Low-complexity incremental search-aided hybrid precoding and combining for massive MIMO systems

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dc.contributor.authorBahingayi, Eduard E.
dc.contributor.authorLeeLee, Kyungchun
dc.date.accessioned2023-10-12T13:58:03Z
dc.date.available2023-10-12T13:58:03Z
dc.date.issued2020
dc.descriptionAbstract. Full text article available at https://doi.org/10.1109/ACCESS.2020.2986390en_US
dc.description.abstractThe hybrid precoding and combining algorithms for mmWave massive multiple-input multiple-output (MIMO) systems must consider the trade-off between the complexity and performance of the system. Unfortunately, because of the unit-norm constraint imposed by the use of phase shifters, the optimization of the radio frequency (RF) precoder and combiner becomes a non-convex problem. As a consequence, the algorithm for hybrid precoding and combining design often incurs high complexity. This paper proposes a dictionary-constrained low-complexity algorithm for hybrid precoding and combining design. The proposed algorithm considers a decoupled optimization scheme between the RF and baseband domains for the spectral efficiency-maximization problem. In the RF domain, we propose an incremental successive selection method to find a subset of array response vectors from a dictionary, which forms the RF precoding/combining matrices. For the digital domain, we employ singular-value decomposition (SVD) of the low-dimensional effective channel matrix to generate the digital baseband precoder and combiner. Through numerical simulation, we show that the proposed algorithm achieves near-optimal performance while providing approximately up to 99% complexity reduction compared to the conventional hybrid precoding and combining algorithms.n this study, we propose a joint hybrid-precoding algorithm for multiuser multiple input single-output downlink systems. Specifcally, we consider that the base station employs an energy-efcient hybrid-precoding subconnected (SC) architecture with fxed equal subarrays (FESA) (SC-FESA). Optimizing the analog precoding matrix in an SC-FESA architecture is challenging due to its unique constraint structure. In this study, to maximize system sum rate, we propose an efcient method to transform the system’s sum-rate optimization problem into a continuous and diferentiable objec tive function wherein only the nonzero elements of the analog precoding matrix are optimized. For the formulated problem, we develop an alternating optimization (AO) approach to jointly optimize the digital and analog precoders in succession by maximizing the system’s sum rate. Specifcally, in the proposed AO method, when the digital precoder is fxed, we employ the Riemannian conjugate gradient algorithm to generate the analog precoder. Furthermore, when the analog precoder is fxed, we use the minimum mean squared error method to obtain the digital precoder. Numerical simulation results show that the proposed AO algorithm improves the sum rate and energy efciency of the SC-FESA architecture compared to existing algorithms.en_US
dc.identifier.citationBahingayi, E. E., & Lee, K. (2020). Low-complexity incremental search-aided hybrid precoding and combining for massive MIMO systems. IEEE Access, 8, 66867-66877.en_US
dc.identifier.otherDOI: https://doi.org/10.1109/ACCESS.2020.2986390
dc.identifier.otherURL: https://ieeexplore.ieee.org/abstract/document/9058676
dc.identifier.urihttp://hdl.handle.net/20.500.12661/4191
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectMillimeter waveen_US
dc.subjectMultiple-input multiple-outputen_US
dc.subjectMIMOen_US
dc.subjectMassive MIMOen_US
dc.subjectmMIMOen_US
dc.subjectPrecodingen_US
dc.subjectArray response vectorsen_US
dc.subjectSubset selectionen_US
dc.subjectMIMO communicationen_US
dc.titleLow-complexity incremental search-aided hybrid precoding and combining for massive MIMO systemsen_US
dc.typeArticleen_US
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