Multi-panel extra-large scale MIMO based joint activity detection and channel estimation for near-field massive IoT access

The extra-large scale multiple-input multiple-output (XL-MIMO) for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service. However, the extremely large antenna array aperture arouses the channel near-field effect, resulting in the deteriorated data rate and other challenges in the practice communication systems. Meanwhile, multi-panel MIMO technology has attracted extensive attention due to its flexible configuration, low hardware cost, and wider coverage. By combining the XL-MIMO and multi-panel array structure, we construct multi-panel XL-MIMO and apply it to massive Internet of Things (IoT) access. First, we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios, where the electromagnetic waves corresponding to different panels have different angles of arrival/departure (AoAs/AoDs). Then, by exploiting the sparsity of the near-field massive IoT access channels, we formulate a compressed sensing based joint active user detection (AUD) and channel estimation (CE) problem which is solved by AMP-EM-MMV algorithm. The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.