Training-Based Hybrid Precoding Scheme for Multiuser Massive MIMO-OFDM

Sun, Yiwei; Wang, Hua; Yuan, Minghao; Zhu, Tianye; Kawoya, Agnes

doi:10.1109/lcomm.2021.3108001

Cited by 3 publications

(7 citation statements)

References 15 publications

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“…In Fig. 3, SE avg related to the precoder from [15] is also shown, assuming single-antenna RXs and perfect channel estimation. This precoder performs worse compared to the MD_SPS_ZF precoders with the same connectivity constraint (i.e., FC).…”

Section: A Spectral Efficiencymentioning

confidence: 99%

“…Moreover, this precoder requires an iterative optimization, which results in a larger computational complexity than with PE-MD_ZF. Therefore, the precoder from [15] will no longer be considered in the sequel.…”

Section: A Spectral Efficiencymentioning

confidence: 99%

“…The optimization of A over the complex circle manifold, referred to as manifold optimisation (MO), has also been performed in the context of hybrid beamforming for scenarios involving intelligent reflective surfaces [9] and relay networks [10]. Other hybrid beamforming designs under the UM constraint avoid the complexity of manifold optimization by making use of a convexication of the optimization problem [4], [9], [11] or by heuristically applying phase extraction (PE) to determine A [2], [6], [7], [12]- [15]. The rate resulting from AltMin manifold optimization in the case of SC modulation turns out to be close to the performance of the all-digital beamformer; therefore, the former rate has been considered as a performance benchmark for UM-constrained hybrid precoders [2].…”

Section: Introductionmentioning

confidence: 99%

“…In [15] a different type of AltMin optimization is performed: the all-digital precoder and combiner are optimized alternatingly between the downlink and uplink until they converge, after which a PE is applied to a low-rank decomposition of the all-digital precoder to find A.…”

Section: Introductionmentioning

confidence: 99%

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Rate-Maximizing Zero-Forcing Hybrid Precoder for MU-MISO-OFDM

2023

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Hybrid precoders, consisting of an analog hardware-constrained part operating at radio frequency (RF) and a digital part operating at baseband, reduce the RF implementation complexity and power consumption of multi-antenna transceivers, at the expense of some rate loss compared to an all-digital precoder. The analog and digital parts of the hybrid precoder are commonly designed by performing a constrained matrix decomposition (MD) of the all-digital precoder, which aims to minimize the Euclidean distance between the matrices corresponding to the hybrid and the all-digital precoder. In contrast, in this contribution we determine the zero-forcing (ZF) hybrid precoder that directly maximizes the weighted sumrate of a MU-MISO-OFDM communication system, taking into account various hardware constraints on the analog part. The resulting maximum rate serves as a useful benchmark for comparison with other ZF hybrid precoders. In a multi-carrier massive MIMO scenario, the rate-maximizing ZF precoders show a considerable performance advantage over MD-type hybrid precoders, indicating that the latter precoders are far from optimum. This contribution also investigates the trade-off between performance and computational complexity. Because of the iterative nature of the rate-maximizing ZF hybrid precoders, their superior performance comes with a large computational complexity. When this complexity cannot be afforded, one should revert to the MD-type precoders, at the expense of a considerable performance penalty; among the MD-type precoders, the non-iterative ones have only a slightly worse performance but a significantly smaller computational complexity, in comparison with the iterative ones.

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Section: A Spectral Efficiencymentioning

confidence: 99%

Section: A Spectral Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rate-Maximizing Zero-Forcing Hybrid Precoder for MU-MISO-OFDM

2023

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“…For traditional MIMO systems, fully-digital beamforming is performed entirely in the digital domain, allowing for control over both the magnitude and the phase of the signals [10]. However, it requires a dedicated radio frequency (RF) chain per antenna [11]- [13], leading to significant hardware costs and power consumption, especially when deploying a large [10], [15], [17], [18], [20] [16] [19] [21]- [24] [25] [26], [27] [28]- [30] [31] [32]- [35] [36], [37] [38], [39] [40] Proposed…”

Section: Introductionmentioning

confidence: 99%