Hybrid MMSE Precoding for Millimeter Wave MU-MISO via Trace Maximization

In an attempt to alleviate the cost and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems, hybrid precoding has been put forth as a possible solution. The hybrid precoding replaces the conventional fully digital precoding with a combination of analog and dig...

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Published in:IEEE transactions on wireless communications Vol. 23; no. 3; p. 1
Main Authors: Gautam, Prabhat Raj, Zhang, Li, Fan, Pingzhi
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1536-1276, 1558-2248
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Abstract In an attempt to alleviate the cost and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems, hybrid precoding has been put forth as a possible solution. The hybrid precoding replaces the conventional fully digital precoding with a combination of analog and digital precoding to achieve good performance using a significantly smaller number of RF chains compared to the number of antennas. In this paper, we consider a narrowband downlink multiple user multiple-input single-output (MU-MISO) system and propose a hybrid precoding method in order to minimize the mean squared error (MSE) for all users. The analog and digital precoding design subproblems are isolated from each other. Analog precoder design is cast as a trace maximization problem, and solved by an iterative procedure based on truncated singular value decomposition (SVD). The digital precoder is determined subsequently after fixing the analog precoder, using Lagrange's method. The proposed hybrid precoder produces spectral and bit error rate (BER) performances quite close to fully digital precoder, and almost the same as existing high performance hybrid precoders, albeit at a much lower complexity. The proposed precoding method is extended to operate in wideband channel, where it exhibits equally good performance with less complexity.
AbstractList In an attempt to alleviate the cost and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems, hybrid precoding has been put forth as a possible solution. The hybrid precoding replaces the conventional fully digital precoding with a combination of analog and digital precoding to achieve good performance using a significantly smaller number of RF chains compared to the number of antennas. In this paper, we consider a narrowband downlink multiple user multiple-input single-output (MU-MISO) system and propose a hybrid precoding method in order to minimize the mean squared error (MSE) for all users. The analog and digital precoding design subproblems are isolated from each other. Analog precoder design is cast as a trace maximization problem, and solved by an iterative procedure based on truncated singular value decomposition (SVD). The digital precoder is determined subsequently after fixing the analog precoder, using Lagrange's method. The proposed hybrid precoder produces spectral and bit error rate (BER) performances quite close to fully digital precoder, and almost the same as existing high performance hybrid precoders, albeit at a much lower complexity. The proposed precoding method is extended to operate in wideband channel, where it exhibits equally good performance with less complexity.
Author Fan, Pingzhi
Gautam, Prabhat Raj
Zhang, Li
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Snippet In an attempt to alleviate the cost and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems, hybrid precoding has been...
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SubjectTerms Antennas
Bit error rate
Complexity
Complexity theory
Hybrid power systems
hybrid precoding
Hybrid systems
Maximization
Millimeter wave (mmWave) communication
Millimeter wave communication
Millimeter waves
MIMO communication
minimum mean squared error (MMSE)
MISO (control systems)
Narrowband
Optimization
Power consumption
Precoding
Radio frequency
Singular value decomposition
trace maximization
Title Hybrid MMSE Precoding for Millimeter Wave MU-MISO via Trace Maximization
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Volume 23
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