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Lousy processing increases energy efficiency in massive MIMO systems

Gunnarsson, Sara LU ; Bortas, Micaela; Huang, Yanxiang; Chen, Cheng-Ming; van Der Perre, Liesbet LU and Edfors, Ove LU (2017) European Conference on Networks and Communications In 2017 European Conference on Networks and Communications (EuCNC)
Abstract
Massive MIMO (MaMIMO) is a key technology for 5G wireless communication, enabling large increase in both spectral and energy efficiency at the same time. Before it can be deployed, it is important to find efficient implementation strategies. Because of the many antennas, an essential part of decreasing complexity, and further improving energy efficiency, is optimization of the digital signal processing (DSP) in the per-antenna functions. Assuming an orthogonal frequency-division multiplexing (OFDM) based MaMIMO system, this paper explores coarse quantization in the per-antenna digital transmit filters and inverse fast Fourier transforms (IFFTs) and evaluates it in terms of performance and complexity savings. Results show that DSP... (More)
Massive MIMO (MaMIMO) is a key technology for 5G wireless communication, enabling large increase in both spectral and energy efficiency at the same time. Before it can be deployed, it is important to find efficient implementation strategies. Because of the many antennas, an essential part of decreasing complexity, and further improving energy efficiency, is optimization of the digital signal processing (DSP) in the per-antenna functions. Assuming an orthogonal frequency-division multiplexing (OFDM) based MaMIMO system, this paper explores coarse quantization in the per-antenna digital transmit filters and inverse fast Fourier transforms (IFFTs) and evaluates it in terms of performance and complexity savings. Results show that DSP complexity can be greatly reduced per-antenna, and therefore significant power savings can be achieved, with limited performance degradation. More specifically, when going towards MaMIMO and therefore increasing the number of antennas from 8 to 64, it is possible to reduce the complexity in each transmit filter by 55%. Also, when using 6 bits to represent the input signal and 6 bits for the filter coefficients, this results in an SNR degradation of less than 0.5 dB compared to floating-point performance. Consequently, we conclude that the overall system energy greatly benefits from lousy per-antenna processing. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Massive MIMO, energy efficiency, digital signal processing, low accuracy, quantization
in
2017 European Conference on Networks and Communications (EuCNC)
pages
5 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
European Conference on Networks and Communications
external identifiers
  • scopus:85039925557
ISBN
978-1-5386-3873-6
DOI
10.1109/EuCNC.2017.7980739
language
English
LU publication?
yes
id
6642c041-12fb-41a6-9a79-d2b8698939b1
date added to LUP
2017-08-07 16:23:15
date last changed
2018-01-14 04:34:50
@inproceedings{6642c041-12fb-41a6-9a79-d2b8698939b1,
  abstract     = {Massive MIMO (MaMIMO) is a key technology for 5G wireless communication, enabling large increase in both spectral and energy efficiency at the same time. Before it can be deployed, it is important to find efficient implementation strategies. Because of the many antennas, an essential part of decreasing complexity, and further improving energy efficiency, is optimization of the digital signal processing (DSP) in the per-antenna functions. Assuming an orthogonal frequency-division multiplexing (OFDM) based MaMIMO system, this paper explores coarse quantization in the per-antenna digital transmit filters and inverse fast Fourier transforms (IFFTs) and evaluates it in terms of performance and complexity savings. Results show that DSP complexity can be greatly reduced per-antenna, and therefore significant power savings can be achieved, with limited performance degradation. More specifically, when going towards MaMIMO and therefore increasing the number of antennas from 8 to 64, it is possible to reduce the complexity in each transmit filter by 55%. Also, when using 6 bits to represent the input signal and 6 bits for the filter coefficients, this results in an SNR degradation of less than 0.5 dB compared to floating-point performance. Consequently, we conclude that the overall system energy greatly benefits from lousy per-antenna processing.},
  author       = {Gunnarsson, Sara and Bortas, Micaela and Huang, Yanxiang and Chen, Cheng-Ming and van Der Perre, Liesbet and Edfors, Ove},
  booktitle    = {2017 European Conference on Networks and Communications (EuCNC)},
  isbn         = {978-1-5386-3873-6 },
  keyword      = {Massive MIMO,energy efficiency, digital signal processing, low accuracy,quantization},
  language     = {eng},
  month        = {07},
  pages        = {5},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Lousy processing increases energy efficiency in massive MIMO systems},
  url          = {http://dx.doi.org/10.1109/EuCNC.2017.7980739},
  year         = {2017},
}