Large scale multiple-input multiple-output (MIMO) system is draining attention for its huge spectral efficiency. However, this is only applicable if the base-station is provided with an accurate channel-state information (CSI). In practical cases, the feedback channel is limited, especially when a large antenna array is used making CSI compression mandatory. Therefore, compressive sensing (CS) can be applied to massive MIMO systems, where spatial correlation between the antenna array elements is exploited to obtain sparse representations of the downlink channel. Moreover, the correlation among nearby users seeing similar scatterers can be utilized to recover the CSI at the base-station using distributed compressive sensing (DCS) with a limited amount of errors. Our unique link-level simulation shows the suitability of CS and DCS to reduce the amount of channel feedback to less than 37% and still achieves a very low symbol-error ratio (SER) error floor and high per-user capacity.
['Khaled Hassan', 'Martin Kurras', 'Lars Thiele']
Performance of distributed compressive sensing channel feedback in multi-user massive MIMO