Reducing On-chip Memory for Massive MIMO Baseband Processing using Channel Compression

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


title = "Reducing On-chip Memory for Massive MIMO Baseband Processing using Channel Compression",
abstract = "Employing a large number of antennas at the base station, massive MIMO significantly improves spectral efficiency and transmit power efficiency. On the other hand, massive MIMO also introduces unprecedented implementation challenges, especially in terms of processing and storage of large-size channel state information (CSI) matrices. Since on-chip memory is generally very expensive and has limited storage capacity, this paper uses the concept of on-chip CSI data compression and decompression to reduce memory requirements during baseband processing. To achieve this, massive MIMO channel properties are explored using a hardware-friendly DFT-based compression algorithm. The proposed method is evaluated with measured channel data at 2.6 GHz using a 128-antenna linear array [1]. Simulation results show that aggressive CSI compression can be adopted without significant loss in communication performance, while the DFT-based compression can be conveniently integrated into the on-chip memory. This enables a large reduction of required on-chip memory, with negligible hardware overhead for compression/decompression.",
author = "Yangxurui Liu and Ove Edfors and Liang Liu and Viktor {\"O}wall",
year = "2018",
doi = "10.1109/VTCFall.2017.8288014",
language = "English",
pages = "1--5",
booktitle = "2017 IEEE 86th Vehicular Technology Conference: VTC2017-Fall",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",