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Distributed Sparse Signal Recovery

Rahimpour, Amir | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52673 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Marvasti, Farrokh
  7. Abstract:
  8. Sensor Networks are set of devices which are distributed throughout an environment and are connected to each other, usually wirelessly, to collect environmental information including temperature, aire pressure, moist, pollution and physiological functions of the human body. Each device consists of a microprocessor, converter and power supply, transmitter and a receiver. In this study we intend to investigate such setup and the measured signals assuming they are sparse. A sparse signal is a discrete time signal most of indices of which are equal to zero. With this assumption at hand, we will be able to reduce the sampling rate and take advantage of sparse signal processing techniques. This problem could be formulated in two ways, first, each device sends its measurements to a central node where a centralized processing would take care of the recovery operations. The other scenario which constitutes the main concern of this study is based on the assumption that each device has to take part in the recovery operation and send its results to the adjacent nodes to gradually an estimation of the measured signal could be achieved. In this scenario there will be no need to for a central processor and the entire processing task will be performed by weaker, and hence cheaper processors which are available at every node. For this purpose a new distributed algorithm based on IMAT and ADMM algorithms is proposed to address the problem of distributed sparse signal recovery. the performance of the algorithm is tested in various scenarios including variable sampling rate, changing input signal and different network graphs. The proposed method outperforms the similar algorithms in most scenarios with a considerable margin
  9. Keywords:
  10. Sparse Signal Reconstruction ; Distributed System ; Wireless Sensor Network ; Compressive Sensing ; Signal Recovery ; Iteration Method

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