SparSig Sparse Signal Processing in Wireless Communication

This project was concluded in 2013, but the site is still actively maintained. This project proposes a new way to sample, quantize, process and re-synthesize analog signals. Generally, the signals in many scientific areas are sparse in nature, which means that there is some structure and redundancy in the signals. If it is possible to transform a signal into a sparse domain in such a way that only the really needed information is processed then it is possible to save energy.

Project Overview

This project focuses on wireless communication and signals for such a system - specifically:

  1. in a communication receiver where a radio frequency signal must be sampled (down-converted) and processed, and
  2. to process real audio and video baseband signals.

The main contributions are to develop a framework for handling realistic signals (which are noisy, distorted etc.) and to provide a convincing validation (including experiments) of the achieved results. It is the objective to reduce the power consumption of current state-of-the-art techniques for converters and digital signal processing by at least 50% in the devices typically used in wireless communication systems.

The team is composed of two sections from Aalborg University (applied signal processing and RF signal techniques), one department from Lund University, Sweden (converters in integrated circuit design), and KU Leuven, Belgium (fundamental signal processing), as well as two industrial companies (Texas Instruments, Dallas, USA and Innovative RF which is a Danish start-up). In total four professors are involved in the project together with 3 PhD students and 2 postdocs in an international setting.

Detailed description can be found on: Aalborg University VBN

Project participants:

4GMCT project collaboration

The SparSig project is collaborating with another project called: 4th Generation Mobile Communication and Test Platform (4GMCT)

The project deals with concepts and electronic circuits to perform energy efficient sampling, quantization and processing of signals for wireless communication in a 4G communication systems. The purpose of the research project is to investigate the theoretical basis, analyze and propose solutions for how compressed sampling techniques can be implemented in communication receivers. The main issue is to propose solutions for sub-Nyquist sampling and quantization, as well as a reconstruction algorithm design - with the main objective to reduce the energy consumption in the signal processing. The concepts are to be tested in a radio frequency receiver for wireless communication purpose.

Project page: 4GMCT (VBN)

People involved in collaboration :

Matlab Frameworks

LTE toolbox

GPL-licensed, MATLAB®-based software which is able to generate LTE signals. The software’s main aim is to serve as a virtual LTE signal generator for any researcher or engineer who is interested in the LTE technology. The software is developed with compatibility with the official LTE specification in mind.

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MATLAB simulation framework dedicated to the article:

Sensitivity of the Random Demodulator Framework to Filter Tolerances

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CSP for DSSS 2012

MATLAB simulation framework dedicated to the article:

Demodulation of a Subsampled Direct Sequence Spread Spectrum Signal using Compressive Signal Processing

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MATLAB toolbox for error vector magnitude computations:

Robust Computation of Error Vector Magnitude based on Wireless Standards

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DFT Precoding

MATLAB scripts for downsampling DFT precoded signals.

Downsampling of DFT Precoded Signals for the AWGN Channel

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Algorihms for solving a sparse linear prediction problem with MATLAB interfaces.

Real-time Implementations of Sparse Linear Prediction for Speech Processing

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SCS via Interpolation

MATLAB simulation framework to the article:

Spectral Compressive Sensing with Polar Interpolation

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CPE of STI signals

MATLAB simulation framework to the article:

Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation

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Random Demodulator Calibration

MATLAB simulation framework to the article:

Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing

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Python Frameworks


Python simulation framework for compressive sensing reconstruction algorithms, used in the paper:

Surpassing the Theoretical 1-Norm Phase Transition in Compressive Sensing by Tuning the Smoothed l0 Algorithm

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CS for SS Receivers

Python simulation framework for compressive sensing for spread spectrum receivers, used in the paper:

Compressive Sensing for Spread Spectrum Receivers

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