Title page for etd-0517116-154557


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URN etd-0517116-154557
Author Khumalo Mhlaliseni
Author's Email Address mkjohane@gmail.com
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Department Communications Engineering
Year 2015
Semester 2
Degree Master
Type of Document
Language English
Title Study on fixed point transformation of approximate message passing algorithm in massive MIMO systems
Date of Defense 2016-06-15
Page Count 94
Keyword
  • Massive MIMO detection
  • AMP algorithm
  • Hardware architecture
  • Word lengths
  • Fixed point
  • Damping
  • Log-sum approximation
  • Abstract In massive multiple input and multiple output (MIMO) systems the challenge is the detection of the individual signals from the composite signal in the large system limit. The optimal detector becomes prohibitively complex. The approximate message passing (AMP) algorithm, designed for compressed sensing, has attracted researchers to counter this problem due to its reduced complexity in the large system limit. For this reason the AMP algorithm has been used for detection in massive MIMO systems. In this thesis we focus on implementing this algorithm in fixed-point format. To obtain an implementation friendly architecture, we propose approximations for the mean and variance estimation functions within the algorithm. These estimation functions are obtained using the log-sum approximation then taking the exponent of the result. The log sum approximation is obtained by the Jacobean logarithm with a correction function recursively. We also provide a modification of the correction function for the approximations that best suits our case. We then substitute the mean and variance estimation functions with the approximations. The application of these approximations affect the convergence of the algorithm, to counter that we modify the initial damping strategy of the algorithm and plot a BER performance for the algorithm of the newly damped version. We then transform this algorithm to fixed point with an aim of obtaining word lengths for the variables. We finally provide a hardware architecture for the algorithm.
    Advisory Committee
  • Wan-Jen Huang - chair
  • Fan-Shuo Tseng - co-chair
  • Tong-Yu Hsieh - co-chair
  • Chao-Ka Wen - advisor
  • Files
  • etd-0517116-154557.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2016-06-17

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