Design of Training Sequences for Multi User—MIMO with Accurate Channel Estimation Considering Channel Reliability Under Perfect Channel State Information Using Cuckoo Optimization
| Autor: | Uma Maheswari Ramisetty, Sumanth Kumar Chennupati, Venkata Nagesh Kumar Gundavarapu |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
MIMO 020206 networking & telecommunications 020302 automobile design & engineering 02 engineering and technology Coherence (statistics) Signal-to-noise ratio Channel reliability Compressed sensing 0203 mechanical engineering Channel state information 0202 electrical engineering electronic engineering information engineering Bit error rate Electrical and Electronic Engineering Algorithm Computer Science::Information Theory Communication channel |
| Zdroj: | Journal of Electrical Engineering & Technology. 16:2743-2756 |
| ISSN: | 2093-7423 1975-0102 |
| DOI: | 10.1007/s42835-021-00778-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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