On the Modelling of Coarse Vector Quantization in Distributed Massive MIMO

TY - GEN

T1 - On the Modelling of Coarse Vector Quantization in Distributed Massive MIMO

AU - Burr, Alister

AU - Maryopi, Dick

N1 - Funding Information: The work described in this paper was supported in part by Indonesia Endowment Fund for Education (LPDP), and in part by the EU Horizon 2020 Programme, Project SPOTLIGHT (MCSA-ITN-2016-722788) Publisher Copyright: © 2021 IEEE.

PY - 2021/8/19

Y1 - 2021/8/19

N2 - We consider coarse vector quantization for the fronthaul of distributed or cell-free massive MIMO, which is also related to Cloud Radio Access Network (C-RAN) architecture. We propose the use of lattice vector quantization (LVQ) based on Voronoi codes, and discuss how they can be adapted for efficient quantization of correlated signals, using a near-ellipsoidal Voronoi codebook. The Bussgang decomposition is extended to model these quantizers, and some numerical results obtained. A comparison of the quantization error with that of the Linde-Buzo-Gray (LBG) shows that for strongly correlated signals, while simple Voronoi LVQ has around 3 dB poorer performance, the near-ellipsoidal codebook is very close to LBG.

AB - We consider coarse vector quantization for the fronthaul of distributed or cell-free massive MIMO, which is also related to Cloud Radio Access Network (C-RAN) architecture. We propose the use of lattice vector quantization (LVQ) based on Voronoi codes, and discuss how they can be adapted for efficient quantization of correlated signals, using a near-ellipsoidal Voronoi codebook. The Bussgang decomposition is extended to model these quantizers, and some numerical results obtained. A comparison of the quantization error with that of the Linde-Buzo-Gray (LBG) shows that for strongly correlated signals, while simple Voronoi LVQ has around 3 dB poorer performance, the near-ellipsoidal codebook is very close to LBG.

KW - Bussgang decomposition

KW - C-RAN

KW - distributed massive MIMO

KW - Vector quantization

UR - http://www.scopus.com/inward/record.url?scp=85113570422&partnerID=8YFLogxK

U2 - 10.1109/SSP49050.2021.9513792

DO - 10.1109/SSP49050.2021.9513792

M3 - Conference contribution

AN - SCOPUS:85113570422

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 346

EP - 350

BT - 2021 IEEE Statistical Signal Processing Workshop, SSP 2021

PB - IEEE Computer Society

T2 - 21st IEEE Statistical Signal Processing Workshop, SSP 2021

Y2 - 11 July 2021 through 14 July 2021

ER -