Tensor-Based Channel Estimation for Massive MIMO-OFDM Systems
Daniel C. Araujo,Andre L. F. de Almeida,Joao Paulo C. L. da Costa,Rafael Timóteo de Sousa Júnior +3 more
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A tensor-based minimum mean square error (MMSE) channel estimator is proposed that exploits the multidimensional nature of the frequency-selective massive MIMO channel in thefrequency-domain, being a low-complexity alternative to the well-known vector-MMSE channel estimation.Abstract:
Channel estimation is a crucial problem for massive multiple input multiple output (MIMO) systems to achieve the expected benefits in terms of spectrum and energy efficiencies. However, a considerable number of pilots are usually distributed over a large number of time-frequency resources using orthogonal frequency division multiplexing (OFDM) to effectively estimate a large number of channel coefficients in space and frequency domains, sacrificing spectral efficiency. In this paper, by assuming MIMO-OFDM transmission, we start by proposing a tensor-based minimum mean square error (MMSE) channel estimator that exploits the multidimensional nature of the frequency-selective massive MIMO channel in the frequency-domain, being a low-complexity alternative to the well-known vector-MMSE channel estimation. Then, by incorporating a 3D sparse representation into the tensor-based channel model, a tensor compressive sensing (tensor-CS) model is formulated by assuming that the channel is compressively sampled in space (radio-frequency chains), time (symbol periods), and frequency (pilot subcarriers). This tensor-CS model is used as the basis for the formulation of a tensor-orthogonal matching-pursuit (T-OMP) estimator that solves a greedy problem per dimension of the measured tensor data. The proposed channel estimator has two variants which may either resort to a joint search per tensor dimension or to a sequential search that progressively reduces the search space across the tensor dimensions. The complexities of the different tensor-based algorithms are studied and compared to those of the traditional vector-MMSE and vector-CS estimators. Our results also corroborate the performance-complexity tradeoffs between T-MMSE and T-OMP estimators, both being competing alternatives to their vector-based MMSE and OMP counterparts.read more
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Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach
TL;DR: This paper addresses the receiver design for an IRS-assisted multiple-input multiple-output (MIMO) communication system via a Tensor modeling approach aiming at the channel estimation problem using supervised (pilot-assisted) methods and presents two channel estimation methods that rely on a parallel factor (PARAFAC) tensor modeling of the received signals.
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Tensor-Based Channel Estimation for Millimeter Wave MIMO-OFDM With Dual-Wideband Effects
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Tensor Decompositions in Wireless Communications and MIMO Radar
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A comprehensive survey 5G wireless communication systems: open issues, research challenges, channel estimation, multi carrier modulation and 5G applications
TL;DR: 5G wireless communication systems that requires efficient channel estimation technique with an efficient candidate wave form are reviewed, which can possibly give high spectral efficiency, improving link reliability and suit huge number of clients.
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Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!
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