Optimal Bit Allocation-Based Hybrid Precoder-Combiner Design Techniques for mmWave MIMO-OFDM Systems

This work conceives techniques for the design of hybrid precoders/combiners for optimal bit allocation in frequency selective millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, toward transmission rate maximization. Initially, th...

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Vydáno v:IEEE access Ročník 9; s. 54109 - 54125
Hlavní autoři: Majumder, Manjeer, Saxena, Harshit, Srivastava, Suraj, Jagannatham, Aditya K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Bayesian analysis
Bit rate
Computer architecture
frequency selective
hybrid precoder/ combiner
Machine learning
Matched pursuit
Matching pursuit algorithms
Millimeter wave
Millimeter waves
MIMO
MIMO communication
OFDM
optimal bit allocation
Optimization
Orthogonal Frequency Division Multiplexing
Radio frequency
Recovery
Signal reconstruction
Simulation
simultaneous orthogonal matching pursuit
sparse Bayesian learning
Subcarriers
Transceivers
ISSN:2169-3536, 2169-3536
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Shrnutí:This work conceives techniques for the design of hybrid precoders/combiners for optimal bit allocation in frequency selective millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, toward transmission rate maximization. Initially, the optimal fully digital ideal precoder/ combiner design is derived together with a closed-form expression for the optimal bit allocation in the above system. This is followed by the development of a framework for optimal transceiver design and bit allocation in a practical mmWave MIMO-OFDM implementation with a hybrid architecture. It is demonstrated that the pertinent problem can be formulated as a multiple measurement vector (MMV)-based sparse signal recovery problem for joint design of the RF and baseband components across all the subcarriers, and an explicit algorithm is derived to solve this using the simultaneous orthogonal matching pursuit (SOMP). To overcome the shortcomings of the SOMP-based greedy approach, an MMV sparse Bayesian learning (MSBL)-based state-of-the-art algorithm is subsequently developed, which is seen to lead to improved performance due to the superior sparse recovery properties of the Bayesian learning framework. Simulation results verify the efficacy of the proposed designs and also demonstrate that the performance of the hybrid transceiver is close to that of its fully-digital counterpart.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3070921