tiansuan constellation can
support networking-related experiments at the physical layer (e.g., link
measurement), network layer (e.g., routing), transport layer (e.g., congestion
control), and application layer (i.e., interactive applications). Besides, it
can also support experiments on the network control plane (e.g., core
networks).

For further clarification of the preceding assertion, we elaborate on one of
them, the deployment of 6G core networks. With the rapid construction of space
Internet infrastructure, traditional ways of managing constellations cannot
keep up. Satellites are instead made from generic hardware and deployed using
custom software. By doing so, the onboard network, computation, and storage
resources can be utilized more efficiently. Core networks connect
heterogeneous access networks and data networks. The combination of satellite
constellations and next-generation core networks will lead to a variety of
application scenarios. As an example, LEO satellites are capable of delivering
Internet services with lower latency over long distances [9] [10]. Core
networks deployed on satellites will reduce the latency of signaling
interactions incurred by the control plane, enabling much more real-time
services. On the other hand, satellite constellations can enhance their
capabilities with the next-generation core networks. Satellite constellations
are becoming increasingly difficult to manage due to their explosive growth.
By deploying lightweight core networks on satellites, constellation management
can be more flexible and agile.

Developing the core network functions for LEO satellite nodes is the key to
integrating the 6G core networks with satellite constellations. However, there
are two challenges to overcome. First of all, satellite networks are
experiencing rapid changes in network topology and connectivity. Terrestrial
mobile core networks should virtualize their functions more adaptively in
space. Moreover, power supply and processing resources on satellites are
limited. Network functions and applications should be orchestrated more
efficiently. Thus, one of the key objectives of the Tiansuan constellation is
to verify the cognitive service architecture for 6G core networks as shown in
Fig. 2.

####  III-A1 Mobility Management

Satellites in highly elliptical orbits will have a long latency. While LEO
satellites can reduce latency significantly, they still suffer from short link
duration.

Even though a large ground-orbiting satellite constellation can solve the
short link duration problem, frequent handovers will affect the quality of the
service. Thus, the mobility management of the integrated satellite-terrestrial
network is an important experiment of the Tiansuan constellation.

####  III-A2 Core Network Coordination

Inspired by the distributed nervous system of the octopus, we propose a
cognitive service architecture for the 6G core network. There is one
difference between cognitive service architecture and the previous core
network architecture: the core network is divided into the edge core network
and the cloud network. The edge core networks are similar to the peripheral
nervous system of an octopus’s arms, as they provide the majority of the
network control. The cloud core networks, acting as the central brain of
octopus, are only responsible for guiding and assisting the coordination
between edge core networks. In addition to satellite communication, the edge
core network will be deployed on satellites. With the help of the Tiansuan
constellation, we will deploy edge core networks and evaluate their
performance.

####  III-A3 Serverless and Stateless Design

Figure 2:  Cognitive Service Architecture of 6G core network.

Serverless and stateless design is an effective way to achieve lightweight
deployment. With serverless, the original network element is split into
several independent functions. Each functionality can be independently
developed, tested, deployed, and evolved. Different functionalities can call
each other through flexible interfaces. Stateless separates status from
functions for network elements. This will benefit satellite network
reconfiguration and synchronization.

In an integrated space-air-ground network, the limited resources and weak
capabilities of space-based nodes must be considered. The 6G core network can
be implemented on the satellite with more efficient resource usage by
utilizing serverless network functions. By using the remote interface, network
functions can access abundant resources on the ground. This allows us to
deploy lightweight applications without a heavy network component. By using
this design, network functions can be deployed in different locations
according to business needs and resource availability. In addition, it also
supports network function cancellation and redeployment.

####  III-A4 Knowledge Graph

Satellite networks are highly dynamic and have uncertain links. It is
therefore more difficult to select a reasonable node and plan an optimal flow
path for users. Creating a knowledge map of users, networks, and services can
provide an overview of their real-time status and logical relationship. In
addition, it provides real-time user plane node selection and dynamic traffic
path planning for satellite networks.

The control plane of onboard core networks controls access and mobility, as
well as establishes and schedules user sessions. With the uncertainty of the
network, large-scale user access, and the dynamics of satellite-to-ground
services, the control plane signaling process has become increasingly complex.
Through its knowledge extraction and reasoning capabilities, the knowledge
graph can support decision-making processes in the network. Therefore, it
simplifies signaling interactions in the network.

Nodes and attributes are the basic elements of the knowledge graph. When the
collected information has temporal characteristics, it can be used to describe
the state of the nodes. The mobility of satellite networks and the need to
ensure service continuity necessitate frequent network element migration. The
characteristics of the knowledge graph can be used to plan reasonable
migration strategies for network element functions. As a result, the knowledge
graph ensures better service continuity.

###  III-B Computing

LEO satellite constellations promise to provide global, low-latency, high-
bandwidth Internet service, which has generated tremendous interest in both
industry and the research community. There is also a potential opportunity
that constellations offer computing services wherever people want. Motivated
by edge computing on the ground, satellite edge computing has been proposed by
placing computing resources at the LEO satellite constellation [11, 12].
Tiansuan constellation would support satellite-borne computing platform,
constellation resource management, general computing service, and
heterogeneous satellite data integration, which could provide technical
reference for supporting the construction of space data centers. In the
following, we provide more details on two topics of processing space-native
data, namely real-time earth observation and AI in space. Besides, we also
discuss how to provide general computing services for ground users.

####  III-B1 Real-Time Earth Observation

About 45% LEO satellites in orbit are used for earth observation, which can be
further used in various applications, such as disease spreads [13], crop
monitoring[14], and nature disaster management [15] (e.g. forest fires,
floods, etc.). As the constellation size increases, the conflict between large
space-native data volume and limited downlink capacity is the bottleneck of
real-time Earth observation. There are two potential ways to address the
challenge, (1) modeling the freshness of the interested information and only
downloading freshest information, (2) leveraging multiple distributed ground
stations to schedule downloads effectively.

Freshness of Information. Since the real-time applications need fresh
information for the decision-making, it is critical to make sure that the
received information is valuable and timely. Thus, one of the main reasons
behind the difficulty in the deployment of various real-time earth observation
applications is the hardness of obtaining fresh data. Unlike traditional
terrestrial transmission, data must wait on the satellite before it
establishes a link to the ground station. This adds the staleness of data
received by the ground stations. Furthermore, when the constellation size is
small, the ground stations have very limited time to establish links with
satellites, it is very difficult for them to download so much high-quality
image data in such a limited time, not to mention the bottle-necked bandwidth
and contention. Age of information (AoI) [16, 17] as a metric of capturing the
freshness from the destination widely used in time-sensitive applications.
This motivates us to conduct AoI-aware research on the data sampling,
transmission, and processing on the Tiansuan constellation project for
improving the performance of earth observation systems.

Ground Station Deployment. The current centralized ground stations incur high
deployment costs [4], large delay jitter[3], and weak scalability. In general,
the cost of deploying a ground station is around a million dollars, primarily
due to the current demand for professional equipment and maintenance [4]. For
new entrants, the cost of permitting and establishing ground stations is
prohibitive. To meet the specific delay requirements of different users at
different times, we could study a suitable ground station deployment strategy.
The development of lightweight and scalable ground stations will also be non-
trivial to realize the interconnection and intercommunication of the
satellite-to-ground network. With the large number of satellites and ground
stations, we must dynamically schedule ground station-satellite links while
taking into account orbits, the quality of the link, and the weather, etc. By
dynamic ground station-satellite link planning, congestion is avoided and
transmission efficiency is improved. Forecasting is considered a good way to
solve this problem [4]. Traditional traffic forecasting methods, however, have
been unable to accurately predict traffic on satellite networks due to the
spatial and temporal correlation characteristics. Spatial-temporal predictions
based on the impacts of past workloads themselves (temporal) and their mutual
impacts (spatial) have received considerable attention in recent years, mainly
focused on traffic flow predictions[18, 19, 20]. The satellite downlink rate
varies with the conditions of the channel at a given location. By using
historical data and environmental information (weather conditions) to create a
satellite link spatial-temporal prediction model, we can analyze the mutual
influence between different links and the autocorrelation over time.

####  III-B2 AI in Space

The rapid development of satellite edge computing and satellite Internet will
promote the popularity of on-board applications, which eventually enable
satellite-based AI techniques.

Federated Learning in Space. Federated learning (FL) [21, 22, 23], as a
distributed learning paradigm, has great prospects for widespread deployment
in satellites. Traditional image detection methods need to transmit these
images to the ground station for recognition. But these methods face two key
challenges: (1) The uplink and downlink bandwidth is limited, and the
transmission process has a large latency, and (2) the transmission process is
relatively fragile, and the transmission may be interrupted, the data may be
lost. In addition, privacy protection is also a key requirement for satellite
applications. By FL, we can directly process and analyze the collected
information on the satellite, while protecting the satellite’s data privacy
[24]. We propose to establish a federated learning and AI acceleration
platform based on Tiansuan constellation, through which we further study and
verify the following research points: (1) machine learning model training and
acceleration capability verification based on space-borne computing equipment,
(2) design and verification of AI algorithms for specific satellite
application scenarios, and (3) establishment and verification of federated
learning experimental platform based on Tiansuan constellation.

Meanwhile, FL, just like traditional deep learning systems [25, 26], often
demonstrates incorrect or unexpected corner-case behaviors, especially in the
harsher space environment. We can design a systematic testing tool for
automatically detecting erroneous behaviors of FL-driven satellites that can
potentially lead to data invalidation analysis [27]. Our tool is designed to
automatically generate test cases leveraging real-world changes in the space
environment like meteorites, lighting conditions, shooting angle, etc. We can
further show that the test inputs generated by our testing tool can also be
used to retrain the corresponding FL model to improve the model’s robustness.

Inference in Space. As mentioned above, offloading all deep learning tasks to
the ground station is one of the current mainstream research methods [28, 29].
However, the challenge is the limited bandwidth in practical applications.
It’s reported that about two-thirds of the earth’s surface is covered by
clouds at any time and satellites often capture and save a large number of
useless images, which cause the downlink bandwidth occupied and great
uncertainty in network environment. The current experimental satellites have
airborne AI processing capabilities. It’s possible to leverage on-board
inference to identify and remove useless images and only send useful ones to
the ground station for subsequent processing. Based on a variety of satellite
SoCs, using the characteristics of heterogeneous computing platforms to
accelerate AI tasks is worth expecting [30]. Moreover, speculative inference
can benefit data-driven applications that involve the fusion of multi-modal
satellite signals [31]. Real-time actuation can benefit from speculative
inference. For instance, satellites navigation or grasping can leverage multi-
modal signals from different points to finish difficult tasks in complex
environments.

####  III-B3 Space Service Computing

The ground-satellite connectivity and inter-satellite connectivity are time-
varying due to the dynamics of the satellite network topology. In such a
dynamic context, (1) how to select the ground-satellite path and inter-
satellite path and provide reliable network service, (2) how to realize the
dynamic service deployment, computing offloading, service migration and
service coordination are still to be explored. Micro-service and function as a
service are the lightweight application architectures for services in
satellites. They decompose applications to finer-grained service components
which can be deployed and executed quickly and independently. However, (1) how
to partition applications to a set of micro-services or functions, (2) how to
deploy dependent micro-services and functions dynamically, (3) how to register
and manage the highly distributed micro-services and functions, and (4) how to
allocate resource for these large-scale micro-services and functions are still
to be researched.

####  III-B4 Cluster Orchestration

Satellite edge computing can realize computation offloading and process the
computation tasks at LEO satellites, which can save satellite-ground or inter-
satellite link transmission bandwidth and reduce the impact of large
satellite-ground link delays. There are some issues to be addressed. First,
the computing power of a single LEO satellite is limited, then how to select
satellite clusters to process the task cooperatively. Second, how to design a
cooperation strategy to adapt to LEO satellites’ high mobility, and wide-area
load imbalance. Third, how to distribute the computation tasks to realize the
balance between multiple objectives, such as on-board energy efficiency, on-
board computing delay, communication transmission delay. The demand of
computing resources and satellite-ground collaborative AI inference can be
used to optimize multi-satellite node computing task allocation, computing
data migration, inter-satellite signaling, and computing result return
strategies.

Besides, it is necessary to manage a large number of satellites in the
constellation. Traditional cluster orchestration technology has been widely
adopted to manage a huge number of computation and storage resources in the
network [32, 33, 34, 35]. In recent years, with the continuous development of
edge computing [36], integrating the emerging edge nodes and platforms to
cluster management provides a huge number of benefits, such as resources
utilization improvement, the software deployment process simplification and
the compatibility with cloud-native software stack [37, 38]. Treating LEO
satellites as edge nodes gives a unified way to schedule and deploy the
applications on the satellite by reusing the existing applications. This
approach avoids re-developing the platform-specific programs, while moving
toward the vision of software-defined satellite computing. Previous work has
investigated the difficulty of deploying traditional container orchestration
technology on the satellite co-located with the ground station [39], where we
call this inter-satellite orchestration. However, there may be a bunch of
computing units in a single satellite (e.g., Raspberry Pi). Scheduling tasks
to these computing units efficiently can fully utilize the computing resources
on the satellite. Inspired by the inter-satellite orchestration, we can also
use this approach to integrate computing units inside the satellite and
provide services (e.g., KubeEdge [40] provides huge advantages in managing
edge nodes). We call this inner-satellite orchestration. By using this multi-
level management technology, we think the computing efficiency can achieve a
great enhancement.

###  III-C Satellite Operation System

In order to adapt to the increasing demand for satellite software [41, 39], it
is urgent to design and develop an underlying operating system for the
satellite Internet. This new operating system features miniaturization,
ubiquity and universalization, and will be installed and verified on the
orbiting satellite, so as to build an open satellite software environment. Due
to the lack of software and API in the traditional operating system ecosystem
[42], the satellite operating system should be a soft real-time and hard real-
time operating system for embedded hardware. Therefore, the satellite
operating system needs to support users’ definable and quantifiable multi-task
real-time requirements, such as databases, machine learning engines, image and
video processing. However, the implementation may face a list of challenges:
(1) How to propose an approach to upload third-party software while guarding
the security and extensibility? This requires the operating system to support
a variety of technologies, such as static code analysis, run-time stain
analysis, and execution sandbox. (2) How to ensure memory safety during
software runtime? We can leverage the Rust programming language to implement
the operating system for maximum memory safety [43, 44]. We also need to
improve the relevant toolchain and system middleware to pave the way for
future development. (3) How to ensure the reliability of the operating system
in the extreme environment of space (e.g., single event ﬂipping)? This
requires the developers of the operating system to research on multi-machine
backup and fault-tolerance technology to ensure the stability of the software
and the system.

###  III-D Security and Reliability

The security and reliability of satellite constellations are fundamental to
enable its great potential. With the advantage of wide coverage of satellites,
everyone in the world is allowed to participate in the network while there may
be some security issues. Besides, in the LEO environment, due to the combined
effects of temperature and radiation, it is very challenging to ensure the
reliability of LEO micro-satellite service computation. Tiansuan constellation
would support solutions that guarantee security and reliability.

####  III-D1 Security of Constellation

Blockchain is a decentralized data storage technology with ensuring the data
transparency, integrity, and immutability. The feasibility of blockchain
deployment on satellites is indicated by previous work. SpaceChain
333https://spacechain.com/ employs blockchain on satellites to provide secure
orbit data storage, reducing the dependency of blockchain on the ground
network. Two nanosatellite-based blockchain nodes have been launched into
orbit aboard Chinese Long March rockets in 2018. A successful test of space-
to-ground blockchain transactions has been done by SpaceChain in 2019.
Blockstream444https://blockstream.com/ has launched the service of Blockstream
satellite aimed at broadcasting the Bitcoin blockchain to the entire planet
via satellite. Mital et al. demonstrated the usage of blockchain’s smart
contract and distributed ledger for multi-sensor satellite by employing
software STK [45].

While the work mentioned above demonstrates their implementation of blockchain
on satellites, there are remaining issues to be studied. As satellites are the
main source of spatial data, the storage of data on satellites and the
transmission to the ground is one of the most crucial issues. Digital
signature on the satellite is required to be researched to provide the
traceability of data so as to ensure immutable storage. The satellite-ground
collaborative blockchain transmission is a promising solution to guarantee the
reliability of links and the credibility of data. The limited resource and the
special architecture of satellite networks increase the difficulty of problem
modeling and analysis. Moreover, simulations on the ground are insufficient to
conduct further researches. Fortunately, the Tiansuan satellite as an open-
source platform provides the opportunity for real experiments, which would
speed up the evolution of blockchain deployment on satellites.

####  III-D2 Reliability of Constellation

Benefiting from the wide application of commercial-off-the-shelf in LEO
satellites, the cost of LEO satellites has been further reduced, and at the
same time, LEO satellites are also endowed with excellent application
compatibility. However, the commercial-off-the-shelf is not designed for the
harsh environment of space. In the LEO environment, since there is almost no
atmosphere, satellites mainly rely on thermal radiation for heat dissipation.
In the illuminated area, the temperature can rise to 400K, while in the non-
illuminated area it will drop to 150K [46]. The low-mass LEO satellites
frequently enter and exit the illuminated area, causing their working
temperature to fluctuate drastically and frequently. Besides, affected by the
energy distribution strategy and overheating protection strategy on the
satellite, the computing power of the satellite will fluctuate greatly too. On
the other hand, high-energy charged particles in space are affected by the
earth’s magnetic field, forming the Van Allen Belts. The commercial-off-the-
shelf components within its range may have a single-event effect [47] at any
time, which can lead to calculation errors, temporary, or even permanent
failures of the components. Traditional reliability assurance strategies, such
as the introduction of redundancy, checkpoints, etc., are difficult to apply
to nowadays more complex and large-scale satellite service instances on
satellites with extremely limited energy reserves and computing power.
Therefore, the research on the service calculation guarantee strategy of LEO
satellites is a key point in the Tiansuan satellite constellation project.

###  III-E Hardware Testing

The Tiansuan constellation also supports space experiments and in-orbit
testing of new hardware devices. For example, the performance of sensors,
cameras, and antennas can be tested on Tiansuan constellation. Besides,
computing acceleration equipment such as acceleration boards can also be
deployed to verify its performance and reliability in the space environment.
Furthermore, the Tiansuan constellation can also carry out the distributed
deployment of the computing platform to evaluate its performance in the
satellite environment.

##  IV Case Study

We have deployed two use cases in both two experimental satellites of Tiansuan
constellation named Baonyun and Chuangxingleishen, which will be launched on
December 2, 2021.

###  IV-A Satellite-borne B5G Core Network

Figure 3:  Satellite-Borne B5G Core Network.

On August 9, 2021, we conducted the first deployment test of a 5G core network
system on the satellite called TY20. We tested the signaling interaction (base
station and UPF) between the on-board B5G core network and the terrestrial
private 5G network in both the control plane and the user plane. Satellite
control commands were sent through the uplink telemetry link. The B5G core
network on the satellite successfully realized user registration, session
establishment, and control of satellite equipment. Downlink telemetry
monitoring showed that the three main functional components of the core
network were operating normally. It also showed that the core network control
data was generated correctly. The control data generated by the B5G core
network was downloaded to the terrestrial private 5G network. In this way, we
realized the local offloading of edge computing controlled by satellites, and
conducted tests such as video calls. As shown in Fig. 3, we have successfully
deployed a lightweight B5G core network on both the Baonyun and
Chuangxingleishen satellites. This core network is the updated version of the
former 5G core network. It enhances the signaling interactions and can be used
to set up video calls based on Session Initiation Protocol. Tests of
functionalities and performance will be conducted after the two satellites are
launched.

###  IV-B Image Inference Based on KubeEdge

We have implemented KubeEdge and the corresponding AI-related system on both
Baonyun and Chuangxingleishen satellites as shown in Fig. 4. In this system,
we deployed a central controller using a Linux server on the ground. The
satellite establishes an intermittent connection with the central controller
inside the KubeEdge runtime according to the position of the satellite. Based
on Sedna, the KubeEdge AI extension, we deployed two image detection models, a
lightweight model and a high-precision model on the satellite and the central
controller respectively. During the inference of the AI pipeline, the
satellite will capture the image and detect whether it is an object of
interest. When the conﬁdence of inference is high, the satellite will use this
concrete result for later processing. Only when the conﬁdence is low, the
satellite will transfer the image to the central controller on the ground,
aiming to get an exact inference result using the high-precision image
detection model. Such a collaborative approach utilizes the computing
resources on the satellite, which reduces the end-to-end latency in an AI
pipeline.

Figure 4:  Image Inference based on KubeEdge.

##  V Conclusion

In this paper, we introduce a global research platform, Tiansuan
constellation, to enable experiment evaluations on real satellite
constellations. We present the design of Tiansuan constellation in detail and
state how can different parties use our Tiansuan constellation. Moreover, we
list several research topics that Tiansuan would enable and provide two case
studies of real deployment. We hope that more institutes and individuals
interested in LEO satellites can join us in this exciting research area.

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