scholarly journals A Comparison of the Uplink Performance of Cell-Free Massive MIMO using Three Linear Combining Schemes: Full-Pilot Zero Forcing with Access Point Selection, Matched-Filter and Local-Minimum-Mean-Square Error

2020 ◽  
Author(s):  
Stephen O'Hurley ◽  
Le-Nam Tran
Keyword(s):  
Mean Square Error ◽  
Massive Mimo ◽  
Local Minimum ◽  
Matched Filter ◽  
Minimum Mean Square Error ◽  
Access Point ◽  
Mean Square ◽  
Zero Forcing ◽  
Point Selection

scholarly journals Performance Analysis of Large-Scale MU-MIMO With a Simple and Effective Chanel Estimator

2019 ◽  
Author(s):  
Felipe Augusto Pereira de Figueiredo ◽  
Claudio Ferreira Dias ◽  
Eduardo Rodrigues de Lima ◽  
Gustavo Fraidenraich
Keyword(s):  
Mean Square Error ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Large Scale ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Noise Power ◽  
Mean Square ◽  
Pilot Contamination ◽  
Channel Estimator

Accurate channel estimation is of utmost importance for massive MIMO systems that allow providing significant improvements in spectral and energy efficiency. In this work, we investigate the spectral efficiency performance and present a channel estimator for multi-cell massive MIMO systems subjected to pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without prior knowledge of the inter-cell large-scale channel coefficients and noise power. The estimator approximates the performance of a linear Minimum Mean Square Error (MMSE) as the number of antennas increases. Following, we derive a lower bound closed-form spectral efficiency of the Maximum Ratio Combining (MRC) detector in the proposed channel estimator. The simulation results highlight that the proposed estimator performance approaches the linear minimum mean square error (LMMSE) channel estimator asymptotically.

scholarly journals Improved Signal Detection Techniques for QOSTBC System in Fast Fading Channel

2020 ◽  
Vol 1 ◽  
pp. 6-12
Author(s):  
Jyoti P. Patra ◽  
Poonam Singh
Keyword(s):  
Signal Detection ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Decision Feedback ◽  
Decision Feedback Equalizer ◽  
Mean Square ◽  
Zero Forcing ◽  
Decision Feedback Equalization ◽  
Interference Cancelation ◽  
Detection Schemes

Most existing quasi-orthogonal space time Block coding (QO-STBC) schemes have been developed relying on the assumption that the channel is at or remains static during the length of the code word symbol periods to achieve an optimal antenna diversity gain. However, in time-selective fading channels, this assumption does not hold and causes intertransmit-antenna-interferences (ITAI). Therefore, the simple pairwise maximum likelihood decoding scheme is not sufficient to recover original transmitted signals at the receiver side. To avoid the interferences, we have analyzed several signal detection schemes, namely zero forcing (ZF), two-step zero forcing (TS-ZF), minimum mean square error (MMSE), zero forcing - interference cancelation - decision feedback equalizer (ZF-IC-DFE) and minimum mean square error - interference cancelation { decision feedback equalizer (MMSE-IC-DFE). We have proposed two efficient iterative signal detection schemes, namely zero forcing - iterative interference cancelation - zero forcing { decision feedback equalization (ZF-IIC-ZF-DFE) and minimum mean square error - parallel interference cancelation - zero forcing – decision feedback equalization (MMSE-IIC-ZF-DFE). The simulation results show that these two proposed detection schemes significantly outperform all conventional methods for QOSTBC system over time selective channel.

scholarly journals Data Ridden Pilots Based Estimation of Sparse Multipath Massive MIMO Channels Using Orthogonal Matching Pursuit

2019 ◽  
Vol 25 (4) ◽  
pp. 81-87 ◽  
Author(s):  
Babar Mansoor ◽  
Moazzam Islam Tiwana ◽  
Syed Junaid Nawaz ◽  
Abrar Ahmed ◽  
Abdul Haseeb ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Matching Pursuit ◽  
Minimum Mean Square Error ◽  
Research Work ◽  
Base Station ◽  
Orthogonal Matching Pursuit ◽  
Mean Square ◽  
Mimo Channels

Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results, the proposed SiT-OMP technique outperforms the existing Least Squares (SiT) channel estimation technique. The comparison is done using performance metrics of the Bit Error Rate (BER) and the Normalized Channel Mean Square Error (NCMSE).

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