Impact of Angular Spread in Moderately Large MIMO Systems Under Pilot Contamination

Rupasinghe, Nadisanka; Yapıcı, Yavuz; Iscar, Jorge; Güvenç, İsmail

doi:10.1109/tvt.2018.2866799

Cited by 4 publications

(14 citation statements)

References 33 publications

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“…Equations 20, (26) and (27) are powerful tools for interpreting the behavior of ray-based massive MIMO channels. Note that (52) in Appendix B demonstrates that κ has the alternative expression,…”

Section: Implications Of the Sinr Resultsmentioning

confidence: 99%

“…We also note that in the high SNR regime, we have ρM β (k) /(1 + ρM β (k) ) ≈ 1 and with this approximation we have the simplified version of (26) given by…”

Section: B Mmse Processingmentioning

confidence: 92%

“…Hence, as ρ → ∞ the second term in (28) vanishes and the SE approximation using (27) converges to the equivalent approximation using (26). Conversely, at low SNR both (26) and (27) are O(ρ) so the error in using (27) is also O(ρ). Hence, (27) is a convenient indicator of performance across all SNRs.…”

Section: B Mmse Processingmentioning

confidence: 95%

“…For example, it is often assumed that the ray angles [21] are uniform over the whole range (180 o in elevation and 360 o in azimuth) or the sine of the ray angles [24], [25] is uniform over [ −1, 1]. More recently, work has appeared on DL MRT and regularized ZF [26] with pilot contamination in ray-based channels but yet again the angles are restricted to be uniformly distributed.…”

mentioning

confidence: 99%

“…For instance, recent measurement data [12] has shown that the ray angles in azimuth and elevation domains across multiple clusters are Gaussian and Laplacian distributed, respectively, whereas angles within a cluster are well described by Laplacian distributions. 3GPP channel models [16] also follow a similar form with more complex statistical distributions, which are not treated in the prior state-of-the-art [26]- [30] due to the underlying mathematical complexities. Following this line of reasoning, the models presented in this paper are inherently general and, thus, analysis proves to be extremely challenging.…”

mentioning

confidence: 99%

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Analytical Framework for Full-Dimensional Massive MIMO With Ray-Based Channels

Miller

Smith

Dmochowski

et al. 2019

IEEE J. Sel. Top. Signal Process.

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The performance of baseband beamforming in multiuser multiple-input multiple-output (MU-MIMO) systems has been extensively studied for simplified statistical channel models where no angular parameters are taken into account. In contrast, there is little performance analysis with ray-based models, which are more physically motivated, feature prominently in standardization and have been experimentally validated. Thus, unlike previous studies, we present a mathematical framework to analyze the performance of zero forcing (ZF) and minimum mean-squared error (MMSE) combining. Using a central result for averaging in the angular domain, we derive tight approximations for the uplink signal-to-noise ratio and signal-to-interference-and-noise ratio (SINR) for ZF and MMSE processing, respectively, and the resulting spectral efficiencies. The remarkably simple expressions offer the following insights into the effects of the propagation environment. We demonstrate an improvement in performance when moving from vertical uniform rectangular array (URA) to horizontal URA to uniform linear array (ULA) antenna configurations. There is also a corresponding increase in the robustness of the performance to propagation scenarios. We demonstrate that under specific conditions increasing the angular spread can decrease the SINR for a ULA-an unexpected behavior which we link to the effects of end-fire radiation. Furthermore, our results allow us to investigate the impact of different array configurations and system parameters on the rate of convergence to favorable propagation conditions. Finally, we evaluate the spatial correlation properties intrinsically present in ray-based models, and compare them to the commonly used simple exponential model which yields equal, fixed correlation characteristics for each user. Index Terms-Ergodic spectral efficiency, favorable propagation, linear processing, MU-MIMO, ray-based channel models. I. INTRODUCTION Performance analysis of linear transceiver techniques for conventional and massive multiuser multiple-input multipleoutput (MU-MIMO) systems is well advanced for simple statistical channel models [2]-[7]. Early work on independent, identically distributed Rayleigh fading channels has been extended to a wide range of more complex and realistic channel C. L. Miller and P. A. Dmochowski are with the School of Engineering and Computer Science,

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Section: Implications Of the Sinr Resultsmentioning

confidence: 99%

“…We also note that in the high SNR regime, we have ρM β (k) /(1 + ρM β (k) ) ≈ 1 and with this approximation we have the simplified version of (26) given by…”

Section: B Mmse Processingmentioning

confidence: 92%

Section: B Mmse Processingmentioning

confidence: 95%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Analytical Framework for Full-Dimensional Massive MIMO With Ray-Based Channels

Miller

Smith

Dmochowski

et al. 2019

IEEE J. Sel. Top. Signal Process.

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Multi-User Processing for Ray-Based Channels

Miller

Dmochowski

Smith

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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The performance of linear multiuser multiple-input multiple-output (MU-MIMO) systems has been extensively studied for classical statistical channel models. In contrast, there is little analysis for ray-based models, which are physically motivated, feature prominently in standards and have been experimentally validated. Thus, we present a novel analysis framework for zero forcing (ZF) and maximal ratio combining (MRC) applicable to such models. Specifically, using a central result for averaging in the angular domain, we derive accurate expressions for ZF signalto-noise ratio and MRC signal, interference and noise powers. The remarkably simple expressions offer the following insights into the effects of the propagation environment. While ZF is robust to parameters such as cluster and subray angle spreads, MRC interference is highly sensitive to them. We show that the performance scales linearly with the number of antennas, and that it degrades with narrow angle spreads and as the propagation moves toward the antenna end-fire. Finally, by evaluating the variance of the MRC interference, we observe that an approximation to the MRC SINR widely used for classical statistical models, is inaccurate in ray-based channels.

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Super-Resolution of Wireless Channel Characteristics: A Multitask Learning Model

Wang,

Guan,

et al. 2023

IEEE Trans. Antennas Propagat.

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Impact of Angular Spread in Moderately Large MIMO Systems Under Pilot Contamination

Cited by 4 publications

References 33 publications

Analytical Framework for Full-Dimensional Massive MIMO With Ray-Based Channels

Analytical Framework for Full-Dimensional Massive MIMO With Ray-Based Channels

Multi-User Processing for Ray-Based Channels

Super-Resolution of Wireless Channel Characteristics: A Multitask Learning Model

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