5 research outputs found
Multi-Band Covariance Interpolation with Applications in Massive MIMO
In this paper, we study the problem of multi-band (frequency-variant)
covariance interpolation with a particular emphasis towards massive MIMO
applications. In a massive MIMO system, the communication between each BS with
antennas and each single-antenna user occurs through a collection of
scatterers in the environment, where the channel vector of each user at BS
antennas consists in a weighted linear combination of the array responses of
the scatterers, where each scatterer has its own angle of arrival (AoA) and
complex channel gain. The array response at a given AoA depends on the
wavelength of the incoming planar wave and is naturally frequency dependent.
This results in a frequency-dependent distortion where the second order
statistics, i.e., the covariance matrix, of the channel vectors varies with
frequency. In this paper, we show that although this effect is generally
negligible for a small number of antennas , it results in a considerable
distortion of the covariance matrix and especially its dominant signal subspace
in the massive MIMO regime where , and can generally incur a
serious degradation of the performance especially in frequency division
duplexing (FDD) massive MIMO systems where the uplink (UL) and the downlink
(DL) communication occur over different frequency bands. We propose a novel
UL-DL covariance interpolation technique that is able to recover the covariance
matrix in the DL from an estimate of the covariance matrix in the UL under a
mild reciprocity condition on the angular power spread function (PSF) of the
users. We analyze the performance of our proposed scheme mathematically and
prove its robustness under a sufficiently large spatial oversampling of the
array. We also propose several simple off-the-shelf algorithms for UL-DL
covariance interpolation and evaluate their performance via numerical
simulations.Comment: A short version of this paper was submitted to IEEE International
Symposium on Information Theory (ISIT), 201
FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification
We propose a novel method for massive multiple-input multiple-output (massive MIMO) in frequency division duplexing (FDD) systems. Due to the large frequency separation between uplink (UL) and downlink (DL) in FDD systems, channel reciprocity does not hold. Hence, in order to provide DL channel state information to the base station (BS), closed-loop DL channel probing, and channel state information (CSI) feedback is needed. In massive MIMO, this typically incurs a large training overhead. For example, in a typical configuration with M β200 BS antennas and fading coherence block of T β 200 symbols, the resulting rate penalty factor due to the DL training overhead, given by max{0, 1 - M/T }, is close to 0. To reduce this overhead, we build upon the well-known fact that the angular scattering function of the user channels is invariant over frequency intervals whose size is small with respect to the carrier frequency (as in current FDD cellular standards). This allows us to estimate the users' DL channel covariance matrix from UL pilots without additional overhead. Based on this covariance information, we propose a novel sparsifying precoder in order to maximize the rank of the effective sparsified channel matrix subject to the condition that each effective user channel has sparsity not larger than some desired DL pilot dimension T dl , resulting in the DL training overhead factor max{0, 1 - T dl /T } and CSI feedback cost of Tdl pilot measurements. The optimization of the sparsifying precoder is formulated as a mixed integer linear program, that can be efficiently solved. Extensive simulation results demonstrate the superiority of the proposed approach with respect to the concurrent state-of-the-art schemes based on compressed sensing or UL/DL dictionary learning