Compressive Sensing With Prior Support Quality Information and Application to Massive MIMO Channel Estimation With Temporal Correlation

@article{Rao2015CompressiveSW,
  title={Compressive Sensing With Prior Support Quality Information and Application to Massive MIMO Channel Estimation With Temporal Correlation},
  author={Xiongbin Rao and Vincent K. N. Lau},
  journal={IEEE Transactions on Signal Processing},
  year={2015},
  volume={63},
  pages={4914-4924}
}
In this paper, we consider the problem of compressive sensing (CS) recovery with a prior support and the prior support quality information available. Different from classical works which exploit prior support blindly, we shall propose novel CS recovery algorithms to exploit the prior support adaptively based on the quality information. We analyze the distortion bound of the recovered signal from the proposed algorithm and we show that a better quality prior support can lead to better CS… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 36 CITATIONS

Downlink Channel Estimation for Massive MIMO Systems Relying on Vector Approximate Message Passing

  • IEEE Transactions on Vehicular Technology
  • 2019
VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Modified CS-based downlink channel estimation with temporal correlation in FDD massive MIMO systems

  • 2018 Wireless Telecommunications Symposium (WTS)
  • 2018
VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Compressed Sensing-Aided Downlink Channel Training for FDD Massive MIMO Systems

  • IEEE Transactions on Communications
  • 2017
VIEW 16 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Exploiting Dynamic Sparsity for Downlink FDD-Massive MIMO Channel Tracking

  • IEEE Transactions on Signal Processing
  • 2019
VIEW 8 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Modified two-dimensional compressed sensing scheme for massive MIMO channel estimation

  • 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)
  • 2016
VIEW 3 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Compressive RF Training for Massive MIMO With Channel Support Side Information

  • IEEE Transactions on Wireless Communications
  • 2019
VIEW 1 EXCERPT
CITES METHODS

Learning-Based Remote Channel Inference: Feasibility Analysis and Case Study

  • IEEE Transactions on Wireless Communications
  • 2019
VIEW 1 EXCERPT
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 22 REFERENCES

Dynamic Compressive Sensing of Time-Varying Signals Via Approximate Message Passing

  • IEEE Transactions on Signal Processing
  • 2013
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Robust Recovery of Signals From a Structured Union of Subspaces

  • IEEE Transactions on Information Theory
  • 2008
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Decoding by linear programming

  • IEEE Transactions on Information Theory
  • 2005
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Adaptive Identification and Recovery of Jointly Sparse Vectors

  • IEEE Transactions on Signal Processing
  • 2014
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Structured Compressed Sensing: From Theory to Applications

  • IEEE Transactions on Signal Processing
  • 2011
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Modified-CS: Modifying Compressive Sensing for Problems With Partially Known Support

  • IEEE Transactions on Signal Processing
  • 2009
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers