Hybrid preprocessing aided spatial modulation in multi‐user massive MIMO systems

Maleki, Marjan; Mohamed-pour, Kamal

doi:10.1049/iet-com.2018.0162

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…Joint user scheduling and MU-MIMO HP algorithm for mmWave mMIMO systems are discussed in [7], where the RF beamformer in the BS adopts the channel matrix derived from instantaneous CSI via singular value decomposition (SVD). In [8], the size of the feedback CSI overhead for the RF beamformer in a hybrid processing scheme is practically reduced by quantising the phase entries with limited available bits while achieving satisfactory data rate performance. However, all above studies cannot completely remove the pilot and feedback overhead for the RF beamformer because they use the full-size instantaneous CSI.…”

Section: A Related Workmentioning

confidence: 99%

“…Configuring the hybrid precoder requires the complete knowledge of channel state information (CSI) for both RF beamformer and baseband precoder regarding a massive number of antenna elements. Considering the configuration parameters of the RF beamformer design, there are two possible strategies: i) using fast time-varying instantaneous CSI obtained by conventional online channel sounding [6]- [8], or ii) using slow time-varying CSI such as the angular information and the channel covariance matrix [9]- [14]. In the first approach where the full-size fast time-varying instantaneous CSI is utilized as input parameters for the hybrid precoder design, a large-size CSI training and feedback overhead is required during online connections.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

et al. 2021

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This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16 × 16 = 256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing. The imperfection of UT-level CSI introduced by the DNN-based path estimation technique is mitigated by the FCM clustering technique, where the AB-HP using offline UT-group CSI generated by the DNN-based channel estimation model and ray tracing based model for the RF beamformer achieves, respectively, 98.7% and 99.1% sum-rate performance of the fully digital precoding (FDP) technique using full-size online CSI.

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Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

et al. 2021

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“…As stated in [20], the accuracy of the RF beamformer in OBS-HP and NOAS-HP heavily depends on the angle spread and it is shown that OBS-HP and NOAS-HP suffer from the sum-rate performance degradation when the angle spread is just increased from 1 • to 3 • . A HP scheme is investigated for the spatial modulation technique in the massive MU-MIMO systems in [21], where the RF beamformer is constructed via the phase of instantaneous CSI as in [14]. In [22], joint user scheduling and HP design is studied for the mmWave massive MIMO systems, where the BS equipped with uniform rectangular array (URA) builds the RF beamformer via applying the singular value decomposition to the instantaneous channel matrix.…”

Section: A Related Workmentioning

confidence: 99%

“…This aspect also makes URA a better solution for 3D beamforming [33]- [35]. However, due to its simple structure, ULAs are often investigated in the literature (e.g., in [14], [16]- [21], [23]- [26], [29]).…”

Section: A Related Workmentioning

confidence: 99%

3D Angular-Based Hybrid Precoding and User Grouping for Uniform Rectangular Arrays in Massive MU-MIMO Systems

2020

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This paper proposes a new user grouping algorithm and three-dimensional (3D) angular-based hybrid precoding (AB-HP) scheme for massive multi-user multiple-input multiple-output (MU-MIMO) systems using uniform rectangular arrays (URA). At first, the users clustered in multiple spots are efficiently grouped according to the proposed user grouping algorithm, which only utilizes the user angle-of-departure (AoD) information and does not require prior knowledge of the number of user groups. By employing the AoD support of the user groups, the RF-beamformer of AB-HP is designed to reduce the inter-group interference, the channel state information (CSI) overhead, and the number of RF chains. Then, the digital baseband precoder of AB-HP is constructed via regularized zero-forcing (RZF) using the effective channel seen from baseband to simultaneously serve the users clustered in multiple groups, by considering three approaches: joint-group-processing (JGP), per-group-processing (PGP) and common-group-processing (CGP). For each approach, the signal-to-interference-plus-noise ratio (SINR) expressions as well as their tight deterministic approximations are derived. To further reduce the number of RF chains, we also propose a new transfer block design, which reduces the number of RF chains down to the number of independent data streams without penalizing the sum-rate performance. Illustrative results reveal that the proposed AB-HP schemes with the relaxed CSI estimation overhead and reduced hardware cost/complexity can closely approach to the sum-rate performance of the single-stage fully-digital precoding (FDP). Furthermore, AB-HP has considerably higher energy efficiency performance compared to FDP due to the reduced number of RF chains. We show through simulation that the proposed AB-HP can offer significantly better performance than existing HP techniques. The computational complexity of AB-HP is also analyzed.INDEX TERMS Massive MIMO, 3D hybrid precoding, user grouping, angle of departure (AoD), uniform rectangular array (URA), RF chain reduction.

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“…M. Maleki and K. Mohamed-pour [15] propose a two-stage precoding based on Zeroforcing (ZF) that can reduce the Channel State Information (CSI) feedback overhead in the frequency division duplex mode. The literature [16] proposes a ZF-based Propensity Score Matching (PSM) procedure to reduce the hardware complexity design by reducing the number of Radio Frequency (RF) chains for millimetre wave and associated channels. However, these preprocessing methods are costly and complex, so literature [17] and literature [18] proposed the preprocessing methods of Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT), which are easy to implement on the computer, but cannot guarantee the randomness of the generated keys.…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Key Generation Method Based on SVD Pre-processing

Liu

Guo²,

Ju³

2023

JCSANDM

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Environmental factors such as channel noise and hardware fingerprints affect the encryption effect of physical layer key generation techniques, resulting in low consistency of generated keys. Feature pre-processing is a common means of improving consistency of keys. However, most of the existing feature pre-processing algorithms improve key consistency by sacrificing key generation rate, which is not very usable. Therefore, it is proposed a physical layer key generation method based on SVD pre-processing. This method uses the SVD feature processing algorithm to pre-process the channel features extracted from both sides of the communication before quantization, in order to simultaneously improve key consistency and key generation rate. The simulation results show that when the channel SNR is greater than 10 dB, the BER of the SVD scheme is significantly lower compared to the scheme without pre-processing and the DCT and PCA pre-processing schemes; when the SNR is greater than 20 dB, the SVD scheme KGR can reach a level of 10bit/s, which is significantly higher than the other three schemes. The results show that this scheme can significantly increase the key generation rate while effectively improving key consistency.

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Hybrid preprocessing aided spatial modulation in multi‐user massive MIMO systems

Cited by 7 publications

References 30 publications

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

3D Angular-Based Hybrid Precoding and User Grouping for Uniform Rectangular Arrays in Massive MU-MIMO Systems

Physical Layer Key Generation Method Based on SVD Pre-processing

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