Adaptive Beam Pattern Selection and Resource Allocation for NOMA-Based LEO Satellite Systems

Wang, Anyue; Lei, Lei; Hu, Xin; Lagunas, Eva; Pérez-Neira, Ana I.; Chatzinotas, Symeon

doi:10.1109/globecom48099.2022.10000986

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…As an early-attempt study in [20], we evaluate the performance gain of adopting adaptive beam pattern in LEO satellite systems under pre-decided user scheduling. For extension, we are motivated to provide more comprehensive insights on joint resource optimization of beam pattern selection and resource allocation and aim to analyze their mutual influence, which distinguish this work from other state-of-the-art works [10], [12], [14], [15].…”

Section: B Motivations and Contributionsmentioning

confidence: 99%

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Spatial–Temporal Resource Optimization for Uneven-Traffic LEO Satellite Systems: Beam Pattern Selection and User Scheduling

Lei,

Wang,

Lagunas

et al. 2024

IEEE J. Select. Areas Commun.

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With the commercial deployment of low earth orbit (LEO) satellites, the future integrated 6G-satellite system represents an excellent solution for ubiquitous connectivity and highthroughput data service to massive users. Due to the heterogeneity of users' traffic profiles, uneven traffic distribution among beams or users often occurs in LEO satellite systems. Conventional satellite payloads with fixed beam radiation patterns may result in large gaps between requested and allocated capacity. The advances of flexible satellite payloads with dynamic beamforming capabilities enable spot beams to adjust their coverage and adaptively schedule users, thus offering spatial-temporal domain flexibility. Motivated by this, as an early attempt, we investigate how adaptive beam patterns with flexible user scheduling schemes can help alleviate mismatches of requested-transmitted data in uneven-traffic and full-frequency reuse LEO systems. We formulate an optimization problem to jointly determine beam patterns, power allocation, user-LEO association, and user-slot scheduling. The problem is identified as mixed-integer nonconvex programming. We propose an efficient iterative algorithm to solve the problem by first determining beam patterns and user associations at the frame scale, followed by optimizing power allocation and user scheduling at the timeslot scale. The fourdecision components are iteratively updated to improve the overall performance. Numerical results demonstrate the benefits brought by adaptive beam patterns and their effectiveness in reducing the mismatch effect in uneven-traffic LEO systems.

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Section: B Motivations and Contributionsmentioning

confidence: 99%

“…where L sf sk denotes shadow fading following log-normal distribution. L sk denotes the free-space path loss, which is expressed by [20],…”

Section: System Modelmentioning

confidence: 99%

Spatial–Temporal Resource Optimization for Uneven-Traffic LEO Satellite Systems: Beam Pattern Selection and User Scheduling

Lei,

Wang,

Lagunas

et al. 2024

IEEE J. Select. Areas Commun.

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“…Gamal et al [23] have derived a closed-form expression for outage probability in cooperative NOMA satellite networks. The work in [24] has proposed a joint resource optimization for minimizing the capacity demand gap in NOMA LEO satellite networks. Zhao et al [25] have derived closed-form solutions for the achievable and asymptotic outage probability of satellite networks under hardware impairment and imperfect signal decoding.…”

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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Joint User Identification, Channel Estimation, and Data Detection for Grant-Free NOMA in LEO Satellite Communications

Zhang,

Liu,

et al. 2025

IEEE J. Select. Areas Commun.

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Adaptive Beam Pattern Selection and Resource Allocation for NOMA-Based LEO Satellite Systems

Cited by 6 publications

References 16 publications

Spatial–Temporal Resource Optimization for Uneven-Traffic LEO Satellite Systems: Beam Pattern Selection and User Scheduling

Spatial–Temporal Resource Optimization for Uneven-Traffic LEO Satellite Systems: Beam Pattern Selection and User Scheduling

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

Joint User Identification, Channel Estimation, and Data Detection for Grant-Free NOMA in LEO Satellite Communications

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