M. Abdelghany, U. Madhow and A. Tölli, "Efficient Beamspace Downlink Precoding for mmWave Massive MIMO," 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2019, pp. 1459-1464, https://doi.org/10.1109/IEEECONF44664.2019.9048656
Efficient beamspace downlink precoding 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, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020043023602
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-04-30
We investigate efficient downlink precoding for all-digital downlink mmWave massive MIMO, with the number of users scaling with the number of antennas. The iterative computations required for optimal linear precoding are a severe bottleneck as the number of antennas increases, with the computational complexity per iteration scaling cubically with the number of antennas. In this paper, we propose a near-optimal linear precoding algorithm that exploits the sparsity of mmWave channels, employing a beamspace decomposition which limits the spatial channel seen by each user to a small window which does not scale with the number of antennas. This drastically reduces the complexity of computing the precoder, with complexity per iteration scaling linearly with the number of users, and makes it feasible to scale the system up to hundreds of antennas as considered in this paper.
Asilomar Conference on Signals, Systems & Computers
|Pages:||1459 - 1464|
2019 53rd Asilomar Conference on Signals, Systems, and Computers Nov 3-6, 2019 Pacific Grove, CA, USA
|Host publication editor:||
Matthews, Michael B.
Annual Asilomar Conference on Signals, Systems, and Computers
|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 (HR0011-18-3-0004). 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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