Compressed sensing channel estimation in massive MIMO

Compressed sensing channel estimation in massive MIMO

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Massive multiple-input–multiple-output (MIMO) emerges as a promising technology to meet the ever growing demand on a large volume of data transmission in an energy efficient manner in the upcoming fifth generation networks. This explores an integrated system model of improved wireless channel estimation (CE) and its consequent effect on image reconstruction, both done in compressed sensing (CS) framework. A massive MIMO channel exhibits sparse nature and has been modelled as a CS problem. Two CS-MIMO-CE algorithms, discrete cosine transform-based stagewise orthogonal matching pursuit and improved stagewise orthogonal matching pursuit (iStOMP), are suggested here. Low density parity check codes are also integrated to serve three-fold purposes, as a pilot signal, sensing matrix, and error control code, while Daubechies transform offers a sparse representation of the MIMO channel and transmitted image. The proposed iStOMP MIMO-CE algorithm offers gain in CE over the existing works and gain in 2 dB on image visual quality at 75% CS measurements.

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