Graduate Thesis Or Dissertation
 

Channel Estimation in TDD MU-MIMO Systems using Sub-Linear Sparse Signal Recovery Algorithms

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/sx61dr354

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  • Massive Multiple-User Miltiple-Input Multiple-Output (MU-MIMO) wireless communication systems incorporate promising advanced strong technologies for upcoming 5G communications. To obtain some of the high spectrum and energy e ciencies bonuses brought by MU-MIMO systems, the ability to obtain Channel State information, especially on the receiver side (CSI), is important. To minimize the amount of overhead and the complexity caused by the more common Frequency Division Duplexing standard, a Time Division Duplex scenario will be considered. The scenario will exploit the sparsity of the CSI in the angular domain to leverage compressive sensing techniques for channel estimation. The characteristics of the DFT matrix will be exploited to recover major channel components with sub-linear computational complexity. Simulation results will demonstrate that the algorithm can, with relatively low error, estimate the major components within a channel matrix while reducing the complexity of calculations.
  • Keywords: TDD, Channel Estimation, Sparse Signal Recovery Algorithms, 5G, MU-MIMO
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