What is Spatial Channel Correlation?

The channel between a single-antenna user and an M-antenna base station can be represented by an M-dimensional channel vector. The canonical channel model in the Massive MIMO literature is independent and identically distributed (i.i.d.) Rayleigh fading, in which the vector is a circularly symmetric complex Gaussian random variable with a scaled identity matrix as correlation/covariance matrix: \mathbf{h} \sim CN(\mathbf{0},\beta \mathbf{I}_M), where \beta is the variance.

With i.i.d. Rayleigh fading, the channel gain \|\mathbf{h}\|^2 has an Erlang(M,1/\beta)-distribution (this is a scaled \chi^2 distribution) and the channel direction \mathbf{h} / \|\mathbf{h}\| is uniformly distributed over the unit sphere in \mathbb{C}^M. The channel gain and the channel direction are also independent random variables, which is why this is a spatially uncorrelated channel model.

One of the key benefits of i.i.d. Rayleigh fading is that one can compute closed-form rate expressions, at least when using maximum ratio or zero-forcing processing; see Fundamentals of Massive MIMO for details. These expressions have an intuitive interpretation, but should be treated with care because practical channels are not spatially uncorrelated. Firstly, due to the propagation environment, the channel vector is more probable to point in some directions than in others. Secondly, the antennas have spatially dependent antenna patterns. Both factors contribute to the fact that spatial channel correlation always appears in practice.

One of the basic properties of spatial channel correlation is that the base station array receives different average signal power from different spatial directions. This is illustrated in Figure 1 below for a uniform linear array with 100 antennas, where the angle of arrival is measured from the boresight of the array.

Figure 1: The average signal power received at a Massive MIMO base station from different angular directions, as seen from the array. Spatially correlated fading implies that this average power is angle-dependent, while i.i.d. fading gives the same power in all directions.


As seen from Figure 1, with i.i.d. Rayleigh fading the average received power is equally large from all directions, while with spatially correlated fading it varies depending on in which direction the base station applies its receive beamforming. Note that this is a numerical example that was generated by letting the signal come from four scattering clusters located in different angular directions. Channel measurements from Lund University (see Figure 4 in this paper) show how the spatial correlation behaves in practical scenarios.

Correlated Rayleigh fading is a tractable way to model a spatially  correlation channel vector: \mathbf{h} \sim CN(\mathbf{0}, \mathbf{B}), where the covariance matrix \mathbf{B} is also the correlation matrix. It is only when \mathbf{B} is a scaled identity matrix that we have spatially uncorrelated fading. The eigenvalue distribution determines how strongly spatially correlated the channel is. If all eigenvalues are identical, then \mathbf{B} is a scaled identity matrix and there is no spatial correlation. If there are a few strong eigenvalues that contain most of the power, then there is very strong spatial correlation and the channel vector is very likely to be (approximately) spanned by the corresponding eigenvectors. This is illustrated in Figure 2 below, for the same scenario as in the previous figure. In the considered correlated fading case, there are 20 eigenvalues that are larger than in the i.i.d. fading case. These eigenvalues contain 94% of the power, while the next 20 eigenvalues contain 5% and the smallest 60 eigenvalues only contain 1%. Hence, most of the power is concentrated to a subspace of dimension \leq40. The fraction of strong eigenvalues is related to the fraction of the angular interval from which strong signals are received. This relation can be made explicit in special cases.

Figure 2: Spatial channel correlation results in eigenvalue variations, while all eigenvalues are the same under i.i.d fading. The larger the variations, the stronger the correlation is.


One example of spatially correlated fading is when the correlation matrix has equal diagonal elements and non-zero off-diagonal elements, which describe the correlation between the channel coefficients of different antennas. This is a reasonable model when deploying a compact base station array in tower. Another example is a diagonal correlation matrix with different diagonal elements. This is a reasonable model when deploying distributed antennas, as in the case of cell-free Massive MIMO.

Finally, a more general channel model is correlated Rician fading: \mathbf{h} \sim CN(\mathbf{b}, \mathbf{B}), where the mean value \mathbf{b} represents the deterministic line-of-sight channel and the covariance matrix \mathbf{B} determines the properties of the fading. The correlation matrix \mathbf{B}+\mathbf{b}\mathbf{b}^H can still be used to determine the spatial correlation of the received signal power. However, from a system performance perspective, the fraction k=\| \mathbf{b} \|^2/\mathrm{tr}(\mathbf{B}) between the power of the line-of-sight path and the scattered paths can have a large impact on the performance as well. A nearly deterministic channel with a large  k-factor provide more reliable communication, in particular since under correlated fading it is only the large eigenvalues of \mathbf{B} that contributes to the channel hardening (which otherwise provides reliability in Massive MIMO).

21 thoughts on “What is Spatial Channel Correlation?”

  1. How to make reconfigurable MIMO antenna arrays at 5G frequencies using phase shifters for wireless devices?

    1. Massive MIMO should be built with fully digital transceivers. Analog beamforming with phase shifters will only work satisfactory in special cases: single user communication with phase-calibrated arrays, line of sight propagation and a relatively small bandwidth.

  2. How does spatial correlation impact the channel capacity (data rate). Is there any tradeoff between spatial channel correlation and spectral efficiency? I would like write a paper on spatial channel correlation.

    1. It depends on how the users are distributed. Users with similar spatial channel correlation will cause more interference to each other, while users with very different spatial channel correlation cause less interference to each other. On the average, it appears that spatial channel correlation improves the spectral efficiency.

      This is discussed in depth in my new book, so I suggest that you read it before conducting research on the topic:

      Emil Björnson, Jakob Hoydis and Luca Sanguinetti (2017), “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency”, Foundations and Trends® in Signal Processing: Vol. 11, No. 3-4, pp 154–655. DOI: 10.1561/2000000093.

      1. Dear Professor,

        I have a question.

        Question: Can we use “AMP Algorithm” in “Correlated Rayleigh Fading Model” for Activity Detection of active user?

        Actually I am working on massive connectivity but I have seen many researchers follow i.i.d fading channel instead of Correlated fading channel.

        1. Yes, I think so.

          It is often more analytically tractable to deal with i.i.d. fading channels, which is why many research advances are first made for i.i.d. channels and then generalized to correlated fading.

      2. Dear Emil,

        I’m interested to know more about what you said:
        “On the average, it appears that spatial channel correlation improves the spectral efficiency.”

        Can you please suggest the chapter of your book and any other paper, where explains this a little bit…?

  3. How to generate interchannel correlation matrix in Matlab? I’m considering the scenario of multi-user MIMO, where users are close to each other.

    1. It is commonly assumed that the channel realizations of different users are statistically independent in the multi-user MIMO literature, even when the users are close to each other. With this assumption, the inter channel correlation matrix is zero.

      But if you want to generate more realistic channels that have some correlation, you can use the following Matlab implementation of 3GPP channel models:

      1. There is unfortunately not a simple way to measure spatial correlation. One way to measure it is via the eigenvalue spread of the spatial correlation matrices, as illustrated in Figure 2.6 of my book “Massive MIMO networks” (http://massivemimobook.com). A strongly correlated channel has a few large eigenvalues and many small eigenvalues. But if you compare the three curves in Figure 2.6, it is not easy to say that one is more correlated than the other.

        If you consider a particular type of channel model, the spatial correlation might be determined by a parameter. In particular, the “angular spread” is a common measure for spatial correlation in physical propagation models (such as the one used in Figure 2.6). A smaller angular spread corresponds to stronger spatial correlation.

  4. Dear Professor,
    I have a question about the spatial correlation matrix.
    As you also wrote in one of your paper that I’m studying, the eigenstructure of the correlation matrix (R) determines the spatial correlation properties of the channel (H).

    Lets suppose we have Nt transmit antennas (1 BS only) and K single-antenna users (it could be also one point-to-point MIMO, system with only 1 user with Nr antennas… yes, they are not actually the same situation, but I want to focus on a different point).
    In this situation we have a K-by-Nt channel matrix H.

    Now, my very simple question is: what’s the spatial correlation matrix R?
    Because from “MIMO Wireless Networks. Channels, Techniques and Standards for Multi-Antenna, Multi-User and Multi-Cell Systems (C. Oestges), section 2.3”, R is given as
    R = E{vec(H)’ * vec(H)},
    which is a K*Nt-by-K*Nt matrix. (E{*} is the expectation operator).
    Then it talks about also the transmit correlation matrix (Rt) and the receive correlation matrix (Rr). In any simulation I’m running, I got just one eigenvalue of R (all the others are zero), and n=rank(H) eigenvalues of Rt/Rr. Moreover, the eigenvalue of R is always the sum of all eigenvalues of Rt/Rr.
    I have not clear the role of R with respect to Rt or Rr.

    Thank you very much, and sorry for the long comment.


    1. Section 2.3 in the book by Clerckx and Oestges considers point-to-point MIMO channels. In this case, you can compute covariance matrix as R = E{ vec(H)’ * vec(H)}. If the channel is modeled as H = Rr^(1/2) * Hiid * Rt^(1/2), where Hiid has i.i.d. elements, then R = Rt^T (kronecker product) Rr.

      When there are multiple users, you should compute a different matrix R for every user. One should not mix them together into a single covariance matrix – that will only cause confusion and complicate things.

  5. Dear Professor,
    I have read about the Local Scattering Spatial Correlation Model in your book and I would like to ask what is your opinion about Kronecker, Weichselberger, and exponential channel model? I think that they are not the most suitable models to represent real conditions but are they good enough to estimate the BER performance of a linear detector such as Zero-Forcing?

    1. The exponential correlation model is an actual method to generate spatial correlation matrices. It is analytically convenient since one can vary one parameter to determine the level of correlation. But it is not especially physically accurate.

      Kronecker and Weichselberger models are not used to generate spatial correlation matrices, but to impose a special structure on single-user point-to-point MIMO channels. They are describing how the channels observed at the transmit arrays and receiver array are correlated. The Weichselberger model is more general than the Kronecker model. In my book and many Massive MIMO papers, single-antenna users are assumed and then Kronecker and Weichselberger model doesn’t really exist – the structure that they impose only make a difference when there are multiple antennas at the users as well.

      1. Thanks for your answer.

        I am going to use the Local Scattering Spatial Correlation Model from your book for my simulations because I deal with multi-user MIMO systems. I thought that the Kronecker model could also impose a special structure on multi-user point-to-point MIMO channels. There are many papers which are using the Kronecker model and they refer to their system as a massive MIMO system. So I was confused because I have on my mind that a massive MIMO system is a multi-user MIMO.

        One more thing I would like to ask. Could a single-user MIMO system with 128 receiving antennas (BS) and 16 transmitting antennas have a practical use or for this number of antennas only multi-user MIMO systems are examined?

        1. I’m sure you can find confusing statements in published papers. Not everything that is said papers is correct, not even in my papers… 🙂

          Yes, particularly, in mmWave bands where 16 antennas can be easily squeezed into a user device (in fact, it might be needed to get a decent SNR). A rule-of-thumb is that each antenna is lambda/2 x lambda/2, thus the total area of 16 antennas is 4*lambda^2. If lambda = 1 cm (30 GHz), then the array will have an area of 4 cm^2, for example, configured as a square of size of 2 cm x 2 cm. That is not much!

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