Closed-formed distribution for the SINR of MMSE-detected MIMO systems and performance analysis

Zhai, Kai; Ma, Zheng; Lei, Xianfu

doi:10.1016/j.aeue.2018.09.038

Cited by 10 publications

(15 citation statements)

References 19 publications

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“…Figure 2 presents the SEP performance of 16 QAM, and 64 QAM signaling for an uncoded Large-Scale MIMO system with an MMSE receiver. It should be explained that, based on the derived SINR distribution, the corresponding SEP expression can be obtained by using the method of [16]. The black curves correspond to the system used in [16], where only Rayleigh fading channels were considered.…”

Section: Numerical Results and Simulationsmentioning

confidence: 99%

“…It should be explained that, based on the derived SINR distribution, the corresponding SEP expression can be obtained by using the method of [16]. The black curves correspond to the system used in [16], where only Rayleigh fading channels were considered. By comparison, it can be seen that large-scale fading has a greater impact on the performance of the system.…”

Section: Numerical Results and Simulationsmentioning

confidence: 99%

“…Influenced by the large-scale fading factor, the eigenvalue joint distribution function of boldHboldDHH is more complicated than the eigenvalue joint distribution function of boldHHH. Therefore, method [16] is not suitable for Equation (6).…”

Section: Distribution Of the Sinrmentioning

confidence: 99%

“…Impressively, a system named cell-free massive MIMO with many distributed access points was investigated in [15]; by applying MMSE detection, the author comprehensively analyzed the spectral efficiencies of the system, and the analysis results showed that the cell-free massive MIMO is a more promising technology than conventional Cellular massive MIMO in beyond-5G networks. Furthermore, a closed-form distribution for the output SINR of the MMSE receiver was derived in [16], for uncoded MIMO systems in ideal fast Rayleigh fading, and accurate symbol error performance was evaluated. In this paper, we focus on the investigation of the uplink performance for coded cellular large-scale multiuser MIMO systems with an MMSE receiver.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with [16], the channel considered in this paper is more in line with the actual multi-user MIMO scenario. In addition, we extend the analysis results to the encoded case.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers

Zhai

Lei

2019

Sensors

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In this paper, we estimate the uplink performance of large-scale multi-user multiple-input multiple-output (MIMO) networks. By applying minimum-mean-square-error (MMSE) detection, a novel statistical distribution of the signal-to-interference-plus-noise ratio (SINR) for any user is derived, for path loss, shadowing and Rayleigh fading. Suppose that the channel state information is perfectly known at the base station. Then, we derive the analytical expressions for the pairwise error probability (PEP) of the massive multiuser MMSE–MIMO systems, based on which we further obtain the upper bound of the bit error rate (BER). The analytical results are validated successfully through simulations for all cases.

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Section: Numerical Results and Simulationsmentioning

confidence: 99%

Section: Numerical Results and Simulationsmentioning

confidence: 99%

Section: Distribution Of the Sinrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Compared with [16], the channel considered in this paper is more in line with the actual multi-user MIMO scenario. In addition, we extend the analysis results to the encoded case.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations