Self-IQ-Demodulation Based Compensation Scheme of Frequency-Dependent IQ Imbalance for Wideband Direct-Conversion Transmitters

Intell. and Converged Netw.

Cosmas

et al. 2020

Self Cite

The Internet of Radio-Light (IoRL) is a cutting-edge system paradigm to enable seamless 5G service provision in indoor environments, such as homes, hospitals, and museums. The system draws on innovative architectural structure that sits on the synergy between the Radio Access Network (RAN) technologies of millimeter Wave communications (mmWave) and Visible Light Communications (VLC) for improving network throughput, latency, and coverage compared to existing efforts. The aim of this paper is to introduce the IoRL system architecture and present the key technologies and techniques utilised at each layer of the system. Special emphasis is given in detailing the IoRL physical layer (Layer 1) and Medium Access Control layer (MAC, Layer 2) by means of describing their unique design characteristics and interfaces as well as the robust IoRL methods of improving the estimation accuracy of user positioning relying on uplink mmWave and downlink VLC measurements.

Section: Mmwave Transmission Measurementmentioning

confidence: 99%

Internet of radio and light: 5G building network radio and edge architecture

Intell. and Converged Netw.

Cosmas

et al. 2020

Self Cite

“…1, which shows the RF band imbalance model directly [19] although there exist some models based on the equivalent baseband imbalance [28]- [30]. The massive MIMO system model with hardware impairments is studied next.…”

Section: Hardware Impairments Modelmentioning

confidence: 99%

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

Luo

IEEE Trans. on Broadcast.

Huang

et al. 2017

Self Cite

Abstract-Massive multi-input multioutput (MIMO) is believed to be an effective technique for future terrestrial broadcasting systems. Reciprocity calibration is one of the major practical challenges for massive MIMO systems operating in time-division duplexing mode. A new closed-loop reciprocity calibration method is investigated in this paper which can support online calibration with a higher accuracy compared to the existing methods. In the first part of the proposed method, an optimized relative calibration is introduced using the same structure of traditional relative calibration, but with less impaired hardware in the reference radio chain. In the second part, a test device (TD)-based calibration is proposed which makes online calibration possible. An experiment setup is built for the measurement of the base station hardware impairments and TD-based calibration implementation. Simulation results and the error vector magnitude of UE received signal after calibration show that the performance of our proposed method is improved significantly compared to the existing relative calibration methods.

“…Baseband signal processing such as channel estimation, channel equalization, channel encoding, etc, are the important parts of wireless communication systems to resist fading channel [8][9][10][11], where channel estimation is especially critical for massive MIMO systems. In massive MIMO systems, accurate and efficient channel estimation is a challenging problem and an open research issue, because the number of channel parameters to be estimated is very large as the antennas increase, while the number of pilots adopted by channel estimation is limited to make sure a high spectrum efficiency.…”

Section: Introductionmentioning

confidence: 99%

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

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.