The low-rankness of the channel covariance matrix

In this post, I will briefly talk about a property of massive MIMO channels – the low-rankness of the channel covariance matrix, which is defined as {{\mathbf{R}}} \triangleq \mathbb{E}\{ {{{\mathbf{h}}}{\mathbf{h}}^H} \} where the expectation is taken over the random realizations of the channel vector {\mathbf{h}}.

Generally speaking, due to limited antenna spacing and angular spread, the channel covariance matrix in massive MIMO has a reduced rank. In other words, the wireless signal of a certain user lives in a reduced subspace. This feature can be exploited in application scenarios like pilot contamination reduction, spatial multiplexing, statistical interference mitigation, compressed sensing (as it reveals a certain sparsity of the signal), etc.

Intuition tells us that the rank is a function of the richness of the scattering environment, the antenna spacing, and the number of antennas. But how can we express it in closed-form?

Let’s consider a very common antenna placement: uniform linear array (ULA).

Fig. 1. Multipath model
Fig. 1. Multipath model

Using a general multipath channel model, the uplink channel vector \mathbf{h} can be written as a linear combination of \mathbf{P} steering vectors.

{\mathbf{h}} = \frac{\beta}{\sqrt{P}} \sum\limits_{p = 1}^P {{\mathbf{a}}({\theta _{p}}){e^{i\varphi_{p}}}},

where the p-th path has the angle of arrival (AoA) {\theta _{p}} and the random phase \varphi_{p}.

Now consider the scenario when the multipath AoAs are randomly distributed within a bounded support \Phi = [\theta_{\text{min}}, \theta_{\text{max}}], i.e., the AoA \theta always satisfies \theta_{\text{min}} \leq \theta \leq \theta_{\text{max}}
as shown in Fig. 2.

Fig. 2. Bounded AoA support
Fig. 2. Bounded AoA support

When the number of antennas M grows large, no matter how many paths we have in the channel, the rank of the channel covariance matrix yields

\frac{{rank} ({{\mathbf{R}}})} {M} \leq {\left( {\cos (\theta^{{\text{min}}}) - \cos (\theta^{{\text{max}}})} \right)} \frac{D}{\lambda } ,

where \lambda is the signal wavelength and D is the antenna spacing.

This low-rankness property indicates that in massive MIMO there exists a large null space {null} ({{\mathbf{R}}}), which can be exploited for the purpose of, for example, interference rejection.

For detailed mathematical proof, please refer to

[1] H. Yin, D. Gesbert, M. Filippou and Y. Liu, “A coordinated approach to channel estimation in large-scale multiple-antenna systems,” IEEE J. Sel. Areas Commun., Vol. 31, No. 2, pp. 264-273, Feb. 2013.

Apart from ULA and single AoA support, some generalizations of this result can be made to the case of random linear array, multiple clusters of AoA supports, and to two-dimensional distributed array. These generalized results can be found in

[2] H. Yin, D. Gesbert, and L. Cottatellucci, “Dealing with interference in distributed large-scale MIMO systems : A statistical approach,” IEEE J. Sel. Topics Signal Process., Vol. 8, No. 5, pp. 942-953, Oct. 2014.


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