On the Interference Reduction Factor in Massive MIMO System over Rician Fading Channels

Arellano, Jorge; Altamirano, Carlos Daniel; Mora, Henry Ramiro Carvajal

doi:10.1109/etcm56276.2022.9935732

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…In this appendix, the covariance matrix of the channel estimation error is simplified. For this, in the following, we calculate each term of Equation (18).…”

Section: Appendix Covariance Matrix Calculation For the Channel Estim...mentioning

confidence: 99%

“…From a comprehensive literature review, it is evident that the performance of M-MIMO systems over Rician fading channels remains significantly less explored in comparison to Rayleigh fading scenarios. Moreover, within this less-explored domain, scenarios involving channel correlation in Rician fading channels xhave received even less attention despite their practical relevance [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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On the Performance of MMSE Channel Estimation in Massive MIMO Systems over Spatially Correlated Rician Fading Channels

Arellano,

Altamirano,

Carvajal Mora

et al. 2024

Wireless Communications and Mobile Computing

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Massive multiple-input-multiple-output (M-MIMO) offers remarkable advantages in terms of spectral, energy, and hardware efficiency for future wireless systems. However, its performance relies on the accuracy of channel state information (CSI) available at the transceivers. This makes channel estimation pivotal in the context of M-MIMO systems. Prior research has focused on evaluating channel estimation methods under the assumption of spatially uncorrelated fading channel models. In this study, we evaluate the performance of the minimum-mean-square-error (MMSE) estimator in terms of the normalized mean square error (NMSE) in the uplink of M-MIMO systems over spatially correlated Rician fading. The NMSE allows for easy comparison of different M-MIMO configurations, serving as a relative performance indicator. Besides, it is an advantageous metric due to its normalization, scale invariance, and consistent performance indication across diverse scenarios. In the system model, we assume imperfections in channel estimation and that the random angles in the correlation model follow a Gaussian distribution. For this scenario, we derive an accurate closed-form expression for calculating the NMSE, which is validated via Monte-Carlo simulations. Our numerical results reveal that as the Rician K-factor decreases, approaching Rayleigh fading conditions, the NMSE improves. Additionally, spatial correlation and a reduction in the antenna array interelement spacing lead to a reduction in NMSE, further enhancing the overall system performance.

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“…In this appendix, the covariance matrix of the channel estimation error is simplified. For this, in the following, we calculate each term of Equation (18).…”

Section: Appendix Covariance Matrix Calculation For the Channel Estim...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Performance of MMSE Channel Estimation in Massive MIMO Systems over Spatially Correlated Rician Fading Channels

Arellano,

Altamirano,

Carvajal Mora

et al. 2024

Wireless Communications and Mobile Computing

Self Cite

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Interference Reduction Factor Evaluation for Cylindrical and Parallelepiped Antenna Arrays in Massive MIMO Systems

Lara,

Altamirano,

Narváez

et al. 2024

2024 IEEE Colombian Conference on Communications and Computing (COLCOM)

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On the Interference Reduction Factor in Massive MIMO System over Rician Fading Channels

Cited by 2 publications

References 16 publications

On the Performance of MMSE Channel Estimation in Massive MIMO Systems over Spatially Correlated Rician Fading Channels

On the Performance of MMSE Channel Estimation in Massive MIMO Systems over Spatially Correlated Rician Fading Channels

Interference Reduction Factor Evaluation for Cylindrical and Parallelepiped Antenna Arrays in Massive MIMO Systems

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