PhD project: Sparse Signal Processing in Wireless Communications (SparSig)

Project Details


This project aims to implement compressive sensing technology to sample, process and reconstruct realistic communication signals as well as audio and video baseband signals. Conventional approaches in sampling and reconstruction of signals follow the classic Shannon’s celebrated theorem. However, the new theory of sparse signal processing suggests that sensors only sample a few key useful components of the signals and complete the sampling and compressing data simultaneously.

This project focuses on developing practical sampling and recovering algorithm for realistic communication signals in proper sparse basis. Novel transceiver will be designed to sample and process audio and video baseband signals, which are noisy and distorted in real environment, according to the compressive sensing theory. The theoretical findings will be experimentally proved by implementing a prototype with the key electronics to show measured results.

This project is a part of the Sparse Signal Processing in Wireless Communications (SparSig) project. There is a cooperative work with 2 other PhD students, 2 postdocs and four professors in an international setting

Effective start/end date01/06/201031/05/2013


  • Research Output

    • 1 Article in proceeding

    Extended Reconstruction Approaches for Saturation Measurements Using Reserved Quantization Indices

    Li, P., Arildsen, T. & Larsen, T., 2012, Proceedings of the 12th IEEE International Symposium on Signal Processing and Information Technology. IEEE Press, p. 198-202 5 p.

    Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

    Open Access
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