An Efficient Downlink Channel Estimation Approach for TDD Massive MIMO Systems

Nan, Yang; Zhang, Li; Sun, Xiaoying

doi:10.1109/vtcspring.2016.7504124

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…The data is transmitted in the UL by using symbols where denotes the length of the coherence block. The BS uses the UL pilot s for data transmission in the DL by using channel reciprocity [10]. The DL data transmission phase is achieved by precoding scheme at the BS.…”

Section: A Uplink Channel Estimationmentioning

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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Section: A Uplink Channel Estimationmentioning

confidence: 99%

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

Rayi*,

Prasad

2020

IJITEE

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“…The signals are coherently combined and coordinated at the receiving antennas. However, this process requires the perfect CSI at the BS from the UL channel [32][33]. To improve the capacity of massive MIMO systems, spectral resources were reused in different cells.…”

Section: Average Spectral Efficiencymentioning

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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“…Things are getting worse at the user terminal (UT) side where each user has to accurately estimate channels from the large number of BS antennas. For this reason, the time division duplex (TDD) mode is more desirable for massive MIMO systems, where the acquired uplink CIR at the BS can be directly fed back to the users thanks to the channel reciprocity in slow time-varying channels [2] or used as basis to obtain the downlink CIR in fast time-varying channels [3]. In this way, the heavy overhead of the pilots in the downlink transmission as well as the battery power consumption at the user terminal for channel estimation can be saved.…”

Section: Takedownmentioning

confidence: 99%

Decision Aided Uplink Compressive Channel Estimation for Massive MIMO Systems

Nan

Sun

Zhang

2017

Wireless Pers Commun

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Thank to the observation that in massive multi-input multi-output (MIMO) systems, the channels associated with different base station (BS) antennas may share common sparse support, the significant path delays can be accurately captured by only few pilots, leading to a reduction of pilot overhead. However, when the number of pilots is small, the path gains can not be accurately estimated and this limits the system performance. To solve this problem, in this paper we propose a decision aided compressive sensing based channel estimation scheme, which utilizes the decoded data to refine the channel estimation. This scheme can effectively improve the channel estimation without increasing the length of pilot sequence, which is confirmed by both analyses and simulation results. Keywords Massive MIMO • channel estimation • compressive sensing 1 Introduction As a promising technology for the future fifth-generation wireless communication, massive multiple-input multiple-output (MIMO) systems with a large

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An Efficient Downlink Channel Estimation Approach for TDD Massive MIMO Systems

Cited by 5 publications

References 11 publications

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

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

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

Decision Aided Uplink Compressive Channel Estimation for Massive MIMO Systems

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