Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems. (30th June 2022)
- Record Type:
- Journal Article
- Title:
- Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems. (30th June 2022)
- Main Title:
- Performance Evaluation of Spectral Efficiency for Uplink and Downlink Multi-Cell Massive MIMO Systems
- Authors:
- Asif, Rao Muhammad
Shakir, Mustafa
Rehman, Ateeq Ur
Shafiq, Muhammad
Khan, Rehan Ali
Khan, Wali Ullah - Other Names:
- Marques Carlos Academic Editor.
- Abstract:
- Abstract : Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DLAbstract : Massive multiple-input and multiple-output (MIMO) systems have become the most persuasive technology for 5G as it increased the energy efficiency gigantically as compared to other wireless communication systems. Being the most vibrant research technology in the communication sector, this research work is based on the optimal model development of energy-efficient massive MIMO systems. The proposed model is a realistic model that augmented the spectral efficiency (SE) of massive MIMO systems where a multi-cell model scenario is considered. Channel estimation is carried out at the base stations (BSs) based on uplink (UL) transmission while the minimum mean-squared error (MMSE), Element-wise MMSE, and Least-square (LS) estimators are used for the estimation. We analyze the achievable SE of the UL based on the MMSE channel estimator with different receive combining schemes. Moreover, the downlink (DL) transmission model is also modelled with different precoding schemes by taking the same vectors used in combining schemes. The simulation results show a significant improvement in spectral efficiency by developing UL and DL transmission models and also realized that the average sum of SE per cell can be improved by optimized MMSE channel estimation, installing multiple BS antennas, and serving multiple UEs per cell. The findings of this work specify that the massive MIMO system can be developed by optimizing the channel estimation for the augmentation of SE in UL and DL transmissions. Conclusively, it can be summarized that some complex computations of MMSE channel estimators can enhance the average sum of SE per cell as per the results verified in this model. … (more)
- Is Part Of:
- Journal of sensors. Volume 2022(2022)
- Journal:
- Journal of sensors
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-06-30
- Subjects:
- Detectors -- Periodicals
681.205 - Journal URLs:
- https://www.hindawi.com/journals/js/ ↗
- DOI:
- 10.1155/2022/7205687 ↗
- Languages:
- English
- ISSNs:
- 1687-725X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 22315.xml