Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

Article Engineering, Electrical & Electronic

On Infinite Past Predictability of Cyclostationary Signals

Jaume Riba et al.

Summary: This paper explores the asymptotic spectral decomposition of periodically Toeplitz matrices with finite summable elements. It proposes a method leveraging on the Toeplitz structure of cyclic autocorrelation matrices, providing a more direct link to the cyclic spectrum and spectral coherence. These results are experimentally tested in a prediction setting.

IEEE SIGNAL PROCESSING LETTERS (2022)

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Article Environmental Sciences

Compression of Multibeam Echosounders Bathymetry and Water Column Data

Aniol Marti et al.

Summary: Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. In this paper, an algorithm for decorrelating water column and bathymetry data is proposed, and it is integrated into the FAPEC data compressor for preprocessing. The results show that FAPEC achieves good compression ratios and quality of reconstructed water column images at high speeds.

REMOTE SENSING (2022)

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Article Computer Science, Information Systems

Spectrum Sensing Using Software Defined Radio for Cognitive Radio Networks: A Survey

Julio Manco et al.

Summary: This survey provides an overview of the latest works in cognitive radio networks related to spectrum awareness approaches and their implementation on testbeds. The existing works for each category are reviewed in detail, and the features of current SDR platforms used for experimental evaluation of CR approaches are highlighted. The challenges to implement current spectrum awareness approaches on SDR platforms are also discussed. Based on this review, research challenges and open issues are identified for future research directions.

IEEE ACCESS (2022)

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Proceedings Paper Acoustics

SINGULAR VALUE DECOMPOSITION FOR COMPRESSION OF LARGE-SCALE RADIO FREQUENCY SIGNALS

R. David Badger et al.

Summary: The paper introduces an efficient matrix factorization-based approach for large-scale radio frequency (RF) signal compression tasks, utilizing singular value decomposition (SVD) to reduce the dimension of time-frequency representation and achieve fast and reliable compression results with acceptable reconstruction error.

29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021) (2021)

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Article Environmental Sciences