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.
Bibliographical noteFunding Information:
This work was supported by National Key Research and Development Program of China under Grants 2021YFB1600500, 2021YFB3201502, and 2022YFB3207704, Natural Science Foundation of China (NSFC) under Grants U2233216, 62071044, 61827901, 62088101 and 62201056, supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62, and supported by Beijing Nova Program, Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009
© 2013 China Institute of Communications.
- active user detection
- approximate message passing
- channel estimation
- extra-large scale MIMO
- massive IoT access
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering