Scalable Distributed Massive MIMO Baseband Processing


Junzhi Gong, Harvard University; Anuj Kalia, Microsoft; Minlan Yu, Harvard University


Massive MIMO (multiple-in multiple-out) is a key wireless technique to get higher bandwidth in modern mobile networks such as 5G. The large amount of computation required for massive MIMO baseband processing poses a challenge to the ongoing softwarization of radio access networks (RAN), in which mobile network operators are replacing specialized baseband processing chips with commodity servers. Existing software-based systems for massive MIMO fail to scale to increasingly larger MIMO dimensions with an ever-increasing number of antennas and users. This paper presents a new scalable distributed system called Hydra, designed to parallelize massive MIMO baseband processing while minimizing the overhead of distributing computation over multiple machines. Hydra's high scalability comes from reducing inter-server and inter-core communication at different stages of baseband processing. To do so, among other techniques, we take advantage of hardware features in modern commodity radios in novel ways. Our evaluation shows that Hydra can support over four times larger MIMO configurations than prior state-of-the-art systems, handling for the first time, 150*32 massive MIMO with three servers.

NSDI '23 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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This content is available to:

@inproceedings {285088,
author = {Junzhi Gong and Anuj Kalia and Minlan Yu},
title = {Scalable Distributed Massive {MIMO} Baseband Processing},
booktitle = {20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
year = {2023},
isbn = {978-1-939133-33-5},
address = {Boston, MA},
pages = {405--417},
url = {},
publisher = {USENIX Association},
month = apr
Gong Paper (Prepublication) PDF

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