Pilot Optimization for Structured Compressive Sensing Based Channel Estimation in Large-Scale MIMO Systems with Superimposed Pilot Pattern

2018 ◽  
Vol 100 (3) ◽  
pp. 977-993 ◽  
Author(s):  
Jun Cai ◽  
Xueyun He ◽  
Rongfang Song
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Large Scale ◽  
Mimo Systems ◽  
Pilot Pattern

scholarly journals Channel Estimation for Millimeter Wave Massive MIMO Systems Using Separable Compressive Sensing

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 49738-49749
Author(s):  
Ting Jiang ◽  
Maozhong Song ◽  
Xuejian Zhao ◽  
Xu Liu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems

scholarly journals Channel Estimation for Extremely Large-Scale Massive MIMO Systems

2020 ◽  
Vol 9 (5) ◽  
pp. 633-637 ◽  
Author(s):  
Yu Han ◽  
Shi Jin ◽  
Chao-Kai Wen ◽  
Xiaoli Ma
Keyword(s):  
Channel Estimation ◽  
Massive Mimo ◽  
Large Scale ◽  
Mimo Systems

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 GWO Based Optimal Channel Estimation Technique for Large Scale Mimo in LTE Network

2019 ◽  
Vol 8 (12) ◽  
pp. 5306-5314
Keyword(s):  
Channel Estimation ◽  
Spectral Efficiency ◽  
Large Scale ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Fourth Generation ◽  
High Data ◽  
Data Rates ◽  
High Data Rates

The Wireless Systems Are Employed With More Number Of Antennas For Fulfilling The Demand For High Data Rates. In Telecommunication, Lte-A (Long Term EvolutionAdvanced) Is A Well-Known Technology Intended For Wireless Broadband Communication Aimed At Data Terminals And Mobile Devices. Multiple Input Multiple Output (Mimo) Technology Is Used By Lte Which Is Also Known As Fourth Generation Mobile Networks To Attain Very High Data Rates In Downlink And Uplink Channels. Though The Mimo Systems In Massive Mimo Are Provided By Multiple Antennas, The Design Of The Appropriate Non-Erroneous Detection Algorithm Is A Major Challenge. Also, With The Increase In Quantity Of Antennas, The System's Spectral Efficiency Begins To Degrade. So As To Deal With This Issue, A Proper Algorithm Has To Be Utilized For Channel Estimation. The Bio Inspired Algorithms Have Shown Potential In Handling These Issues In Communication And Signal Processing. So, Grey Wolf Optimization (Gwo) Algorithm Is Used In This Approach To Estimate The Most Optimal Communication Channel. Also, The Spectral Efficiency And Data Capacity Are Enhanced Using The Presented Approach. The Proposed Approach’s Performance Is Compared With The Existing Approaches. The Simulation Result Exposes That The Presented Channel Estimation Approach Is Far Better Than Existing Channel Estimation Approaches In Performance Metrics Such As Bit Error Rate, Minimum Delay, Papr, Spectral Efficiency, Uplink Throughput, Downlink Throughput And Mean-Squared-Error

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