Codeword Selection and Hybrid Precoding for Multiuser Millimeter-Wave Massive MIMO Systems

Sun, Xuyao; Qi, Chenhao

doi:10.1109/lcomm.2018.2889077

Cited by 16 publications

(5 citation statements)

References 10 publications

(25 reference statements)

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“…In [20], a flexible adaptive overlapped subarray architecture was proposed for analog precoder to maximize energy efficiency. In [21], to maximize the achievable sum-rate, a novel hybrid precoding scheme was presented by introducing interference-free achievable rate. To balance the energy efficiency and the system sum-rate when it is set that the analog phaseshifter is under two-bits resolution, a low-complexity hybrid precoding was proposed in [22].…”

Section: B Multi-user Massive Mimo Systemsmentioning

confidence: 99%

Hybrid Precoding for Multi-user Massive MIMO Systems Based on Equivalent Channel

Wang

Zhao

et al. 2023

Preprint

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Utilizing millimeter-wave (mmWave) band with massive MIMO architecture is deemed as an available paradigm for the future 6G wireless systems to cater the increasing demands in communication rates, in which hybrid precoding is a promising solution to reduce hardware cost and power consumption while maintains almost the same spectrum efficiency. In this paper, for multi-user massive MIMO systems, a new hybrid precoding scheme is presented based on the concept of equivalent channel by designing the analog precoder at the transmitter and the analog combiner at the receiver jointly. Specifically, after the optimal analog precoder/combiner for each user has been derived based on the equivalent channel of the individual sub-channel via exploiting the singular value decomposition (SVD) technique, the total baseband digital precoding is implemented with the block diagonalization method to suppress inter-user interference. In this way, not only high spectrum efficiency can be achieved, but also the computational complexity to realize hybrid precoding is decreased. To further verify the superior performance of the proposed hybrid precoding, simulation results are presented with different parameter settings.

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Section: B Multi-user Massive Mimo Systemsmentioning

confidence: 99%

Hybrid Precoding for Multi-user Massive MIMO Systems Based on Equivalent Channel

Wang

Zhao

et al. 2023

Preprint

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“…Another approach to reducing the beam training overhead is hierarchical beam training, which uses a fast codeword selection strategy based on a hierarchical codebook [38][39][40][41][42][43][44]. The hierarchical codebook has multiple layers with increasing numbers of codewords from the upper to the lower layers.…”

Section: Hierarchical Beam Trainingmentioning

confidence: 99%

Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

Dong

et al. 2021

Sci. China Inf. Sci.

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The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications. In this article, we provide an overview on CSI acquisition, including beam training and channel estimation for mmWave massive multiple-input multiple-output systems. The beam training can avoid the estimation of a high-dimension channel matrix, while the channel estimation can flexibly exploit advanced signal processing techniques. In addition to introducing the traditional and machine learning-based approaches in this article, we also compare different approaches in terms of spectral efficiency, computational complexity, and overhead.

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“…Typically, when wireless communication devices are equipped with large antenna arrays, communication protocols are based on signal processing techniques such as precoding and combining. The precoding and combining matrices must be developed from complete knowledge of channel state information (CSI) decomposition for attaining optimal results similar to the ideal performance [11]- [13]. This will enable numerous independently controlled beams to be generated with the highest gains.…”

Section: Introductionmentioning

confidence: 99%

Joint Hybrid Precoding and Combining Design Based Multi-Stage Compressed Sensing Approach for mmWave MIMO Channel Estimation

Hadji,

Aïssa-El-Bey,

Fergani

et al. 2023

IEEE Access

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Although the design of hybrid precoders and combiners separately from the complete channel state information (CSI) offers satisfactory performance, the resulting spatial multiplexing channel may not always be orthogonal during communication. Also, acquiring CSI to design optimal precoders and combiners poses several challenges, particularly in millimeter wave (mmWave) channel estimation, and getting the sensing matrix is equivalent to designing the precoders and combiners. For this, we propose a new iterative method based on alternating minimization to design the optimal sensing matrix (incoherent projection matrix) with the given dictionary to minimize the mutual coherence values (µ mx , µ ave and µ all ) simultaneously according to the equiangular tight frame (ETF) properties for achieving better-compressed sensing (CS) recovery performance. Then, in order to derive the best hybrid precoders and combiners jointly from the optimally designed sensing matrix, we formulate the optimization design problem as the nearest Kronecker product (NKP) problem. The proposed sensing matrix design works better at lowering the mutual coherence values concurrently with the straightforward shrinkage function, according to simulation findings of mutual coherence values evolution versus outer iteration numbers. In comparison to existing codebookbased hybrid precoder/combiner schemes, the proposed joint hybrid precoder and combiner design improves the performance of the simulation results obtained by multi-stage CS-based mmWave channel estimation in terms of channel estimation accuracy and spectral efficiency (SE).INDEX TERMS Millimeter-wave channel estimation, Multi-stage CS approach, Hybrid mmWave MIMO transceiver, Joint hybrid precoder and combiner design, Equiangular tight frame, Mutual coherence values, Incoherent projection matrix.

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Codeword Selection and Hybrid Precoding for Multiuser Millimeter-Wave Massive MIMO Systems

Cited by 16 publications

References 10 publications

Hybrid Precoding for Multi-user Massive MIMO Systems Based on Equivalent Channel

Hybrid Precoding for Multi-user Massive MIMO Systems Based on Equivalent Channel

Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

Joint Hybrid Precoding and Combining Design Based Multi-Stage Compressed Sensing Approach for mmWave MIMO Channel Estimation

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