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

Liu, Yangxurui LU ; Edfors, Ove LU ; Liu, Liang LU and Öwall, Viktor LU (2017) 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) In 2017 IEEE 86th Vehicular Technology Conference: VTC2017-Fall
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
in press
subject
in
2017 IEEE 86th Vehicular Technology Conference: VTC2017-Fall
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)
external identifiers
  • scopus:85045231937
DOI
language
English
LU publication?
yes
id
27cedd95-f65e-4cf5-9a0b-601a020d9ef4
date added to LUP
2017-07-10 15:09:41
date last changed
2018-05-29 11:09:46
@inproceedings{27cedd95-f65e-4cf5-9a0b-601a020d9ef4,
  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       = {Liu, Yangxurui and Edfors, Ove and Liu, Liang and Öwall, Viktor},
  booktitle    = {2017 IEEE 86th Vehicular Technology Conference: VTC2017-Fall},
  language     = {eng},
  pages        = {5},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Reducing On-chip Memory for Massive MIMO Baseband Processing using Channel Compression},
  url          = {http://dx.doi.org/},
  year         = {2017},
}