958 research outputs found
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems
Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity.
The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel
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