Stochastic Successive Convex Optimization for Two-Timescale Hybrid Precoding in Massive MIMO

Hybrid precoding, which consists of an RF precoder and a baseband precoder, is a popular precoding architecture for massive multiple-input multiple-output (MIMO) due to its low hardware cost and power consumption. In conventional hybrid precoding, both RF and baseband precoders are adaptive to the r...

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Vydané v:IEEE journal of selected topics in signal processing Ročník 12; číslo 3; s. 432 - 444
Hlavní autori: Liu, An, Lau, Vincent K. N., Zhao, Min-Jian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Baseband
Broadband
Computational geometry
Convexity
Massive multiple-input multiple-output (MIMO)
MIMO communication
Optimization
Power consumption
Precoding
Radio frequency
Real-time systems
Signal processing algorithms
Subcarriers
successive convex approximation
Time
two-timescale hybrid precoding
ISSN:1932-4553, 1941-0484
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Shrnutí:Hybrid precoding, which consists of an RF precoder and a baseband precoder, is a popular precoding architecture for massive multiple-input multiple-output (MIMO) due to its low hardware cost and power consumption. In conventional hybrid precoding, both RF and baseband precoders are adaptive to the real-time channel state information. As a result, an individual RF precoder is required for each subcarrier in wideband systems, leading to high implementation cost. To overcome this issue, two-timescale hybrid precoding (THP), which adapts the RF precoder to the channel statistics, has been proposed. Since the channel statistics are approximately the same over different subcarriers, only a single RF precoder is required in THP. Despite the advantages of THP, there lacks a unified and efficient algorithm for its optimization due to the nonconvex and stochastic nature of the problem. Based on stochastic successive convex approximation (SSCA), we propose an online algorithmic framework called SSCA-THP for general THP optimization problems, in which the hybrid precoder is updated by solving a quadratic surrogate optimization problem whenever a new channel sample is obtained. Then, we prove the convergence of SSCA-THP to stationary points. Finally, we apply SSCA-THP to solve three important THP optimization problems and verify its advantages over existing solutions.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2018.2819084