A Closed-Loop Reciprocity Calibration Method for Massive MIMO in Terrestrial Broadcasting Systems

Luo, Hui; Zhang, Yue; Huang, Li-Ke; Cosmas, John; Aggoun, A.

doi:10.1109/tbc.2016.2606890

Cited by 16 publications

(8 citation statements)

References 39 publications

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“…In addition, the Cyclic Prefix (CP) is configured long enough to ensure all subcarriers are orthogonal [20]. At the receiver side, assuming all active subcarriers patterns can be decoded successfully, the Channel State Information (CSI)H and the codebook CB are already known [21].…”

Section: Ldm-im System a System Frameworkmentioning

confidence: 99%

Average SER Analysis for Layered Division Multiplexing System with Index Modulation

Zhang

Chen

et al. 2019

EasyChair Preprints

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A novel Layered Division Multiplexing (LDM) With Index Modulation (LDM-IM) system is proposed in this paper. It employs the Index Modulation (OFDM-IM) technology to enhance the transmission performance of the original LDM system by transmitting extra bits through the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers indices. The proposed system is based on a two-layer, Upper Layer (UL) and Lower Layer (LL), LDM system that serves two independent data services for at least two User Equipment(s) (UE) simultaneously. Besides this, by exploiting the Index Modulation (IM), each UE can receive the extra bits by decoding the subcarriers activation patterns. To map the extra bits to the subcarriers, a simple random codebook is designed in the proposed system based on the concept of OFDM-IM. To proof the availability and reliability of the proposed system, two metrics are chosen to evaluate the system performance, the average Symbol Error Rate (SER) and the transmission rate. In this paper, the architecture of the proposed system is introduced firstly. After that, the average SER of it is analyzed and verified by the Monte Carlo simulation. Finally, the transmission rate of the proposed system and the original LDM system is compared and evaluated.

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Section: Ldm-im System a System Frameworkmentioning

confidence: 99%

Average SER Analysis for Layered Division Multiplexing System with Index Modulation

Zhang

Chen

et al. 2019

EasyChair Preprints

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“…Finally, DVB-NGH is the first broadcasting system to incorporate multiple-input multiple-output (MIMO) antenna schemes as the key technology not only to improve the robustness of the transmitted signal by exploiting the spatial diversity of the MIMO channel but also to achieve increased data rates through spatial multiplexing. Recently, reciprocity calibration for massive MIMO has been studied for future terrestrial broadcasting technologies [13]. In this work, Luo et al proposed a novel and innovative closed-loop reciprocity calibration method for massive MIMO systems with a better performance compared to the existing methods.…”

Section: Introductionmentioning

confidence: 99%

Capacity Improvement for DVB-NGH with Dual-Polarized MIMO Spatial Multiplexing and Hybrid Beamforming

Nguyen

2020

International Journal of Digital Multimedia Broadcasting

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Multiple-input multiple-output (MIMO) antenna scheme is an effective technique for future terrestrial broadcasting systems such as digital video broadcasting-next-generation handheld (DVB-NGH) to overcome the limits on information theory of traditional single-antenna wireless communications without any additional bandwidth or increased transmit power. In this paper, we propose a hybrid beamforming scheme for dual-polarized MIMO spatial multiplexing DVB-NGH broadcasting systems. The system of interest makes use of phase shifters and amplitude attenuators for the digital-analog precoder at beamforming stage of the transmitter end to maximize the signal-to-noise ratio to increase the channel capacity of the DVB-NGH systems. At the receiver end, the maximum likelihood (ML) detector is used to evaluate the system performance. We consider the signal-to-interference-and-noise ratio (SINR) and the achievable average capacity for the DVB-NGH MIMO with various FFT sizes, number of transmit antennas, and different modulation schemes. The performance results on bit error rate, channel capacity, and beampatterns show that the proposed hybrid beamforming and dual-polarized MIMO spatial multiplexing schemes provide more robustness against signal interference by beamforming and/or nulling techniques. The simulation results also show that the proposed system achieves higher capacity than the existing MIMO schemes for the DVB-NGH systems.

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“…Massive MIMO can make a huge increase on system capacity with a much larger number of antennas configured at the base station (BS). Thus, it is regarded as one of the key technologies for future fifth generation (5G) wireless communication systems for its high spectrum and energy efficiency [2][3]. By utilizing spatial multiplexing, a massive MIMO base station can serve multiple users simultaneously through multi-user beamforming [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Compression-Based LMMSE Channel Estimation With Adaptive Sparsity for Massive MIMO in 5G Systems

Zhang

Chen

et al. 2019

IEEE Systems Journal

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Massive multi-input multi-output (MIMO) has been regarded as one of the key technologies for fifth generation (5G) mobile communication systems, as it can significantly enhance the system capacity with high spectrum and energy efficiency. For massive MIMO systems, accurate channel estimation is a challenging problem, especially when the number of parameters to be estimated is large and the number of pilots is limited. In this paper, a compression based linear minimum mean square error (CLMMSE) channel estimation algorithm is proposed for massive MIMO in 5G systems. Compared with the traditional linear minimum mean square error (LMMSE) algorithm, the proposed approach calculates the channel autocorrelation matrix by investigating the channel prior information based on compressive sensing (CS) theory, utilizing the block sparsity of massive MIMO channels, to reduce the complexity for obtaining autocorrelation matrix. Then it substitutes matrix inverse operation by singular value decomposition (SVD) to further reduce the computational complexity. In addition, a block sparsity adaptive matching pursuit (BSAMP) method is also proposed to adaptively estimate the block sparsity of the channel in the first step of the proposed CLMMSE algorithm, which can make it more efficient. The sparsity-adaptive processing is achieved by setting a threshold and finding the position of the maximum backward difference, then using the regularized method to solve channel estimation as a convex optimization problem. Analyses and simulations indicate that with slight performance degradation, the proposed algorithm reduces the computational complexity significantly compared with the traditional LMMSE algorithm. And compared with pure CS methods, CLMMSE has an obviously better performance, which is benefit to solve the pilot pollution problem of massive MIMO in 5G systems. Furthermore, the BSAMP based CLMMSE algorithm has better performance and lower time complexity than the algorithm based on other CS methods, which further improves the system performance.

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A Closed-Loop Reciprocity Calibration Method for Massive MIMO in Terrestrial Broadcasting Systems

Cited by 16 publications

References 39 publications

Average SER Analysis for Layered Division Multiplexing System with Index Modulation

Average SER Analysis for Layered Division Multiplexing System with Index Modulation

Capacity Improvement for DVB-NGH with Dual-Polarized MIMO Spatial Multiplexing and Hybrid Beamforming

Compression-Based LMMSE Channel Estimation With Adaptive Sparsity for Massive MIMO in 5G Systems

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