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Fully Decentralized Massive MIMO Detection Based on Recursive Methods

Sánchez, Jesús Rodríguez LU ; Rusek, Fredrik LU ; Sarajlić, Muris LU ; Edfors, Ove LU and Liu, Liang LU (2019) 2018 IEEE Workshop on Signal Processing Systems, SiPS 2018 2018-October. p.53-58
Abstract

Algorithms for Massive MIMO uplink detection typically rely on a centralized approach, by which baseband data from all antennas modules are routed to a central node in order to be processed. In case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, this architecture leads to a bottleneck, with critical limitations in terms of interconnection bandwidth requirements. This paper presents a fully decentralized architecture and algorithms for Massive MIMO uplink based on recursive methods, which do not require a central node for the detection process. Through a recursive approach and very low complexity operations, the proposed algorithms provide a sequence of estimates that converge asymptotically to... (More)

Algorithms for Massive MIMO uplink detection typically rely on a centralized approach, by which baseband data from all antennas modules are routed to a central node in order to be processed. In case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, this architecture leads to a bottleneck, with critical limitations in terms of interconnection bandwidth requirements. This paper presents a fully decentralized architecture and algorithms for Massive MIMO uplink based on recursive methods, which do not require a central node for the detection process. Through a recursive approach and very low complexity operations, the proposed algorithms provide a sequence of estimates that converge asymptotically to the zero-forcing solution, without the need of specific hardware for matrix inversion. The proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.

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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
Decentralized, Detection and zero-forcing, Gradient Descent, Massive MIMO, Recursive Least Squares, Stochastic Approximation
host publication
Proceedings of the IEEE Workshop on Signal Processing Systems, SiPS 2018
volume
2018-October
pages
6 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
2018 IEEE Workshop on Signal Processing Systems, SiPS 2018
conference location
Cape Town, South Africa
conference dates
2018-10-21 - 2018-10-24
external identifiers
  • scopus:85061402433
ISBN
9781538663189
DOI
10.1109/SiPS.2018.8598321
language
English
LU publication?
yes
id
53f03fe8-2d75-4b96-aa10-2500cfa201dc
date added to LUP
2019-02-22 14:38:37
date last changed
2019-03-19 04:06:02
@inproceedings{53f03fe8-2d75-4b96-aa10-2500cfa201dc,
  abstract     = {<p>Algorithms for Massive MIMO uplink detection typically rely on a centralized approach, by which baseband data from all antennas modules are routed to a central node in order to be processed. In case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, this architecture leads to a bottleneck, with critical limitations in terms of interconnection bandwidth requirements. This paper presents a fully decentralized architecture and algorithms for Massive MIMO uplink based on recursive methods, which do not require a central node for the detection process. Through a recursive approach and very low complexity operations, the proposed algorithms provide a sequence of estimates that converge asymptotically to the zero-forcing solution, without the need of specific hardware for matrix inversion. The proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.</p>},
  author       = {Sánchez, Jesús Rodríguez and Rusek, Fredrik and Sarajlić, Muris and Edfors, Ove and Liu, Liang},
  isbn         = {9781538663189},
  keyword      = {Decentralized,Detection and zero-forcing,Gradient Descent,Massive MIMO,Recursive Least Squares,Stochastic Approximation},
  language     = {eng},
  location     = {Cape Town, South Africa},
  month        = {01},
  pages        = {53--58},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Fully Decentralized Massive MIMO Detection Based on Recursive Methods},
  url          = {http://dx.doi.org/10.1109/SiPS.2018.8598321},
  volume       = {2018-October},
  year         = {2019},
}