843 research outputs found
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Hybrid massive MIMO structures with reduced hardware complexity and power
consumption have been widely studied as a potential candidate for millimeter
wave (mmWave) communications. Channel estimators that require knowledge of the
array response, such as those using compressive sensing (CS) methods, may
suffer from performance degradation when array-inherent impairments bring
unknown phase errors and gain errors to the antenna elements. In this paper, we
design matrix completion (MC)-based channel estimation schemes which are robust
against the array-inherent impairments. We first design an open-loop training
scheme that can sample entries from the effective channel matrix randomly and
is compatible with the phase shifter-based hybrid system. Leveraging the
low-rank property of the effective channel matrix, we then design a channel
estimator based on the generalized conditional gradient (GCG) framework and the
alternating minimization (AltMin) approach. The resulting estimator is immune
to array-inherent impairments and can be implemented to systems with any array
shapes for its independence of the array response. In addition, we extend our
design to sample a transformed channel matrix following the concept of
inductive matrix completion (IMC), which can be solved efficiently using our
proposed estimator and achieve similar performance with a lower requirement of
the dynamic range of the transmission power per antenna. Numerical results
demonstrate the advantages of our proposed MC-based channel estimators in terms
of estimation performance, computational complexity and robustness against
array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS
channel estimator.Comment: This work has been submitted to the IEEE for possible publication.
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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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