Downlink Performance of Massive-MIMO Systems Using EVD-Based Channel Estimation

Xu, Weiyang; Wang, Xiang; Jia, Yunjian; Youjun, LI; Yi, Yang

doi:10.1109/tvt.2016.2590943

Cited by 21 publications

(8 citation statements)

References 27 publications

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“…Actually, the rank of the correlation matrix equals to M if and only if ρ = 1. Resolving (12) into its partial fractions with repeated roots yields…”

Section: A Exact Symbol-error Ratementioning

confidence: 99%

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Performance of ED-Based Non-Coherent Massive SIMO Systems in Correlated Rayleigh Fading

Xie

Wang

et al. 2019

IEEE Access

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In recent studies, a simple non-coherent communications' scheme based on energy detection is proposed for massive single-input multiple-output systems, where transmit symbols are decoded by averaging the received signal energy across all antennas. In this paper, we investigate the effect of correlated Rayleigh fading on the performance of the aforementioned systems. Specifically, we derive the analytical expressions of the symbol error rate, achievable rate, and outage probability. Furthermore, the asymptotic behaviors of these expressions in regimes of large numbers of receive antennas, high channel correlation, and high signal-to-noise ratio are investigated. The analytical results demonstrate the adverse impact of channel correlation on the error probability, which can be attributed to the fact that channel correlation reduces the degrees of freedom. Our analysis also shows that channel correlation poses little influence on the achievable rate. Besides, an upper bound of the achievable rate is given when the number of receive antennas goes to infinity. Finally, the numerical results are presented to verify our analysis. INDEX TERMS Non-coherent massive single-input multiple-output (SIMO), correlated Rayleigh fading, symbol-error rate (SER), achievable rate.

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“…Actually, the rank of the correlation matrix equals to M if and only if ρ = 1. Resolving (12) into its partial fractions with repeated roots yields…”

Section: A Exact Symbol-error Ratementioning

confidence: 99%

“…This is dubbed pilot contamination, which has become a fundamental performance bottleneck in massive MIMO systems. [8] and references therein provide a good overview of schemes to tackle this problem, such as protocolbased, precoding and blind or semi-blind methods [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

Performance of ED-Based Non-Coherent Massive SIMO Systems in Correlated Rayleigh Fading

Xie

Wang

et al. 2019

IEEE Access

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“…6 shows the gain in the spectral efficiency when adding one more antenna at each user, and illustrates how this gain varies for small and large pilot reuse factor β when BS performs LS channel estimation (for the first antenna) and MF for data detection. In this figure we compare between four different schemes: (i) singleantenna user: conventional system, (ii) semi-blind: double-antenna user using semi-blind channel estimation method for second antenna (5), (iii) eigenvalue decomposition (EVD): double-antenna user using EVD on the covariance matrix of the received samples [9] [EVD is a commonly known method of blind channel estimation, which is used in many recent literatures [23,24]. Although the eigenvalue decomposition method is a blind channel estimation method, it uses a short train of pilot sequences in order to solve the multiplicative matrix ambiguity.…”

Section: Numerical Analysismentioning

confidence: 99%

Optimal number of user antennas in a constrained pilot‐length massive MIMO system

Alwakeel¹,

Mehana²

2019

IET Communications

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“…To maximize the gain of massive multiple-input-multipleoutput (MIMO) systems, having accurate channel state information (CSI) at the base station (BS) is essential [1]. In frequency division duplex (FDD) systems, the CSI is typically obtained by downlink (DL) channel estimation and then fed back to the BS [2], [3]. However, the conventional least squares channel estimation method requires a high pilot overhead that increases linearly with both the number of BS antennas and the length of channel impulse response (CIR), and becomes inefficient in massive MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

Downlink Channel Estimation for Massive MIMO Systems Relying on Vector Approximate Message Passing

Yao

Jiang

et al. 2019

IEEE Trans. Veh. Technol.

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To reduce the pilot overhead of downlink channel estimation in massive multiple-input-multiple-output (MIMO) systems, a sparse recovery algorithm relying on the vector approximate message passing (VAMP) technique is proposed. More specifically, an a-priori channel model characterized by a multivariate Bernoulli-Gaussian distribution is invoked for exploiting the common sparsity of massive MIMO channels, and the VAMP technique is used for jointly estimating the spatially correlated channels. Moreover, the hyperparameters of the apriori model are learned by invoking the expectation maximization (EM) algorithm. Our numerical results demonstrate that the proposed algorithm is capable of reducing the pilot overhead by 50% in massive MIMO systems.

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Downlink Performance of Massive-MIMO Systems Using EVD-Based Channel Estimation

Cited by 21 publications

References 27 publications

Performance of ED-Based Non-Coherent Massive SIMO Systems in Correlated Rayleigh Fading

Performance of ED-Based Non-Coherent Massive SIMO Systems in Correlated Rayleigh Fading

Optimal number of user antennas in a constrained pilot‐length massive MIMO system

Downlink Channel Estimation for Massive MIMO Systems Relying on Vector Approximate Message Passing

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