scholarly journals Improvement of Complexity and Performance of Least Square Based Channel Estimation in MIMO System

2016 ◽  
Vol 1 ◽  
pp. 11
Author(s):  
Anand Kumar Sah ◽  
Arun Kumar Timalsina
Keyword(s):  
Channel Estimation ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Vital Role ◽  
Least Square ◽  
Mimo Channel ◽  
Fourth Generation ◽  
Simulation Results

<p>Multiple-input multiple-output (MIMO) systems play a vital role in fourth generation wireless systems to provide advanced data rate. In this paper, a better performance and reduced complexity channel estimation method is proposed for MIMO systems based on matrix factorization. This technique is applied on training based least squares (LS) channel estimation using STBC for performance improvement. Simulation results indicate that the proposed method not only alleviates the performance of MIMO channel estimation but also significantly reduces the complexity caused by matrix inversion. The performance evaluations are validated through computer simulations using MATLAB in terms of bit error rate (BER) for modified LS with LS and MMSE channel estimation techniques. Simulation results show that the BER performance and complexity of the proposed method clearly outperforms the conventional LS channel estimation method.</p><p><em>Journal of Advanced College of Engineering and Management, Vol. 1, 2015</em>, pp. 11-24</p>

scholarly journals Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

2017 ◽  
Vol 67 (6) ◽  
pp. 668
Author(s):  
Qingzhu Wang ◽  
Mengying Wei ◽  
Yihai Zhu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Data Representation ◽  
Frequency Division ◽  
Frequency Division Duplexing

<p class="p1">To make full use of space multiplexing gains for the multi-user massive multiple-input multiple-output, accurate channel state information at the transmitter (CSIT) is required. However, the large number of users and antennas make CSIT a higher-order data representation. Tensor-based compressive sensing (TCS) is a promising method that is suitable for high-dimensional data processing; it can reduce training pilot and feedback overhead during channel estimation. In this paper, we consider the channel estimation in frequency division duplexing (FDD) multi-user massive MIMO system. A novel estimation framework for three dimensional CSIT is presented, in which the modes include the number of transmitting antennas, receiving antennas, and users. The TCS technique is employed to complete the reconstruction of three dimensional CSIT. The simulation results are given to demonstrate that the proposed estimation approach outperforms existing algorithms.</p>

scholarly journals A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 218 ◽  
Author(s):  
Kifayatullah Bangash ◽  
Imran Khan ◽  
Jaime Lloret ◽  
Antonio Leon
Keyword(s):  
Computational Complexity ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Low Complexity ◽  
Least Square ◽  
Mimo Channel ◽  
Finite Sample ◽  
The Impact

Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. Simulation results show that the proposed scheme can effectively reduce the deviation, improve the channel estimation accuracy, mitigate the impact of pilot contamination and obtain accurate CSI with low overhead and computational complexity.

scholarly journals GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jianfeng Shao ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Zhiguang Han ◽  
Ting Su
Keyword(s):  
Channel Estimation ◽  
Millimeter Wave ◽  
Mimo Systems ◽  
Learning Algorithm ◽  
Mimo System ◽  
Estimation Algorithm ◽  
Mimo Channel ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Signal Recovery

AbstractBased on the finite scattering characters of the millimeter-wave multiple-input multiple-output (MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation–maximization-based sparse Bayesian learning algorithm can be applied to handle this problem, it spends lots of time to calculate the E-step which needs to compute the inversion of a high-dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

Hybrid multibeam forming and multiuser detection technique for MU-MIMO system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
D. Lalitha Kumari ◽  
M.N. Giri Prasad
Keyword(s):  
Energy Efficiency ◽  
Error Rate ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Experimental Results ◽  
Rate Performance ◽  
Hardware Complexity ◽  
Content Type ◽  
Simulation Results

PurposeIn recent years, multiuser-multiple-input multiple-output (MU-MIMO)-based wireless communication system has emerged as a prominent 5G technique that has several advantages over conventional MIMO systems such as high data rate and channel capacity. In this paper, the authors introduce a novel low-complexity radix factorization-based fast Fourier transform (FFT) as a multibeamformer and maximal likelihood-MU detection (ML-MUD) techniques as an optimal signal subdetector which results with considerable complexity reduction with intolerable error rate performance.Design/methodology/approachThe proposed radix-factorized FFT-multibeamforming (RF-FFT-MBF) architectures have the potential to reduce both hardware complexity and energy consumptions as compared to its state-of-the-art methods while meeting the throughput requirements of emerging 5G devices. Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors.FindingsHere through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.Originality/valueHere through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.

ESTIMATION METHODS OF A CHANNEL WITH SPATIAL MODULATION BASED ON CORRELATION

2021 ◽  
pp. 106-110
Author(s):  
О.Н. Чирков
Keyword(s):  
Channel Estimation ◽  
Communication Channel ◽  
Energy Savings ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Spatial Modulation ◽  
Mimo Channel ◽  
Estimation Methods ◽  
Signal Ratio ◽  
Single Stream

Рассматриваются методы оценки канала связи с пространственной модуляцией. Данный вид модуляции представляет собой методику однопотоковой передачи данных с несколькими входами и несколькими выходами (MIMO), при которой одновременно активируется только одна передающая антенна. Пространственная модуляция позволяет добиться полного исключения межканальных помех, а также демонстрирует большую экономию энергии в радиочастотной цепи. Однако, в отличие от многопоточных систем MIMO, оценка канала для пространственной модуляции становится проблемой, поскольку канал MIMO не может быть оценен на одном этапе передачи по единственному потоку. На основании этого факта была предложена новая схема оценки канала, которая использует корреляцию каналов и совместно оценивает каналы для разных передающих антенн. Предложенный метод обеспечивает тот же период оценки, что и многопоточные схемы MIMO. Исследовано изменение количества передаваемых пилот-сигналов при пространственной модуляции как для традиционных, так и для новых методов оценки канала связи. Уравновешивая точность и объем данных, можно достичь оптимального отношения пилот-сигналов для максимальной пропускной способности канала. Результаты моделирования показывают, что новый подход оценки превосходит традиционный метод с гораздо более низким оптимальным коэффициентом количества пилотов The article considers methods for estimating a communication channel with spatial modulation. This type of modulation is a single-stream multiple-input multiple-output (MIMO) technique in which only one transmit antenna is activated at a time. Spatial modulation allows for complete elimination of inter-channel interference, and also demonstrates great energy savings in the RF circuit. However, unlike multi-stream MIMO systems, channel estimation for spatial modulation becomes a problem because a MIMO channel cannot be estimated in a single transmission step on a single stream. Based on this fact, I proposed a new channel estimation scheme that uses channel correlation and jointly estimates channels for different transmit antennas. The proposed method provides the same evaluation period as multithreaded MIMO schemes. The change in the number of transmitted pilot-signals with spatial modulation is investigated for both traditional and new methods of estimation of the communication channel. By balancing accuracy and data volume, an optimal pilot signal ratio can be achieved for maximum channel throughput. Simulation results show that the new scoring approach outperforms the traditional method with a much lower optimal pilot count ratio

scholarly journals Effect of Polarization on the Correlation and Capacity of Indoor MIMO Channels

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Hsun-Chang Lo ◽  
Ding-Bing Lin ◽  
Teng-Chieh Yang ◽  
Hsueh-Jyh Li
Keyword(s):  
Fading Channels ◽  
Channel Capacity ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Mimo Channel ◽  
Analysis Method ◽  
Mimo Channels ◽  
Received Power ◽  
Value Decomposition

We describe a simple multiple-input/multiple-output (MIMO) channel measurement system for acquiring indoor MIMO channel responses. Four configurations of the polarization diversity antenna, referred to asVVV,YYH,YVYandVHH, were studied in terms of the capacity of indoor MIMO systems. Measurements were taken for a3×3MIMO system in the 2.4 GHz band. In addition, the channel capacity, singular-value decomposition, and correlation coefficient were used to explain the effects of various polarization schemes on MIMO fading channels. We also propose an analysis method for polarization channel capacity; this method includes the normalization of the received power and polarization effect for different polarization schemes. The validation of the model is based upon data collected in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. From the numerical simulation results, the proposed analysis method was close to measurements made in an indoor environment.

scholarly journals Experimental Analysis of Concentrated versus Distributed Massive MIMO in an Indoor Cell at 3.5 GHz

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1646
Author(s):  
Jesús R. Pérez ◽  
Óscar Fernández ◽  
Luis Valle ◽  
Abla Bedoui ◽  
Mohamed Et-tolba ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Access Network ◽  
Base Station ◽  
Mimo Channel ◽  
Power Imbalance ◽  
Channel Measurements

This paper presents a measurement-based comparison between distributed and concentrated massive multiple-input multiple-output (MIMO) systems, which are called D-mMIMO and C-mMIMO systems, in an indoor environment considering a 400 MHz bandwidth centered at 3.5 GHz. In both cases, we have considered an array of 64 antennas in the base station and eight simultaneously active users. The work focuses on the characterization of both schemes in the up-link, considering the analysis of the sum capacity, the total spectral efficiency (SE) achievable by using the zero forcing (ZF) combining method, as well as the user fairness. The effect of the power imbalance between the different transmitters or user terminal (UT) locations, and thus, the benefits of performing an adequate power control are also investigated. The differences between the C-mMIMO and D-mMIMO channel performances are explained through the observation of the structure of their respective measured channel matrices through parameters such as the condition number or the power imbalance between the channels established by each UT. The channel measurements have been performed in the frequency domain, emulating a massive MIMO system in the framework of a time-domain duplex orthogonal frequency multiple access network (TDD-OFDM-MIMO). The characterization of the MIMO channel is based on the virtual array technique for both C-mMIMO and D-mMIMO systems. The deployment of the C-mMIMO and D-MIMO systems, as well as the distribution of users in the measurement environment, has been arranged as realistically as possible, avoiding the movement of people or machines.

scholarly journals Precise Channel Estimation Approach for a mmWave MIMO System

2020 ◽  
Vol 10 (12) ◽  
pp. 4397 ◽  
Author(s):  
Prateek Saurabh Srivastav ◽  
Lan Chen ◽  
Arfan Haider Wahla
Keyword(s):  
Channel Estimation ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Low Rank ◽  
High Signal ◽  
Formidable Challenge ◽  
Input Multiple Output ◽  
Efficient Recovery

Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculate fewer dominant paths in mmWave channels. However, conventional CS techniques require a higher training overhead for efficient recovery. In this paper, an efficient extended alternation direction method of multipliers (Ex-ADMM) is proposed for mmWave channel estimation. In the proposed scheme, a joint optimization problem is formulated to exploit low rank and channel sparsity individually in the antenna domain. Moreover, a relaxation factor is introduced which improves the proposed algorithm’s convergence. Simulation experiments illustrate that the proposed algorithm converges at lower Normalized Mean Squared Error (NMSE) with improved spectral efficiency. The proposed algorithm also ameliorates NMSE performance at low, mid and high Signal to Noise (SNR) ranges.

Improved Port Modulation for Multiuser Massive MIMO Systems

2015 ◽  
Vol 7 (4) ◽  
pp. 15-25
Author(s):  
Yujiao He ◽  
Jianing Zhao ◽  
Lijuan Tao ◽  
Fuyu Hou ◽  
Wei Jia
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Channel State ◽  
State Information ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results ◽  
Csi Feedback

This paper proposes an improved port modulation (PM) method which can be applied to the multiuser (MU) massive multiple-input multiple-output (MIMO) system. The precoding process of the improved PM can be divided into two parts: port precoding and MU precoding. The methods of the precoding and detection are provided and the performance of the proposed improved PM is simulated and analyzed. Simulation results show that the proposed improved PM system can achieve a satisfying bit error rate (BER) performance with a cutdown channel state information (CSI) feedback.

scholarly journals Downlink Channel Estimation in Massive Multiple-Input Multiple-Output with Correlated Sparsity by Overcomplete Dictionary and Bayesian Inference

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 473 ◽  
Author(s):  
Wei Lu ◽  
Yongliang Wang ◽  
Xiaoqiao Wen ◽  
Shixin Peng ◽  
Liang Zhong
Keyword(s):  
Bayesian Inference ◽  
Channel Estimation ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Temporal Correlation ◽  
Angular Domain ◽  
Multiple Input ◽  
Overcomplete Dictionary ◽  
Input Multiple Output

We exploited the temporal correlation of channels in the angular domain for the downlink channel estimation in a massive multiple-input multiple-output (MIMO) system. Based on the slow time-varying channel supports in the angular domain, we combined the channel support information of the downlink angular channel in the previous timeslot into the channel estimation in the current timeslot. A downlink channel estimation method based on variational Bayesian inference (VBI) and overcomplete dictionary was proposed, in which the support prior information of the previous timeslot was merged into the VBI for the channel estimation in the current timeslot. Meanwhile the VBI was discussed for a complex value in our system model, and the structural sparsity was utilized in the Bayesian inference. The Bayesian Cramér–Rao bound for the channel estimation mean square error (MSE) was also given out. Compared with other algorithms, the proposed algorithm with overcomplete dictionary achieved a better performance in terms of channel estimation MSE in simulations.

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