Decentralized Baseband Processing for Massive MU-MIMO Systems

Achieving high spectral efficiency in realistic massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems requires computationally complex algorithms for data detection in the uplink (users transmit to base-station) and beamforming in the downlink (base-station transmits to user...

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Bibliographic Details
Published in:IEEE journal on emerging and selected topics in circuits and systems Vol. 7; no. 4; pp. 491 - 507
Main Authors: Li, Kaipeng, Sharan, Rishi R., Chen, Yujun, Goldstein, Tom, Cavallaro, Joseph R., Studer, Christoph
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
5G mobile communication
Algorithms
Alternating direction method of multipliers (ADMM)
Analog circuits
Antennas
Array signal processing
Bandwidths
Baseband
Beamforming
Circuits
Clusters
Complexity
Computation
conjugate gradient
data detection
equalization
Error analysis
Frequency division multiplexing
general-purpose computing on graphics processing unit (GPGPU)
Graphics processing units
Hardware
massive MU-MIMO
MIMO
MIMO communication
Network architecture
OFDM
ISSN:2156-3357, 2156-3365
Online Access:Get full text
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Summary:Achieving high spectral efficiency in realistic massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems requires computationally complex algorithms for data detection in the uplink (users transmit to base-station) and beamforming in the downlink (base-station transmits to user). Most existing algorithms are designed to be executed on centralized computing hardware at the base-station (BS), which results in prohibitive complexity for systems with hundreds or thousands of antennas and generates raw baseband data rates that exceed the limits of current interconnect technology and chip I/O interfaces. This paper proposes a novel decentralized baseband processing architecture that alleviates these bottlenecks by partitioning the BS antenna array into clusters, each associated with independent radio-frequency chains, analog and digital modulation circuitry, and computing hardware. For this architecture, we develop novel decentralized data detection and beamforming algorithms that only access local channel-state information and require low communication bandwidth among the clusters. We study the associated tradeoffs between error-rate performance, computational complexity, and interconnect bandwidth, and we demonstrate the scalability of our solutions for massive MU-MIMO systems with thousands of BS antennas using reference implementations on a graphics processing unit (GPU) cluster.
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ISSN:2156-3357
2156-3365
DOI:10.1109/JETCAS.2017.2775151