THESIS
2020
xxi, 151 pages : illustrations ; 30 cm
Abstract
Compressive sensing (CS) has attracted significant attention as a technique that under-samples
high dimensional signals and accurately recovers them exploiting the sparsity of
these signals. There are several ingredients of the CS algorithm. The first is the structure of
the sparse signal. By exploiting additional signal structures in addition to the simple sparsity,
additional performance gains can be obtained. How to choose a flexible yet tractable sparse
prior to capture various sophisticated structured sparsity in specific application would be one
of the challenges for the CS algorithm design. Another important ingredient that would affect
the CS recovery performance is the measurement matrix. Different applications may result in
measurement matrices with different features....[
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Compressive sensing (CS) has attracted significant attention as a technique that under-samples
high dimensional signals and accurately recovers them exploiting the sparsity of
these signals. There are several ingredients of the CS algorithm. The first is the structure of
the sparse signal. By exploiting additional signal structures in addition to the simple sparsity,
additional performance gains can be obtained. How to choose a flexible yet tractable sparse
prior to capture various sophisticated structured sparsity in specific application would be one
of the challenges for the CS algorithm design. Another important ingredient that would affect
the CS recovery performance is the measurement matrix. Different applications may result in
measurement matrices with different features. How to handle a general measurement matrix
would be another challenge for the CS algorithm design. In wireless communication system,
due to the limited number of scatterers in the environment, the massive multi-input multi-output
(MIMO) channel can be quite sparse under an appropriate spatial basis. Besides the
channel sparsity, the massive MIMO channel further exhibits additional structures. In this
thesis, we focus on the CS algorithm designs with applications to massive MIMO systems to
exploit the possible structured sparsity and handle specific measurement requirement under
different application contexts.
First, we consider channel support side information (CSSI) is available at base station,
which can be exploited to enhance the channel estimation performance and reduce the pilot
overhead. We propose a weighted LASSO algorithm to fully exploit the CSSI and propose an
optimal weight policy to optimize the recovery performance. We also derive the closed-form
accurate expression for the minimum asymptotic normalized squared error and characterize
the minimum number of measurements required to achieve stable recovery.
Then, we consider a channel tracking problem in downlink frequency-division duplexing
(FDD) massive MIMO system. We propose a two-dimensional Markov model to capture
the two-dimensional (2D) dynamic sparsity of massive MIMO channels. We derive an effective
message passing algorithm to recursively track the dynamic massive MIMO channels
exploiting the 2D dynamic sparsity.
Besides the above works, we further propose a more general CS algorithm to solve the
problem of recovering a structured sparse signal from a linear measurement model with uncertain
measurement matrix. The proposed general framework can be utilized to provide highly accurate user location tracking in massive MIMO systems. Specifically, a three-layer
hierarchical structured sparse prior model is proposed to capture complicated structured
sparsities. By combining the message passing and variational Bayesian inference (VBI) approaches
via the turbo framework, the proposed Turbo-VBI algorithm is able to fully exploit
the structured sparsity for robust recovery of structured sparse signals under an uncertain
measurement matrix.
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