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Design of Training Sequences for Multi User—MIMO with Accurate Channel Estimation Considering Channel Reliability Under Perfect Channel State Information Using Cuckoo Optimization

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Abstract

Designing the time domain training sequences is very critical in multi carrier transmission which degrades the performance as it is contaminated by different blocks in different cells. To improve the spectral efficiency and high accuracy, MU-MIMO needs the sensing matrix to be reduced by using the training sequence design and optimization. Integrating the training sequence design and sparse channel estimation improves the capacity of the system. The capacity can be enhanced by reducing the bit error rate. The system capacity for multi user- multi-input and multi output (MU-MIMO) is studied by proper channel estimation with compressed sensing model. The design and optimization of training sequence is analysed for MU-MIMO model using auto coherence and block coherence matrices. The block coherence matrix is optimized using cuckoo algorithm for obtaining lower coherence value for different sparsity values. The performance improvement in terms of signal to noise ratio is 1 dB for single user- multi-input and multi output (SU-MIMO) using genetic algorithm and the performance of MU-MIMO is observed to be 0.93 dB using cuckoo algorithm.

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Abbreviations

\(h_{l}\) :

Channel vector of lth user

\(\tilde{h}_{l}\) :

Channel matrix

\(\tilde{h}_{lbest}\) :

Lowest coherence Value of block coherence matrix

\(K\) :

Number of Users

\(M\) :

Number of Base station antennas

\(p\) :

Degree of perfection

\(R_{l}\) :

Hermitian Matrix

\(R_{sum}\) :

Achievable Sum rate

s n :

Transmitted Symbol

V :

Rician factor

W :

Weight factor

\(w_{l}\) :

Optimal beam forming Vector

\(w_{n}\) :

Normalized vector

\(\varepsilon\) :

Channel reliability

\(\varepsilon_{c}\) :

Critical reliability

\(\varphi g\) :

Sensing Matrix

\(\mu_{B} (\varphi )\) :

Block coherence matrix

\(\mu_{B} (\varphi_{\alpha } ,\varphi_{\beta } )\) :

Lower bound of Block coherence Matrix

\(\theta_{l}\) :

Angle of departure

\(\sigma^{2}\) :

Variance

BER:

Bit error rate

MU-MIMO:

Multi User Multi input and multi output

SINR:

Signal to interference plus noise ratio

SLNR:

Signal to leakage noise ratio

ZF:

Zero Forcing

MRT:

Maximum ratio transmission

SISO:

Single input single output

LOS:

Line of sight

IDFT:

Inverse discrete Fourier transform

SVD:

Single value Decomposition

CSI:

Channel state Information

IDFT:

Inverse discrete Fourier Transform

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Authors and Affiliations

  1. Vignan’s Institute of Information Technology, Visakhapatnam, India

    Uma Maheswari Ramisetty

  2. GITAM University, Visakhapatnam, India

    Sumanth Kumar Chennupati

  3. JNTUA College of Engineering Pulivendula, Pulivendula, India

    Venkata Nagesh Kumar Gundavarapu

Authors
  1. Uma Maheswari Ramisetty
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  2. Sumanth Kumar Chennupati
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  3. Venkata Nagesh Kumar Gundavarapu
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Correspondence to Venkata Nagesh Kumar Gundavarapu.

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Ramisetty, U.M., Chennupati, S.K. & Gundavarapu, V.N. Design of Training Sequences for Multi User—MIMO with Accurate Channel Estimation Considering Channel Reliability Under Perfect Channel State Information Using Cuckoo Optimization. J. Electr. Eng. Technol. 16, 2743–2756 (2021). https://doi.org/10.1007/s42835-021-00778-6

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  • DOI: https://doi.org/10.1007/s42835-021-00778-6

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