M. Abdelghany, U. Madhow and A. Tölli, "Beamspace Local LMMSE: An Efficient Digital Backend for mmWave Massive MIMO," 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, 2019, pp. 1-5. doi: 10.1109/SPAWC.2019.8815585
Beamspace local LMMSE : an efficient digital backend for mmWave massive MIMO
|Author:||Abdelghany, Mohammed1; Madhow, Upamanyu1; Tölli, Antti2|
1Department of ECE, University of California at Santa Barbara, Santa Barbara, CA 93106 USA
2Centre for Wireless Communications, University of Oulu, P.O. Box 4500, 90014, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202001081454
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-01-08
We explore an all-digital architecture for a mmWave massive MIMO cellular uplink in which the number of users scales with the number of antenna elements at the base station. We consider the design of multiuser detection strategies after a spatial DFT, which concentrates the energy of each user onto a few DFT bins in “beamspace.” In this paper, we propose and investigate a local LMMSE receiver that exploits this property, using a small window in beamspace to demodulate each user. The proposed architecture is computationally efficient: the required window size depends on load factor (the number of users divided by the number of antenna elements) and does not scale with the number of elements. We also show that adaptive implementations of such local LMMSE receivers naturally extend to provide implicit channel estimation.
SPAWC. Signal processing advances in wireless communications
20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2019, 2-5 July 2019, Cannes, France
IEEE International Workshop on Signal Processing Advances in Wireless Communications
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Semiconductor Research Corporation (SRC) under the JUMP program (2018-JU-2778) and by DARPA (HR00II-18-3-0004). Use was made of the computational facilities administered by the Center for Scientific Computing at the CNSI and MRL (an NSF MRSEC; DMR-1720256) and purchased through NSF CNS-1725797.
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