Flexible Resource Allocation With Inter-Beam Interference in Satellite Communication Systems With a Digital Channelizer

Kawamoto, Yuichi; Kamei, Taiki; Takahashi, M.; Kato, Nei; Miura, Amane; Toyoshima, Morio

doi:10.1109/twc.2020.2969173

Cited by 32 publications

(18 citation statements)

References 34 publications

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“…The DRM cost function is proposed in (12), aiming at minimizing three parameters for each time instant t. The first parameter is the error between the offered capacity and the required capacity, where α (in s/b) is the weight of the error in the cost function. The second parameter to be minimized is total effective isotropic radiated power (EIRP), which is assigned to all the beams (in W), where β (in 1/W) is the weight of the total EIRP; the third parameter to be minimized is the total bandwidth (in Hz) that is assigned to the beams of each color (BW c ) within the frequency plan (N c is the number of colors in the frequency plan), and where γ (in 1/Hz or s) is the weight of the total bandwidth assigned in each color of the frequency plan.…”

Section: Drm Cost Functionmentioning

confidence: 99%

“…These possible configurations are coded in a vector of size L, representing the number of possible configurations of the payload resources. By exploiting this, the CNN has, in the output layer, the configuration that minimizes the cost function in (12) for the conditions of the input layer. This is shown in Fig.…”

Section: B Cnn For Drmmentioning

confidence: 99%

“…The full-connected layers are used to allocate the resource configuration with the features obtained in the convolution layers. The output layer results in a resource allocation that minimizes the DRM cost function (12).…”

Section: B Cnn For Drmmentioning

confidence: 99%

“…After the convolution layers, the data finally reach the fullconnected layers, where the data are then debugged; thus, these layers implement the resource configuration that minimizes the DRM cost function (12). The neurons in these layers work identically to those in a multilayer perceptron, where the output of each perceptron of each layer is calculated as [33]…”

Section: B Cnn For Drmmentioning

confidence: 99%

“…Thus, DRM techniques for SatCom will be a key for operators [11]. While it may seem feasible to achieve a solution to this problem through optimization techniques, on a larger scale, the number of resources to be managed, the constraints coming from the system and the infinite number of traffic demand situations may lead to a problem that cannot be solved by conventional techniques [12]. To solve this problem, Liu et al [13] suggested an assignment game-based dynamic power allocation (AG-DPA) to achieve suboptimal low complexity in multibeam satellite systems.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Ortíz

Tarchi

Martínez

et al. 2021

IEEE Systems Journal

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Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved.

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Section: Drm Cost Functionmentioning

confidence: 99%

Section: B Cnn For Drmmentioning

confidence: 99%

Section: B Cnn For Drmmentioning

confidence: 99%

Section: B Cnn For Drmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Ortíz

Tarchi

Martínez

et al. 2021

IEEE Systems Journal

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FlexBeamOpt: Hybrid solution methodologies for high‐throughput GEO satellite beam laydown and resource allocation

Gaudry

Mak‐Hau

2023

Satell Commun Network

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SummaryModern satellite communication systems are required to serve heterogeneous and geographically dispersed user demands with limited resources. In this paper, we investigate methodologies for dynamic resource allocation in Geosynchronous Earth Orbit (GEO) High‐throughput Satellite (HTS) systems. We designed three solution approaches FlexBeamOpt v1, FlexBeamOpt v2, and FlexBeamOpt v3, each as a hybridization of custom heuristics, integer linear programming, and/or constraint programming. We test the performance of the three approaches on 12 test instances that vary in user distribution (realistic, random, and clustered), user numbers (500 vs. 5000 users), and demand distribution (uniform vs. random). We observed that FlexBeamOpt v1 consistently outperformed FlexBeamOpt v2 and FlexBeamOpt v3 in terms of demand coverage and number of users covered for realistic and random user distribution test instances but at the cost of computation time. FlexBeamOpt v3 is the fastest in these instances. For clustered user distribution instances, FlexBeamOpt v3 performed better in terms of demand coverage and number of users covered, at the cost of using more beams. For these test instances, FlexBeamOpt v2 is the fastest in terms of computation time while providing a comparable solution quality.

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CG‐FlexBeamOpt: Advanced solution methodology for high throughput GEO satellite beam laydown and resource allocation

Li,

Gaudry,

Mak‐Hau

2024

Satell Commun Network

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SummaryModern satellite communication systems are designed to serve dispersed users with changing operational requirements. Allocating resources to meet these requirements becomes highly complex when the size, location, and allocated frequency of satellite beams are flexible. We developed solution techniques that generate feasible beam patterns and frequency allocations to maximize the total demand met across all users. In our approach, an integer program selects an optimal set of beams from a heuristically generated pool of feasible candidate beams. The challenge is in how to efficiently build a pool of good quality candidate beams from exponentially many possible solutions. An innovative column generation‐style heuristic to generate mathematically justifiable beams is presented to address this challenge. We also derived two other heuristic candidate beam generation algorithms to compare and contrast performance and robustness characteristics of different algorithmic choices. We tested the performance of our three new approaches on 12 operational instances that vary in user distribution, user numbers, and demand distribution. While the methods performed differently under varying operational scenarios, the column generation‐based methods provided the best trade‐off between computation time and solution quality in most cases. We further tested our two best performing algorithms for scalability. Our column generation‐based methods were able to provide good quality solutions with up to 400 beams and 5,000 users. Our work provides valuable insights for real‐life implementation: an end‐user of our system can select the solution approach based on their business need, computational (time) budget, and the desired solution quality.

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Flexible Resource Allocation With Inter-Beam Interference in Satellite Communication Systems With a Digital Channelizer

Cited by 32 publications

References 34 publications

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

FlexBeamOpt: Hybrid solution methodologies for high‐throughput GEO satellite beam laydown and resource allocation

CG‐FlexBeamOpt: Advanced solution methodology for high throughput GEO satellite beam laydown and resource allocation

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