Reconfigurable Intelligent Surfaces Empowered THz Communication in LEO Satellite Networks

Tekbıyık, Kürşat; Kurt, Güneş Karabulut; Ekti, Ali Rıza; Yanıkömeroğlu, Halim

doi:10.1109/access.2022.3223086

Cited by 37 publications

(14 citation statements)

References 34 publications

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“…Parameter tuning via EM: Note that in the linear model (7), the matrix X can be decomposed as X = SB. S is a function of the time domain grid set τ and the Doppler domain grid set ν, and B is a function of the angle domain grid set θ.…”

Section: Channel Sparsity and Vsp Algorithmmentioning

confidence: 99%

“…Therefore, the M-step is equivalent to finding the minimum of the first term of (10b), that is, min ω F (ω, ω (i) ) (11) where F (ω, ω (i) ) − ln p(y|h, ω)p(h|y, ω (i) )dh. From the additive CSCG noise model in (7), we have…”

Section: Channel Sparsity and Vsp Algorithmmentioning

confidence: 99%

“…The distinctive features of LEO satellites, such as their lower altitude resulting in reduced propagation loss, shorter transmission delay, and lower cost compared to higher orbit satellites, have made them highly attractive for providing communication services to various geographic regions [4,5]. Moreover, LEO satellite communications have shown promising prospects as they seamlessly integrate with cutting-edge wireless communication technologies such as massive multiple-inputmultiple-output (massive MIMO) [6], TeraHertz communication [7], non-orthogonal multiple access (NOMA) [8], and reconfigurable intelligent surfaces (RIS) [9], further enhancing the potential of this innovative approach.…”

Section: Introductionmentioning

confidence: 99%

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Variance state propagation for uplink channel estimation in LEO satellite systems

Lei,

Zhang,

Liu

et al. 2023

Electronics Letters

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This paper, considers the channel estimation problem of the uplink low Earth orbit (LEO) satellite orthogonal frequency division multiplexing (OFDM) system. By exploiting the channel structured sparsity in the delay‐Doppler‐angle domain, the channel estimation problem is formulated as a block‐sparse signal recovery problem. The variance state propagation (VSP) framework is extended to solve such a problem. Considering that the delay, Doppler shift, and angle of departure (AoD) of each propagation path may not exactly fall on the presumed discrete grid points, an expectation maximization (EM) based parameter learning scheme in VSP to iteratively update the grid parameters is introduced. Simulation results demonstrate that the proposed approach achieves a substantial performance gain over the existing methods.

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Section: Channel Sparsity and Vsp Algorithmmentioning

confidence: 99%

Section: Channel Sparsity and Vsp Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Variance state propagation for uplink channel estimation in LEO satellite systems

Lei,

Zhang,

Liu

et al. 2023

Electronics Letters

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“…The work in [35] has designed a new architecture to enhance the overall channel gain in RIS-assisted LEO satellite networks by jointly optimising the transmit and receive beamforming. Further, Tekbiyik et al [36] have investigated bit error rate and achievable rate for RISassisted terahertz communication in LEO satellite networks. Xu et al [37] have also studied a physical layer security problem in RIS-assisted cognitive radio satellite-terrestrial integrated networks.…”

Section: A Recent Literature On Leo Satellite Networkmentioning

confidence: 99%

When RIS Meets GEO Satellite Communications: A New Sustainable Optimization Framework in 6G

Khan

Lagunas

Mahmood

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Low Earth Orbit (LEO) satellite networks are expected to play a crucial role in providing high-speed internet access and low-latency communication worldwide. However, some challenges can affect the performance of LEO satellite networks. For example, they can face energy and spectral efficiency challenges, such as high power consumption and spectral congestion, due to the increasing number of satellites. Furthermore, mobile ground users tend to operate with low directive antennas, which pose significant challenges in closing the LEO-toground communication link, especially when operating at a highfrequency range. To overcome these challenges, energy-efficient technologies like reconfigurable intelligent surfaces (RIS) and advanced spectrum management techniques like non-orthogonal multiple access (NOMA) can be employed. RIS can improve signal quality and reduce power consumption, while NOMA can enhance spectral efficiency by sharing the same resources among multiple users. This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals of the LEO satellite and the passive beamforming of RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47% and 54.9% higher energy efficiency compared to the benchmark and conventional frameworks.

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“…By deploying the IRS at the LEO satellite, which forms a high elevation angle with the user, the received SNR can be improved. In another study [16], the authors investigated the power gain for a constant bit error probability by exploiting multiple IRS-assisted satellites. Nevertheless, most studies in IRS-aided NTNs have not considered the Doppler effect, which has a different value with multiple subcarriers.…”

Section: Introductionmentioning

confidence: 99%

Intelligent reflecting surface‐aided Doppler compensation for Low‐Earth orbit satellite networks: Joint power allocation and passive beamforming optimisation

Lee

Toka

et al. 2023

IET Signal Processing

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Due to the reduction in costs of manufacture and launch of Low-Earth orbit (LEO) satellites, LEO satellite communication (SATCOM) has become a promising solution to provide high data rates and low latency connectivity for future communications. However, due to the fast movement of LEO satellites, it has to face a large Doppler effect, leading to high inter-carrier interference (ICI) power and severe received symbol error rate. To tackle this problem, a novel optimisation framework is proposed that boosts the achievable rate in LEO SATCOM networks by utilising passive beamforming of intelligent reflecting surface (IRS) to compensate for the Doppler effect. First, a carrierto-interference-noise ratio (CINR) was derived for each subcarrier and a joint optimisation problem of both power allocation is formulated to each subcarrier and passive beamforming of IRS. Moreover, a simultaneous iterative-water-filling-algorithm (SIWFA) and semidefinite programing (SDP) are utilised, thereby striking a balance that minimises ICI power and maximises desired signal power. Numerical results demonstrate the superior achievable rate performance of the proposed IRS-aided Doppler compensation for LEO SATCOM networks compared to benchmark schemes.

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Reconfigurable Intelligent Surfaces Empowered THz Communication in LEO Satellite Networks

Cited by 37 publications

References 34 publications

Variance state propagation for uplink channel estimation in LEO satellite systems

Variance state propagation for uplink channel estimation in LEO satellite systems

When RIS Meets GEO Satellite Communications: A New Sustainable Optimization Framework in 6G

Intelligent reflecting surface‐aided Doppler compensation for Low‐Earth orbit satellite networks: Joint power allocation and passive beamforming optimisation

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