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Channel estimation is one of the most important parts in three-dimensional multiple-input multiple-output (3D MIMO) systems. The characteristics of non-line-of-sight (NLOS) channel and line-of-sight (LOS) channel are different in 3D MIMO systems. If the same channel estimation scheme is used in LOS case as NLOS, the performance of estimation will b...
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Citations
... There are many excellent research works [3][4][5][6][7] and reviews [8][9][10] in the literature dealing with the channel estimation. In recent years, the research on channel estimation mainly focuses on the following aspects: channel estimation based on deep learning, 11,12 channel estimation based on iteration, [13][14][15] and channel estimation based on compressive sensing (CS). ...
In this paper, the channel estimation and signal‐to‐noise ratio (SNR) estimation technique of single‐carrier frequency domain equalization (SC‐FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE‐KF) are proposed. This paper first introduces the SC‐FDE system and introduces the principle of MEE‐KF, and then, the channel estimation flow based on MEE‐KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE‐KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC‐FDE system has also been greatly improved.
... Channel estimation is a crucial task in signal processing and in the new generation of wireless communications (see [1][2][3][4][5]). Intersymbol interference (ISI) is avoided by using orthogonal frequency division multiplexing (OFDM). ...
... Many papers considered channel estimation in the presence of the CFO, that's why in this paper, we focused on autoregressive channel parameters estimation by an adaptive algorithm. For m-th subcarrier in OFDM system, the received signal, using a training sequence, can be expressed as y m (n) = s m (n) * h m (n) + b m (n) (1) where m=0,1,..., M-1 while M is total number of subcarriers, b(n) is an additive zero-mean white noise process, and h(n) is a Rayleigh flat fading which can be modeled as an autoregressive (AR) process [10,11] given by h(n) = a * 1 h(n − 1) + a * 2 h(n − 2) + ... + a * p h(n − p) + u(n) (2) * Corresponding author. respectively. ...
This paper presents an adaptive algorithm for unbiased estimation of autoregressive Rayleigh flat fading parameters in OFDM systems. To this end, a variable leaky steepest descent algorithm is proposed. The authors use a variable leak parameter in the algorithm to remove the noise induced bias from the autoregressive estimates. Theoretical performance analyses of the proposed method are conducted. Moreover, computer simulations are presented to evaluate the performance of the proposed algorithm and to compare it with other existing methods.
... Assumptions and approximations considered here can be either logically justified or experimentally verified as follows. circumstances including the 3D channel [35,36] and its empirical verification has been performed to validate the usefulness of the model using the measured field data over a wide range of the carrier frequency [37]. Comparison of the measured and modelled data has also been made using a prototype to confirm the accuracy of the model in [38]. ...
5G new radio (NR) provides enhanced transmission capabilities to transceivers by utilizing the massive multiple-input multiple-output (MIMO) technology with a significantly increased number of antenna elements. Such transmission requires massive arrays to perform accurate high-gain beamforming over the millimeter-wave frequency band. There is no fixed form of array structures for 5G NR base stations, but they are likely to include multiple subarrays or panels for practicality of implementation and are expected to cover the user equipment (UE) in various locations. In this paper, we propose an array structure to transmit signals over the three-dimensional (3D) space in an isotropic fashion for all types of UEs in ground, aerial, and high-rise building locations, by employing panels on surfaces of a polyhedron. We further derive exact beamforming equations for the proposed array and show the resulting beams provide improved receiver performance over the exiting conventional beamforming. The presented beamforming expressions can be applied to an arbitrary multipanel array with massive antenna elements.
... Though Time Domain Duplex (TDD) massive MIMO systems have already facilitated the acquisition of CSI, directly increasing the number of MU to be estimated on a narrow band is not realistic. Given that the Constant Amplitude Zero-Autocorrelation (CAZA) SRS sequences are based on phase shift, conventional Discrete Fourier Transmission (DFT) based approaches will let desired taps overlap [28] and the Time Domain (TD) denoising will be serevely degrade, while the Conjugated Separation (CS) methods introduce the errors caused by the inequality of adjacent subcarriers. Thus, new technologies should be considered to enhance CEC. ...
... Second, it did not discuss how to determine the number of significant taps, which may introduce severe degradation when the significant taps number are apparently different in practical wireless channels. In our previous work [28], the Enhanced DFT-based (E-DFT) method is proposed. E-DFT is based on that the number of significant taps between Line-of-Sight (LoS) and None-Line-of-Sight (NLoS) users are much different, and includes LoS/NLoS identification, the donoising of LoS/NLoS users and existence test. ...
... Given that T CR is much smaller than T M-DnCNN , we use T M-DnCNN to approximately calculate the time complexity of M-DnCNN CE. Based on this result, the typical complexities calculating structure matrix presented in [38] and time complexity in [28] and [39], the time complexity of four MU channel estimation approaches are provided in Table 1. ...
In Time Domain Duplex (TDD) massive MIMO systems, multi-group multi-casting becomes a promising technology since it supports services of mass content distribution. Based on the nature of transmitting common message to groups of users simultaneously, there exists a rich literature discussing the resource allocation under various constraints. However, the practical acquisition of CSI has not been fully explored when the number of multi-groups is large and the band is narrow. The insufficient sounding reference signal resources lead to the limited Channel Estimation Capacity (CEC). Under this case, even with Multi-User (MU) channel estimation techniques, some users still cannot be estimated in-timely, which introduces degradation. Aiming at this problem, in this paper we provide a preliminary exploration on CEC enhancement. Based on Denoising Convolutional Neuron Network (DnCNN), which is recently proposed and has succeeded in image denoising, we propose MU-DnCNN Channel Estimation (M-DnCNN CE). M-DnCNN CE includes three parts. First, we modify the utilization of SRS sequences. Then we establish the feature maps and propose M-DnCNN to denoise the signals. Finally, a matched channel restoration method is provided. The practical 3-D MIMO channel model is utilized to evaluate the performance. Compared with DFT-based and conjugated separation methods, results show that the performance of M-DnCNN CE is robust and superior, and the CEC is remarkably improved on the premise of satisfying latency constraint.
High-speed rail (HSR) travel has become a popular transportation mode for people, and its train-ground communication has received wide attention. However, current research lacks the optimization of communication pilot design in the HSR scenario. To overcome this challenge, this paper establishes a system model for train-ground predictive antenna (PA) communication. We take the sounding reference signal (SRS) design in the uplink channel pilot as a research object, to maximize the system downlink throughput. We decompose the original problem into three subproblems involving the frequency-domain resource allocation for PA, pattern design, and time slot period of SRS to eliminate coupling. Then, we propose the block coordinate descent (BCD)-based coalitional game and a heuristic algorithm, to obtain a reasonable SRS design of PAs so as improve the accuracy of channel estimation for PAs and channel prediction for main antennas. Finally, the effectiveness of the proposed algorithm is verified by simulation and measured data, with superior performance in enhancing the downlink system throughput.
