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Wireless Communications and Mobile Computing
Volume 2018, Article ID 6396173, 9 pages
https://doi.org/10.1155/2018/6396173
Research Article

Modelling and Analysis of Nonstationary Vehicle-to-Infrastructure Channels with Time-Variant Angles of Arrival

1Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway
2Universidad Autonoma de San Luis Potosi, 78290 San Luis Potosi, SLP, Mexico

Correspondence should be addressed to Matthias Pätzold; on.aiu@dlozteap.saihttam

Received 17 October 2017; Revised 16 January 2018; Accepted 4 February 2018; Published 15 March 2018

Academic Editor: Enrico M. Vitucci

Copyright © 2018 Matthias Pätzold and Carlos A. Gutierrez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In mobile radio channel modelling, it is generally assumed that the angles of arrival (AOAs) are independent of time. This assumption does not in general agree with real-world channels in which the AOAs vary with the position of a moving receiver. In this paper, we first present a mathematical model for the time-variant AOAs. This model serves as the basis for the development of two nonstationary multipath fading channels models for vehicle-to-infrastructure communications. The statistical properties of both channel models are analysed with emphasis on the time-dependent autocorrelation function (ACF), time-dependent mean Doppler shift, time-dependent Doppler spread, and the Wigner-Ville spectrum. It is shown that these characteristic quantities are greatly influenced by time-variant AOAs. The presented analytical framework provides a new view on the channel characteristics that goes well beyond ultra-short observation intervals over which the channel can be considered as wide-sense stationary.

1. Introduction

In a typical downlink scenario, where plane waves travel from a base station (BS) to a mobile station (MS) via a large number of fixed scattering objects, the angles of arrival (AOAs) of the received signals are changing along the moving route of the MS. Only for very short observation intervals in which the MS travels a few tens of the wavelengths [1], the temporal variation of the AOAs can be neglected justifying the wide-sense stationary assumption of multipath fading channels. The lengths of the stationary intervals during which the mobile radio channel can be considered as wide-sense stationary or quasi-stationary have been investigated (e.g., in [24] and the references therein). By pushing the observation interval beyond the stationary interval, the received signal captures nonstationary effects that call for new channel modelling approaches using time-frequency analysis techniques [5]. One of the effects that come with long observation intervals is that the AOAs and thus the Doppler frequencies are changing with time along the MS’s moving route.

Attempts to include the temporal variations of the AOAs in mobile radio channel models have been made in [68]. In [6], a nonstationary multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) channel model has been derived by assuming that the AOAs and AODs are piecewise constant. In [7], a proposal has been made for the extension of the IMT-Advanced channel model [9] by replacing the time-invariant model parameters, such as the propagation delays, AOAs, and the angels of departure (AODs) by time-variant parameters. In [8], a nonstationary one-ring model has been introduced in which the time-variant AOAs have been modelled by stochastic processes rather than random variables.

This paper is an extended version of our conference paper [10]. It expands on the recent results by studying the impact of time-variant AOAs on the statistical properties of multipath fading channels. It is shown that the multipath fading channel becomes non-wide-sense stationary if the AOAs change with time. Two new nonstationary channel models with time-variant AOAs are derived. The first one has an instantaneous channel phase that is related to the instantaneous Doppler frequency via the phase-frequency relationship [11], while the second one is based on a sum-of-cisoids (SOC) model in which the time-independent Doppler frequencies are replaced by time-dependent Doppler frequencies. The latter approach is simple, straightforward, and intuitive but results in a less accurate nonstationary channel model. The statistical properties of both channel models are investigated with emphasis on the time-dependent autocorrelation function (ACF), time-dependent mean Doppler shift, time-dependent Doppler spread, and the Wigner-Ville spectrum. Our analysis shows that our first proposed nonstationary channel model is consistent with respect to the mean Doppler shift and the Doppler spread, while this consistency property is not fulfilled by the SOC model with time-variant Doppler frequencies. The two proposed nonstationary channel models provide a trade-off between accuracy and complexity concerning the mathematical expressions.

One of the main differences between [68] and our paper is that the AOAs are modelled in different ways. For example, in [6], the AOAs are modelled as piecewise constant functions, that is, these parameters are considered as constant apart from a finite number of jumps, while in our paper the AOAs are modelled in our paper as continuous time-variant functions. Another difference is that the models in [68] have been developed for different propagation scenarios. The V2V channel model in [6] has been developed to simulate propagation scenarios which are typical for T-junctions. The BS-to-MS channel model in [7] covers basically the same scenarios as the IMT-Advanced channel model [9], while the model in [8] is restricted to scenarios that can be generated by the one-ring model under the assumption of isotropic scattering. This contrasts with our nonstationary generic model which is not restricted to any specific propagation scenario. The drawback of the models in [68] is that they are inconsistent with respect to the mean Doppler shift and the Doppler spread. Our preferred model avoids this drawback by using an integral relationship between the instantaneous channel phases and the corresponding instantaneous Doppler frequencies.

The organization of this paper is as follows. Section 2 presents the derivation of two nonstationary multipath fading channel models with time-variant AOAs. Their statistical properties will be analysed in Section 3. The numerical key results of our study are visualized in Section 4. Section 5 provides guidelines for various extensions of the model. Finally, Section 6 draws the conclusion and suggests possible future research topics in relation to the issues addressed in this paper.

2. Derivation of the Nonstationary Multipath Channel Models

2.1. Time-Variant AOAs

We consider a downlink non-line-of-sight (NLOS) propagation scenario in which a fixed BS operates as transmitter, and an MS acts as receiver. It is supposed that the BS and the MS are equipped with omnidirectional antennas. The BS antenna is elevated and unobstructed by any object, whereas the MS antenna is surrounded by a large number of fixed scattering objects called henceforth scatterers . The coordinate system has been chosen such that the MS is located at the origin of the -plane at . Furthermore, it is assumed that the MS moves with constant velocity in the direction determined by the angle of motion as indicated in Figure 1. For reasons of clarity, this figure highlights only the location of the scatterer from which the MS receives the th multipath component (plane wave) in the form of , where denotes the path gain which is supposed to be constant, and is the associated channel phase that will be studied in Section 2.3. The corresponding AOA is defined as the angle between the propagation direction of the th incident plane wave and the -axis, that is,for , where denotes the four-quadrant inverse tangent function. It should be mentioned that the four-quadrant inverse tangent function returns the angle of the vector with the positive -axis in the range . This function contrasts with the inverse tangent function , whose results are limited to the interval . In (1), the symbols and denote the coordinates of the scatterer ; and and indicate the position of the MS at time . According to (1), the AOA is a nonlinear function of time , which can be turned into a linear function by developing in a Taylor series around and retaining only the first two terms. This results in the following model for the time-variant AOA:whereIn (4), denotes the distance from the scatterer to the origin of the -plane, that is, , as can be deduced from the geometrical model in Figure 1. In Section 4, it is shown that the two-term Taylor series expansion of in (2) is sufficiently accurate for small observation intervals .

Figure 1: A multipath propagation scenario with time-variant AOAs .
2.2. Time-Variant Doppler Frequencies

Owing to the Doppler effect combined with the new feature that the AOAs vary with time, it follows that the th incident plane wave highlighted in Figure 1 experiences a time-variant Doppler shift of that can be expressed by using (2) asfor , where stands for the maximum Doppler frequency. For a given propagation scenario with constant parameters , , , and , the time-variant Doppler shift is a deterministic function of time. Otherwise, if one or several model parameters, for example, and thus , are random variables, then represents a stochastic process. If the MS moves during the time interval , then describes a curve starting from the initial Doppler frequency and ending with the finishing Doppler frequency .

The time-dependent mean Doppler shift and the time-dependent Doppler spread can be computed according to

2.3. Instantaneous Channel Phase

The instantaneous channel phase of the th multipath component is related to the instantaneous Doppler frequency via the phase-frequency relationship [5, Eq. (1.3.40)]for . Using (5), the instantaneous phase can be developed as follows:where denotes the initial phase at . The initial phases are generally unknown and modelled by independent identically distributed (i.i.d.) random variables, each with uniform distribution over the interval : that is, . Equation (9) tells us that the instantaneous phase is not only a nonlinear function of time but also periodic with period if the AOA varies with time according to (2). In the limit , however, it can be shown by applying L’Hôpital’s rule to (9) thatwhere . This result reveals a linear relationship between the instantaneous phase and time , which holds only for constant AOAs . It should be noticed that the expression in (10) can be identified as the standard phase term of SOC channel models for Rayleigh/Rice fading channels [12, Section ].

A simpler but less accurate expression than (9) can be obtained for the instantaneous phase by developing in a first-order Taylor series around as follows:where denotes the time derivative of at . By comparing the last two equations, we can conclude that the linear phase term can be obtained from the nonlinear phase term [see (9)] either in the limit or by developing the nonlinear phase in a first-order Taylor series around .

2.4. Complex Channel Gain

A model for the complex channel gain, denoted by , of a narrowband multipath fading channel is obtained by the superposition of all plane wave components , that is,Substituting the instantaneous channel phase according to (9) in (12) results in the complex channel gain of the proposed nonstationary multipath fading channel with time-variant AOAsOn the other hand, starting from the SOC model for Rayleigh fading channels [12, Eq. (4.97)] and replacing there intuitively the time-independent Doppler frequencies by the instantaneous Doppler frequencies according to (5) provide the complex channel gain in a much simpler form, namely,This intuitive mathematical manipulation results in a nonstationary channel model that is inconsistent with respect to the mean Doppler shift and the Doppler spread , as we will see in Section 3.2. Although the expression in (14) is mathematically simpler than the one in (13), the difference is not significant in terms of implementation costs and simulation time.

From the discussions in the previous subsection, it can be summed up that the two complex channel gains in (13) and (14) include the original SOC model [13]as a special case that arises if the AOA is supposed to be either constant or if the instantaneous phase in (12) is approximated by a first-order Taylor series [see (11)]. The main difference between the three stochastic channel models above is that the former two are non-wide-sense stationary, whereas the third one is wide-sense stationary. The statistical properties of the SOC model have been studied in [13], while those of the new non-wide-sense stationary models will be analysed in the next section.

3. Analysis of the Nonstationary Multipath Channel Models

3.1. Time-Dependent ACF

The time-dependent ACF of a complex stochastic process is defined aswhere denotes the expectation operator and stands for the complex conjugation operator. In the Appendix, it is proved that the time-dependent ACF of the complex channel gain in (13) can be written aswhere denotes the sinc function, which is defined by .

Analogously, it can be shown that the time-dependent ACF of the complex channel gain introduced in (14) can be expressed bywhere is the time-variant Doppler shift in (5) and denotes its derivative with respect to time .

For the special case that the AOA is constant, that is, , it is obvious that the two time-dependent ACFs in (17) and (18) reduce towhich represents the ACF of the SOC model described by (15). In this case, the ACF depends only on the time separation but not on time , which was to be expected, because the SOC process is wide-sense stationary.

Furthermore, if and , then the expressions in (17)–(19) reduce to the ACF , where denotes the mean power of the complex channel gain , and is the zeroth-order Bessel function of the first kind [14, Eq. (8.411-1)]. In other words, the proposed nonstationary multipath fading channel models include the classical Jakes/Clarke model [1, 15] as a special case.

3.2. Time-Dependent Mean Doppler Shift and Time-Dependent Doppler Spread

From the time-dependent ACF , the time-dependent mean Doppler shift and the time-dependent Doppler spread can be derived by means ofrespectively, where denotes the first (second) order derivative of with respect to at . Inserting (17) in (20) and (21) results after some straightforward mathematical steps in the following closed-form solutions:A comparison of (22) with (6) and (23) with (7) reveals that the equalities and hold, from which we can conclude that the proposed nonstationary multipath fading channel model described by (13) is consistent with respect to both the mean Doppler shift and the Doppler spread.

On the other hand, if we insert (18) in (20) and (21), then we obtainThis result demonstrates that the simple nonstationary channel model introduced in (14) is inconsistent with respect to the mean Doppler shift and the Doppler spread, because and hold. Concerning the SOC process in (15), we mention for completeness that the equalities and hold, where and are the same quantities as in (22) and (23), respectively, if we replace by . Thus, the SOC model is consistent with respect to the mean Doppler shift and the Doppler spread. More information on the consistency of nonstationary multipath fading channels can be found in [16].

3.3. Wigner-Ville Spectrum

The Wigner-Ville spectrum, which is also called the time-varying spectrum or the evolutive spectrum, will be denoted by  . This function is defined as the Fourier transform of the time-dependent ACF with respect to [11]: that is,Inserting (17) in (26) and using the property , we can express the Wigner-Ville spectrum of the proposed nonstationary multipath fading channel model described by (13) asFor the wide-sense stationary case, for which holds, the Wigner-Ville spectrum in (27) reduces to the Doppler power spectral density (PSD) of the SOC process presented in (15), that is,Furthermore, for the isotropic scattering case, in which and are i.i.d. random variables with and , we obtain the Jakes/Clarke PSD [1, 15] after computing the expected value of in (28). Hence, the Wigner-Ville spectrum in (27) includes the classical Jakes/Clarke Doppler spectrum as a special case.

4. Numerical Results

This section presents a selection of numerical results to illustrate the main findings of this paper. In all considered propagation scenarios, we have set the number of multipath components to . The gains and initial AOAs have been computed by using the extended method of exact Doppler spread (EMEDS) [17]. According to this method, the parameters and are given byrespectively, and the initial phases are considered as realizations of independent random variables, each characterized by a uniform distribution over the interval . If not stated otherwise, the radii in Figure 1 have been set to 50 m for all . For the mean power (variance) of the in-phase and quadrature components of , we have chosen the value . The carrier frequency was set to 5.9 GHz, and the maximum Doppler frequency was supposed to be Hz. This corresponds to a mobile speed of km/h, where we have assumed that the MS moves in -direction, implying that the angle of motion equals zero, that is, .

Figure 2 depicts the trend of the time-variant Doppler frequencies by using the exact expression for the AOAs according to (1). For comparison, this figure also shows the behaviour of for the approximate solution of in (2). Figure 2 shows clearly that the first-order approximation is quite good over the interval from 0 to s during which the MS has covered a distance of 10 m.

Figure 2: Trend of the time-variant Doppler frequencies by using the exact solution (black solid line) and the approximate solution (blue dashed line), where .

Figure 3 illustrates the signal envelope by using the SOC model [see (15), Case ], the proposed nonstationary multipath fading channel model [see (13), Case ], and the simple nonstationary model [see (14), Case ]. This figure demonstrates clearly that the temporal variations of the AOAs have a great influence on the temporal behaviour of the signal envelope . It is interesting to note that the three signal envelopes are identical at and very similar for small values of , but they differ considerably with increasing values of . It should be mentioned that different realizations of the initial phases result in different sample functions of the signal envelopes, but the aforementioned trend is the same for all realizations.

Figure 3: Illustration of the signal envelope of a sample function of a wide-sense stationary SOC process [see (15)] in comparison with the signal envelopes of the nonstationary processes described by (13) and (14).

Figures 4 and 5 present the ACF of the SOC process in (15) and the time-dependent ACF of the nonstationary process in (13), respectively. It can be observed that both ACFs are identical at the origin , but the temporal correlation properties of the nonstationary model differ more and more if time proceeds. This means that the temporal variations of influence greatly the fading behaviour of the signal envelope .

Figure 4: ACF of a SOC process with constant AOAs for .
Figure 5: Time-dependent ACF of the proposed nonstationary process with time-variant AOAs for .

Figures 6 and 7 depict the corresponding Doppler PSD [see (28)] of the SOC process in (15) and the Wigner-Ville spectrum [see (27)] of the nonstationary process in (13), respectively. A comparison of the two spectral representations shows clearly that the influence of the time-variant AOAs cannot be neglected. This statement is obvious as the Doppler frequencies of the Wigner-Ville spectrum (Doppler PSD) associated with the stationary SOC process remain constant over time (see Figure 6), while the spectral components of the nonstationary process experience a drift if time proceeds (see Figure 7). Finally, we mention that the results in Figure 7 have been obtained numerically by setting the upper limit of in the integral of (27) to  s and evaluating the integral by considering samples, which results in a resolution of  s.

Figure 6: Wigner-Ville spectrum (Doppler PSD) of an SOC process with constant AOAs for .
Figure 7: Wigner-Ville spectrum of the proposed nonstationary process with time-variant AOAs for .

Figures 8 and 9 are devoted to a study on the influence of the (ring) radii on the time-dependent mean Doppler shift and the time-dependent Doppler spread , respectively. The presented graphs show that the smaller the radii are, the faster the functions and are changing over time . Figure 8 shows also a comparison between the time-dependent mean Doppler shift of the consistent model described by (14) and the inconsistent model according to (15). Both models have the same mean Doppler shift at the origin , but the mean Doppler shifts deviate considerably from each other with increasing values of time . The same statement holds for the time-dependent Doppler spread shown in Figure 9. These results underline the importance of consistency, as the deviations between (22) and (24) as well as between (23) and (25) cannot be neglected.

Figure 8: Time-dependent mean Doppler shift of the nonstationary channel models for different values of the radii .
Figure 9: Time-dependent Doppler spread of the nonstationary channel models for different values of the radii for .

5. Model Extensions

To isolate the effect of time-variant AOAs on the Doppler characteristic, we have assumed that the channel is frequency-nonselective and that the transmitter and receiver are equipped with single omnidirectional antennas. To relax these assumptions, we will provide some guidelines on model extensions in the following subsections.

5.1. Extension to Frequency-Selectivity

Starting from the narrowband multipath fading channel model in (12) and taking into account the fact that the th plane wave component will be received after a time-variant propagation delay denoted by , the impulse response of the resulting non-wide-sense stationary single-input single-output channel model can be expressed aswhere is given by (9) for the consistent model or by for the inconsistent model. The time-variant delay can be derived from the geometrical model in Figure 1 aswhere denotes the speed of light, is the distance between the BS and MS, and .

5.2. Extension to MIMO

Let denote the impulse response of a frequency-selective MIMO channel with transit and receive antennas; then the propagation link from the th transmit antenna to the th receive antenna can be modelled asfor and , where is the same as in [18, Eq. ] apart from the fact that we have to replace there the time-invariant quantities and by time-variant quantities and , respectively. The expression in (33) can be derived by applying the design steps of the generalized principle of deterministic channel modelling [12, Section ].

5.3. Other Model Extensions

The proposed model is only applicable to omnidirectional antennas. The extension to directional antennas is possible through a proper adjustment of the constant path gains , which have to be replaced by time-variant path gains . The temporal characteristics of depend on the antenna pattern and the direction in which the MS moves. It is also possible to consider birth-and-death effects of the scatterers. This extension results in a multiplication of each multipath component by a birth-and-death process or, equivalently, by replacing the time-invariant path gains by proper time-variant path gains . The analysis of the Wigner-Ville spectrum of nonstationary mobile radio channels with time-variant path gains is substantially different from the analysis in Section 3.3 and beyond the scope of this paper.

6. Conclusion

In this paper, we have developed and analysed multipath fading channel models with time-variant AOAs. Our study has shown that the effect of time-variant AOAs results in a non-wide-sense stationary multipath fading channel model. Expressions have been derived for the time-dependent ACF, time-dependent mean Doppler shift, time-dependent Doppler spread, and the Wigner-Ville spectrum of the proposed non-wide-sense stationary channel model. By comparing these statistical quantities with known results of studies assuming constant AOAs, we can conclude that the assumption of constant AOAs is only justified for very short observation intervals. The proposed nonstationary channel model allows extending the observation interval over a wider range without losing accuracy. The price for this added accuracy is a higher degree of complexity concerning the mathematical expressions.

One of the remaining problems that might be tackled in an upcoming study is to develop quantitative methods for the investigation of the length of the observation interval over which the proposed nonstationary channel models are sufficiently accurate. Another topic could be to extend the presented framework to the modelling of MIMO channels with time-dependent AOAs.

Appendix

Derivation of the Time-Dependent ACF in (17)

Substituting (13) in the definition of the time-dependent ACF givesUsing if and 0 if , we obtainwhere we have used the sinc function defined as .

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

References

  1. W. C. Jakes, Ed., Microwave Mobile Communications, IEEE Press, Piscataway, NJ, USA, 1994. View at Publisher · View at Google Scholar
  2. R. He, O. Renaudin, V.-M. Kolmonen et al., “Characterization of quasi-stationarity regions for vehicle-to-vehicle radio channels,” IEEE Transactions on Antennas and Propagation, vol. 63, no. 5, pp. 2237–2251, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Umansky and M. Pätzold, “Stationarity test for wireless communication channels,” in Proceedings of the 2009 IEEE Global Telecommunications Conference, GLOBECOM 2009, Honolulu, Hawaii, USA, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Ispas, C. Schneider, G. Ascheid, and R. Thomä, “Analysis of the local quasi-stationarity of measured dual-polarized MIMO channels,” IEEE Transactions on Vehicular Technology, vol. 64, no. 8, pp. 3481–3493, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Boashash, Ed., Time-Frequency Signal, Analysis and Processing: A Comprehensive Reference, Elsevier Academic Press, 2nd edition edition, 2015.
  6. A. Chelli and M. Pätzold, “A non-stationary MIMO vehicle-to-vehicle channel model based on the geometrical T-junction model,” in Proceedings of the 2009 International Conference on Wireless Communications and Signal Processing, WCSP 2009, Nanjing, China, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Zhu, Y. Zhu, T. Zhang, and Z. Zeng, “A time-variant MIMO channel model based on the IMT-Advanced channel model,” in Proceedings of the 2012 International Conference on Wireless Communications and Signal Processing, WCSP 2012, Huangshan, China, 2012.
  8. A. Borhani and M. Pätzold, “A non-stationary one-ring scattering model,” in Proceedings of the 2013 IEEE Wireless Communications and Networking Conference, WCNC 2013, pp. 2661–2666, Shanghai, China, April 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. ITU-R Rep. M2135, “Guidelines for evaluation of radio interface technologies for IMT-Advanced,” 2008, https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2135-2008-PDF-E.pdf.
  10. M. Pätzold and C. A. Gutiérrez, “Modelling and analysis of nonstationary multipath fading channels with time-variant angles of arrival,” in Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, Australia, June 2017.
  11. W. Martin, “Time-frequency analysis of random signals,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1325–1328, Paris, France, May 1982. View at Publisher · View at Google Scholar
  12. M. Pätzold, Mobile Radio Channels, John Wiley & Sons, Chichester, England, 2nd edition edition, 2011.
  13. M. Pätzold and B. Talha, “On the statistical properties of sum-of-cisoidsbased mobile radio channel simulators,” in Proceedings of the 10th International Symposium on Wireless Personal Multimedia Communications, WPMC 2007, pp. 394–400, Jaipur, India, 2007.
  14. I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products, Elsevier Academic Press, 7th edition edition, 2007. View at MathSciNet
  15. R. H. Clarke, “A statistical theory of mobile-radio reception,” Bell Labs Technical Journal, vol. 47, no. 6, pp. 957–1000, 1968. View at Publisher · View at Google Scholar
  16. M. Pätzold, C. A. Gutiérrez, and N. Youssef, “On the consistency of non-stationary multipath fading channels with respect to the average doppler shift and the doppler spread,” in Proceedings of the 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017, San Francisco, CA, USA, March 2017. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Pätzold, B. O. Hogstad, and N. Youssef, “Modeling, analysis, and simulation of MIMO mobile-to-mobile fading channels,” IEEE Transactions on Wireless Communications, vol. 7, no. 2, pp. 510–520, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Pätzold and C. A. Gutiérrez, “Enhancing the Resolution of the Spectrogram of Non-Stationary Mobile Radio Channels by Using Massive MIMO Techniques,” in Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, Canada, September 2017. View at Publisher · View at Google Scholar