Precoding for High-Throughput Satellite Communication Systems: A Survey

Khammassi, Malek; Kammoun, Abla; Alouini, Mohamed-Slim

doi:10.1109/comst.2023.3316283

Cited by 3 publications

(1 citation statement)

References 264 publications

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“…Optimizing MIMO technology, switching off subcarriers in orthogonal frequency division multiplexing [17,18], and applying multidimensional constellation diagrams [19,20] are examples of the channel-level methods. Finally, the source encoding level is related to various energy-efficient coding [21][22][23][24], energy-efficient redundancy coding [25] techniques, and image compression (both lossy and lossless) [26,27]. The rapid progress in multimedia and visual Internet of Things (IoT) technologies [28,29], coupled with the growing reliance on microsatellites and unmanned aerial vehicles (UAVs) for remote sensing of the Earth [30][31][32], highlight the need for improved image compression technologies.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy-Efficient Coding Based on Coordinated Group Signal Transformation for Image Compression in Energy-Starved Systems

Lopukhova,

Voronkov,

Kuznetsov

et al. 2024

Applied Sciences

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This paper introduces a new method for compressing images in energy-starved systems, like satellites, unmanned aerial vehicles, and Internet of Things nodes, which is based on coordinated group signal transformation (CGST). The transformation algorithm is a type of difference coding and may be classified as a non-transform-based image-compression method. CGST simplifies the difference signal conversion scheme using a single group codec for all signals. It considers color channels as correlated signals of a multi-channel communication system. The performance of CGST was evaluated using a dataset of 128 × 128 pixel images from satellite remote sensing systems. To adapt CGST to image compression, some modifications were introduced to the algorithm, such as fixing the procedure of the difference signals calculation to prevent any “zeroing” of brightness and supplementing the group codec with a neural network to improve the quality of restored images. The following types of neural networks were considered: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, fully connected neural networks are recommended if the goal is to minimize processing delay time. These networks have a response time of 13 ms. Conversely, suppose the priority is to improve quality in cases where delays are not critical. In that case, convolution neural networks in the Fourier space should be used, providing an image compression ratio of 4.8 with better minimum square error and Mikowsky norm values than JPEG with the same compression ratio.

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