Design of Energy- and Cost-Efficient Massive MIMO Arrays

Puglielli, Antonio; Townley, Andrew; LaCaille, Greg; Milovanović, Vladimir; Lü, Pei; Trotskovsky, Konstantin; Whitcombe, Amy; Narevsky, Nathan; Wright, G. I.; Courtade, Thomas A.; Alon, Elad; Nikolić, Borivoje; Niknejad, Ali M.

doi:10.1109/jproc.2015.2492539

Cited by 128 publications

(72 citation statements)

References 57 publications

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“…A square matrix A ∈ C n×n is Hermitian if 20) where A H is the Hermitian transpose (sometimes refered to as conjugate transpose) of A [18]. The property…”

Section: Hermitian Matricesmentioning

confidence: 99%

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A scalable architecture for massive MIMO base stations using distributed processing

Bertilsson

Gustafsson

Larsson

2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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Massive MIMO is an emerging technology for future wireless systems that has received much attention from both academia and industry recently. The most prominent feature of Massive MIMO is that the base station is equiped with a large number of antennas. It is therefore important to create scalable architectures to enable simple deployment in different configurations.In this thesis, a distributed architecture for performing the baseband processing in a massive OFDM MU-MIMO system is proposed and analyzed. The proposed architecture is based on connecting several identical nodes in a K-ary tree. It is shown that, depending on the chosen algorithms, all or most computations can be performed in a distrbuted manner. Also, the computational load of each node does not depend on the number of nodes in the tree (except for some timing issues) which implies simple scalability of the system.It is shown that it should be enough that each node contains one or two complex multipliers and a few complex adders running at a couple of hundres MHz to support specifications similar to LTE. Additionally the nodes must communicate with each other over links with data rates in the order of some Gbps.Finally, a VHDL implementation of the system is proposed. The implementation is parameterized such that a system can be generated from a given specification. Nyckelord KeywordsElektroteknik, ASIC, Massiv MIMO AbstractMassive MIMO is an emerging technology for future wireless systems that has received much attention from both academia and industry recently. The most prominent feature of Massive MIMO is that the base station is equiped with a large number of antennas. It is therefore important to create scalable architectures to enable simple deployment in different configurations.In this thesis, a distributed architecture for performing the baseband processing in a massive OFDM MU-MIMO system is proposed and analyzed. The proposed architecture is based on connecting several identical nodes in a K-ary tree. It is shown that, depending on the chosen algorithms, all or most computations can be performed in a distrbuted manner. Also, the computational load of each node does not depend on the number of nodes in the tree (except for some timing issues) which implies simple scalability of the system.It is shown that it should be enough that each node contains one or two complex multipliers and a few complex adders running at a couple of hundres MHz to support specifications similar to LTE. Additionally the nodes must communicate with each other over links with data rates in the order of some Gbps.Finally, a VHDL implementation of the system is proposed. The implementation is parameterized such that a system can be generated from a given specification.iii

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“…A square matrix A ∈ C n×n is Hermitian if 20) where A H is the Hermitian transpose (sometimes refered to as conjugate transpose) of A [18]. The property…”

Section: Hermitian Matricesmentioning

confidence: 99%

“…The authors in [19,20] has proposed a system where the processing is distributed across several identical modules. These modules are connected in an array structure.…”

Section: Node Interconnection Topologymentioning

confidence: 99%

A scalable architecture for massive MIMO base stations using distributed processing

Bertilsson

Gustafsson

Larsson

2016

2016 50th Asilomar Conference on Signals, Systems and Computers

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show abstract

“…However, implementation results on a GPU cluster revealed that the transfer latency of such consensus-sharing methods are limiting the achievable throughput. To avoid this drawback, references [1], [7], [12], [21] recently proposed feedforward architectures that minimize the transfer latency.…”

Section: A Decentralized Baseband Processingmentioning

confidence: 99%

Decentralized Equalization With Feedforward Architectures for Massive MU-MIMO

Jeon

Cavallaro

et al. 2019

IEEE Trans. Signal Process.

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Linear data-detection algorithms that build on zero forcing (ZF) or linear minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral efficiency in massive multi-user multiple-input multiple-output (MU-MIMO) systems. Such algorithms, however, typically rely on centralized processing at the base-station (BS) which results in (i) excessive interconnect and chip input/output (I/O) data rates and (ii) high computational complexity. Decentralized baseband processing (DBP) partitions the BS antenna array into independent clusters that are associated with separate radio-frequency circuits and computing fabrics in order to overcome the limitations of centralized processing. In this paper, we investigate decentralized equalization with feedforward architectures that minimize the latency bottlenecks of existing DBP solutions. We propose two distinct architectures with different interconnect and I/O bandwidth requirements that fuse the local equalization results of each cluster in a feedforward network. For both architectures, we consider maximum ratio combining, ZF, L-MMSE, and a nonlinear equalization algorithm that relies on approximate message passing. For these algorithms and architectures, we analyze the associated post-equalization signal-to-noise-and-interference-ratio (SINR). We provide reference implementation results on a multi graphics processing unit (GPU) system which demonstrate that decentralized equalization with feedforward architectures enables throughputs in the Gb/s regime and incurs no or only a small performance loss compared to centralized solutions.Index Terms-Data detection, decentralized baseband processing, linear and nonlinear equalization, general-purpose computing on graphics processing units (GPGPU), massive MU-MIMO.

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“…To meet ever increasing demands for higher data rates and larger capacity, new modulation schemes have been developed and wider frequency bands, e.g., at mmwave, have been designated to 5G [1], [2]. Massive multi-input multi-output (MIMO), that uses a large number of antennas at the transmitter and receiver, has been considered as one of the key enabling technologies in 5G to improve data throughput and spectrum efficiency [3]. These new application scenarios pose stringent requirements on the wireless transceiver frontends and call for special considerations at both circuit and system design levels.…”

Section: Introductionmentioning

confidence: 99%