Game theoretic power allocation and waveform selection for satellite communications

Shu, Zhihui; Wang, Gang; Tian, Xin; Shen, Dan; Pham, Khanh; Blasch, Erik; Chen, Genshe

doi:10.1117/12.2176890

Cited by 12 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have studied the dynamic network characteristics based on game theory models to maximize the benefits of spatial information networks [2,3]. Dynamic graphs can represent the dynamic change characteristics of these networks.…”

Section: Resource Scheduling Methods Based On An Intelligent Optimiza...mentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Fan

Yang

et al. 2021

Mathematics

View full text Add to dashboard Cite

To overcome the low timeliness of resource scheduling problems in spatial information networks, we propose a method based on a dynamic reconstruction of resource request queues and the autonomous coordinated scheduling of resources. First, we construct a small satellite network and combine the graph maximum flow theory to solve the link resource planning problem during inter-satellite data transmission. In addition, we design a multi-satellite resource scheduling algorithm with minimal time consumption based on graph theory. The algorithm is based on graph theory to reallocate the resource request queue to satellites with idle processing resources. Finally, we simulate the efficient resource scheduling capability in the spatial information network and empirically compare our approaches against two representative swarm intelligence baseline approaches and show that our approach has significant advantages in terms of performance and time consumption during resource scheduling.

show abstract

Section: Resource Scheduling Methods Based On An Intelligent Optimiza...mentioning

confidence: 99%

“…In the spatial information network, the satellite task distribution is unbalanced, and reasonably reconfiguring the task queue and realizing the fast scheduling of satellite resources is an urgent problem [2,3].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

Fan

Yang

et al. 2021

Mathematics

View full text Add to dashboard Cite

show abstract

“…5,6 Therefore, a transponder's front-end AGC is needed for fast response to large amplitude variations without overreacting to the noise and jamming such that the inter-modulation (IM) level at the output of the HPA is at an "acceptable" level and the link performance is enhanced in the presence of smart jammers and non-linearity of the satellite HPA. Jamming mitigation for SATCOM has been the subject of much research using different approaches such as game theory, 7,8 frequency hopping, [9][10][11] wave selection, 12 power allocation, [13][14][15] and systems-level analysis. 16 These approaches are typically developed for near-real-time data-driven approaches with limited predictive analysis.…”

Section: Introductionmentioning

confidence: 99%

PID-based automatic gain control for satellite transponder under partial-time partial-band AWGN jamming

Bao

Peng

Pham³

et al. 2023

Sensors and Systems for Space Applications XVI

View full text Add to dashboard Cite

This paper presents a proportional-integral-derivative (PID)-based automatic gain control (AGC) approach for satellite communications attacked by partial-time partial-band additive white Gaussian noise (AWGN) jamming. The analysis based on the stochastic model predictive control (SMPC) shows that the AGC performance depends on the accurate characterization of the jammed signal in the future time instants. However, such characterization is generally unavailable. To overcome the limitations of the existing AGC schemes without considering the future trend of the signal amplitude tracking errors (i.e., the difference between the average amplitude and the desired amplitude), the proposed approach uses the derivative term of signal amplitude tracking errors for anticipatory control and the integral term in the PID control to eliminate steady-state errors. Furthermore, different block sizes of the sampled signals are used for computing and selecting gain control values to achieve a good trade-off between fast response and robustness to noise/jamming. Extensive simulations of a system based on the typical satellite transponder link using Quadrature Phase Shift Keying (QPSK) modulated input signals and AWGN noise/jamming demonstrate that the proposed approach can achieve better control performance for maintaining the desired signal amplitude range and smaller bite error rate (BER) in the case of AWGN jamming, as compared with the existing AGC schemes.

show abstract

“…In addition, SSA benefits from game-theory studies in supporting pursuit-evasion analysis [ 46 , 47 , 48 , 49 , 50 ] and from gathering data to track satellites, debris, and natural phenomena [ 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. Then, in order to have effective SSA results, tracking efforts must coordinate with detection policies [ 58 , 59 ], waveform selection [ 60 ], and attack mitigation [ 61 , 62 , 63 , 64 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi

Ferrara²,

Benedetto

2022

Sensors

View full text Add to dashboard Cite

Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

show abstract

Game theoretic power allocation and waveform selection for satellite communications

Cited by 12 publications

References 20 publications

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks

PID-based automatic gain control for satellite transponder under partial-time partial-band AWGN jamming

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Contact Info

Product

Resources

About