Capacity enhancement of downlink massive MIMO systems with spatial channel correlation

Rayi, Prasad; Mandava, Prasad; Reddy, Ch Rajasekhara

doi:10.1109/inventive.2016.7830169

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…But ICI is the most significant problem which limits the SE. The impact of imperfect CSI on the SE was demonstrated in [38][39][40], when the BS antennas were M=300. By considering MMSE scheme the ICI can be decreased with increasing antennas at the BS.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Massive MIMO: Enhancement of Spectral and Energy Efficiency for 5G Perspective

Rayi¹,

Prasad²

2019

IJIES

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Massive Multi-Input and Multi-Output (MIMO) antenna system is considered as the key technology to improve both spectral efficiency (SE) and energy efficiency (EE) for 5G wireless systems. This paper evaluates the impact of perfect and imperfect channel state information (CSI) on the SE. The maximum SE of 200 bits/s/Hz achieved in a multi-cell scenario and its limiting factors were explored. We mainly demonstrated linear precoding algorithms such as zero-forcing (ZF), maximum ratio combing (MRC), and minimum mean square error (MMSE) for SE analysis. The explicit SE and EE expressions were derived from Shannon capacity calculations for single and multi-cell scenario. The proposed ZF and MRC precoding schemes were observed with SEs of 185 bit/s/Hz, and 180 bits/s/Hz respectively with pilot reuse factor with λ=7. The simulation results were validated by comparing the SE versus the number of base station (BS) antennas with receiving schemes by using MATLAB 2018a.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Massive MIMO: Enhancement of Spectral and Energy Efficiency for 5G Perspective

Rayi¹,

Prasad²

2019

IJIES

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“…The channel estimate is being a function of covariance matrix. The impact of spatial channel correlation among the channels will lead to decrease the channel capacity [11,12]. It is observed that the correlation between the UTs should be minimum to improve SE in a cell.…”

Section: Fig2 Proposed Frame Structurementioning

confidence: 99%

Achievable Capacity with Spatial Channel Correlation in Massive MIMO Multi-Cell Systems

Rayi*,

Prasad

2020

IJITEE

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Massive Multi-Input and Multi-Output (MIMO) antenna system provides unlimited capacity by the spatial multiplexing and array gain. Since the data rate has been limited by the coherence interference due to pilot contamination (PC). In this paper, we propose transmit combine and precoding schemes to achieve asymptotic capacity in multi-cell scenario, when the number of base station antennas tends to infinity. The impact of spatial channel correlation on channel capacity is explored by considering the co-variance matrices of the user –terminals (UT)s .To do this, we presented linear processing schemes such as MMSE,MRC, and ZF.Where MMSE achieves high capacity in the presence of large-scale fading and PC. Since the diagonals of the channel covariance matrices were designed with non- zero Eigen values and linearly independent. The results outperform and obtain asymptotic limit, when the co-variance of UTs are linearly independent. The results were simulated by using MATLAB 2018b.

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Antenna Selection for Spatial Correlated Channel by Exploiting Statistical CSI

Zhang

Cai

et al. 2022

2022 IEEE/CIC International Conference on Communications in China (ICCC)

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Capacity enhancement of downlink massive MIMO systems with spatial channel correlation

Cited by 7 publications

References 12 publications

Massive MIMO: Enhancement of Spectral and Energy Efficiency for 5G Perspective

Massive MIMO: Enhancement of Spectral and Energy Efficiency for 5G Perspective

Achievable Capacity with Spatial Channel Correlation in Massive MIMO Multi-Cell Systems

Antenna Selection for Spatial Correlated Channel by Exploiting Statistical CSI

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