This paper considers the reconstruction of a discrete-valued random vector from possibly underdetermined linear measurements using sum-of-absolute-value (SOAV) optimization. The proposed algorithm, referred to as disc...
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This paper considers the reconstruction of a discrete-valued random vector from possibly underdetermined linear measurements using sum-of-absolute-value (SOAV) optimization. The proposed algorithm, referred to as discreteness-aware approximate message passing (DAMP), is based on the idea of approximate message passing (AMP), which has been originally proposed for compressed sensing. The DAMP algorithm has low computational complexity and its performance in the large system limit can he predicted analytically via state evolution framework, where we provide a condition for the exact reconstruction with DAMP in the noise-free case. From the analysis, we also propose a method to determine the parameters of the SOAV optimization. Moreover, based on the state evolution, we provide Bayes optimal DAMP, which has the minimum mean-square-error at each iteration of the algorithm. Simulation results show that the DAMP algorithms can reconstruct the discrete-valued vector from underdetermined linear measurements and the empirical performance agrees with our theoretical results in large-scale systems. When the problem size is not large enough, the SOAV optimization with the proposed parameters can achieve better performance than the DAMP algorithms for high signal-to-noise ratio.
This paper proposes signal detection schemes for massive multiple-input multiple-output (MIMO) systems, where the number of receive antennas is less than that of transmitted streams. Assuming practical baseband digita...
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This paper proposes signal detection schemes for massive multiple-input multiple-output (MIMO) systems, where the number of receive antennas is less than that of transmitted streams. Assuming practical baseband digital modulation and taking advantage of the discreteness of transmitted symbols, we formulate the signal detection problem as a convex optimization problem, called sum-of-absolute-value (SOAV) optimization. Moreover, we extend the SOAV optimization into the weighted-SOAV (W-SOAV) optimization and propose an iterative approach to solve the W-SOAV optimization with updating the weights in the objective function. Furthermore, for coded MIMO systems, we also propose a joint detection and decoding scheme, where log likelihood ratios of transmitted symbols are iteratively updated between the MIMO detector and the channel decoder. In addition, a theoretical performance analysis is provided in terms of the upper bound of the size of the estimation error obtained with the W-SOAV optimization. Simulation results show that the bit error rate performance of the proposed scheme is better than that of conventional schemes, especially in large-scale overloaded MIMO systems.
In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the...
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In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.
In this paper, we propose a message passing-based algorithm to reconstruct a discrete-valued vector whose elements have a symmetric probability distribution. The proposed algorithm, referred to as discreteness-aware a...
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ISBN:
(纸本)9781509030095
In this paper, we propose a message passing-based algorithm to reconstruct a discrete-valued vector whose elements have a symmetric probability distribution. The proposed algorithm, referred to as discreteness-aware approximate message passing (DAMP), borrows the idea of the approximate message passing (AMP) algorithm for compressed sensing. We analytically evaluate the performance of DAMP via state evolution framework to derive a required number of linear measurements for the exact reconstruction with DAMP. The analysis also provides the optimal parameter minimizing the required number of measurements. Simulation results show that DAMP can reconstruct the discrete-valued vector and its performance agrees well with our theoretical results via the state evolution.
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