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Sparse Regression Codes

Final Report Summary - SPARC (Sparse Regression Codes)

Project: Sparse Regression Codes
PI: Ramji Venkataramanan, University Lecturer, University of Cambridge (
Project website:

The aim of the project was to develop powerful low-complexity codes for communication and compression. Communication schemes usually constructed by combining a modulation technique with a binary error-correcting code. Though simple to implement, the rates delivered by this approach is far from optimal. One of the main ideas of the project was to step back from the coding/modulation divide, and instead the statistical framework of sparse linear regression to design codes.

In the project, we have designed codes for both communication and compression with near-optimal rates and low-complexity encoding and decoding algorithms. The research outputs of the project include new code designs, theoretical analysis of the proposed codes, and software implementation of the designed Sparse Regression Codes (SPARCs).

Specific research highlights of the projects include:
i) Design and rigorous analysis of a novel SPARC decoder based on `approximate message passing' (AMP),

ii) New algorithms for power allocation to optimize finite length error performance of SPARCs,

iii) Rigorous theoretical analysis of lossy compression using SPARCs, showing that SPARCs attain the optimal distortion vs rate trade-off for Gaussian sources,

iv) Novel SPARC-inspired techniques for high-dimensional statistical estimation, such as new variants of James-Stein estimators.

The CIG has helped the PI to grow his research group in Cambridge, and start new collaborations. In particular, the grant has funded one post-doc (Dr K. P. Srinath), part-funded one PhD student (A. Greig), and facilitated productive collaborations with Dr C. Rush ( Columbia University), Prof. A. Montanari (Stanford), and Prof. O. Johnson (Bristol). The work done in this project formed the basis for the PI's successful EPSRC First Grant proposal and a matching award from the Isaac Newton Trust. In summary, the CIG has been instrumental in jump-starting the PI's career in Cambridge, and the integration of the PI into the international research community in his field.