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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Vydáno v:	IEEE journal on emerging and selected topics in circuits and systems Ročník 7; číslo 4; s. 491 - 507
Hlavní autoři:	Li, Kaipeng, Sharan, Rishi R., Chen, Yujun, Goldstein, Tom, Cavallaro, Joseph R., Studer, Christoph
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Studer, Christoph Sharan, Rishi R. Chen, Yujun Li, Kaipeng Cavallaro, Joseph R. Goldstein, Tom
Author_xml	– sequence: 1 givenname: Kaipeng orcidid: 0000-0002-2069-2337 surname: Li fullname: Li, Kaipeng email: kl33@rice.edu organization: Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA – sequence: 2 givenname: Rishi R. surname: Sharan fullname: Sharan, Rishi R. email: rrs72@cornell.edu organization: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA – sequence: 3 givenname: Yujun surname: Chen fullname: Chen, Yujun email: yujun.chen@rice.edu organization: Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA – sequence: 4 givenname: Tom surname: Goldstein fullname: Goldstein, Tom email: tomg@cs.umd.edu organization: Department of Computer Science, University of Maryland, College Park, MD, USA – sequence: 5 givenname: Joseph R. orcidid: 0000-0002-9841-1806 surname: Cavallaro fullname: Cavallaro, Joseph R. email: cavallar@rice.edu organization: Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA – sequence: 6 givenname: Christoph orcidid: 0000-0001-8950-6267 surname: Studer fullname: Studer, Christoph email: studer@cornell.edu organization: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
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SubjectTerms	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
Title	Decentralized Baseband Processing for Massive MU-MIMO Systems
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