Optimal Training Sequences for MIMO Channel Estimation with Spatial Correlation

Pang, Jiyong; Li, Jiandong; Zhao, Liang; Lu, Zhuo

doi:10.1109/vetecf.2007.146

Cited by 25 publications

(30 citation statements)

References 10 publications

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“…This approach will result in a different solution than the single target cell approach in [18]- [23], as an interfering user in one cell is the desired user in another. Since the matrices A i do not depend on the optimization variables W i and S i , minimizing ǫ is equivalent to maximization of i tr (B i C i B * i ), which yields the following optimization problem:…”

Section: Mmse Channel Estimationmentioning

confidence: 99%

“…Different from previous approaches [27], [28] which considered T = I NRF and optimized (18) over W alone, here the optimization is carried out with respect to both W and T . To solve (18), an alternating minimization approach was suggested, referred to as MaGiQ. The benefit of MaGiQ, is that at each iteration, a closedform solution for both variables is available.…”

Section: B Magiq -Minimal Gap Iterative Quantizationmentioning

confidence: 99%

“…zero-mean unit variance complexnormal distribution, and the smart pilot assignment (SPA) method from [13], which is a near-optimal allocation algorithm when τ = K. As mentioned before, pilot design can also be performed from a single cell perspective as in [18]- [23]. However we do not compare our method with this approach, as when generalizing this framework to the multi-cell setting by iterative design, the solution rarely converges.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Previous pilot sequence design methods typically consider a single cell perspective [18]- [23]. These works treat a single desired channel with multiple interferers and optimize the pilot sequences in the specific cell to minimize its channel estimation error assuming all the pilots of surrounding cells are given.…”

Section: Introductionmentioning

confidence: 99%

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Pilot contamination mitigation with reduced RF chains

Ioushua

Eldar

2017

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Abstract-Massive multiple-input multiple-output (MIMO) communication is a promising technology for increasing spectral efficiency in wireless networks. Two of the main challenges massive MIMO systems face are degraded channel estimation accuracy due to pilot contamination and increase in computational load and hardware complexity due to the massive amount of antennas. In this paper, we focus on the problem of channel estimation in massive MIMO systems, while addressing these two challenges: We jointly design the pilot sequences to mitigate the effect of pilot contamination and propose an analog combiner which maps the high number of sensors to a low number of RF chains, thus reducing the computational and hardware cost. We consider a statistical model in which the channel covariance obeys a Kronecker structure. In particular, we treat two such cases, corresponding to fully-and partially-separable correlations. We prove that with these models, the analog combiner design can be done independently of the pilot sequences. Given the resulting combiner, we derive a closed-form expression for the optimal pilot sequences in the fully-separable case and suggest a greedy sum of ratio traces maximization (GSRTM) method for designing sub-optimal pilots in the partially-separable scenario. We demonstrate via simulations that our pilot design framework achieves lower mean squared error than the common pilot allocation framework previously considered for pilot contamination mitigation.

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Section: Mmse Channel Estimationmentioning

confidence: 99%

Section: B Magiq -Minimal Gap Iterative Quantizationmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pilot contamination mitigation with reduced RF chains

Ioushua

Eldar

2017

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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“…From [17], it can be proved that the optimal training sequences can be found by linear transformation of the FZC. If R hh = QΛ Λ ΛQ H is the eigenvalue decomposition of the hermitian matrix R hh and since Q is an orthogonal matrix, then:…”

Section: Optimal Training Patternsmentioning

confidence: 99%

Performance Evaluation of Beamspace MIMO Systems with Channel Estimation in Realistic Environments

Μαλιάτσος

Vasileiou

Kanatas

2014

JCSM

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Beamspace MIMO (BS-MIMO) systems have been recently proposed as a means to address the two key weaknesses of conventional MIMO systems: the antenna size and the need for multiple RF chains. BS-MIMO transmission is supported by the Electronically Steerable Passive Array Radiators (ESPAR) with a single active and multiple parasitic elements. The main objective is to develop efficient MIMO multiplexing schemes that use only one Radio Frequency (RF) chain and simultaneously maintain extremely small antenna size. Moreover, the recent research results have shown that BS-MIMO systems have increased multiplexing and beamforming capabilities and for small antenna sizes clearly outperform equivalent conventional systems in terms of system capacity. Nevertheless, research on BS-MIMO has been focusing on the study of the ESPAR antenna properties that facilitate beamspace transmission and the theoretical analysis of the ergodic capacity provided by the aerial degrees of freedom (aDoF) of the beamspace channel. Recently, the first steps have been made to design and evaluate practical BS-MIMO systems. This paper presents extended results of the first attempt to design practical and realistic BS-MIMO transmission and reception schemes and it specifically focuses on channel estimation techniques for BS-MIMO systems with adaptive pattern reconfiguration.Adaptation of the basic least-squares (LS) and minimum mean squared error (MMSE) estimators for the beamspace radio channels is performed and the algorithms are incorporated in an adaptive Singular Value DecompositionJournal of Cyber Security, Vol. 2 No. 3 & 4, 265-290. doi: 10.13052/jcsm2245-1439

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Capacity lower bound and energy efficiency of training‐based MIMO systems over correlated channels

Pang¹,

Jin³

2009

Int J Communication

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SUMMARYThe behavior of training-based multiple-input multiple-output wireless communications over correlated channels is studied in the low signal-to-noise ratio (SNR) regime from an energy efficiency perspective. To start with, a relaxed but analytically more tractable capacity lower bound is derived where the impacts of channel spatial correlation are considered simply through the ranks of correlation matrices. It shows that, different from the independent and identically distributed case, the optimal training length no longer equals the number of transmit antennas but is just the same as the rank of transmit correlation matrix. Next, the bit energy requirements are analyzed based on the derived capacity lower bound. It shows that the minimum bit energy required for reliable communication is achieved at a nonzero SNR value below, which the bit energy grows to infinity with a zero-approaching wideband slope. Flash training and transmission scheme is also studied and shown to improve the energy efficiency. In addition, the impacts of spatial correlation, channel coherence interval and antenna number on the energy efficiency are analyzed. It shows that stronger spatial correlation leads to less bit energy, i.e. higher-energy efficiency. The minimum bit energy is achieved at lower SNR in a more correlated channel. Spatial correlation also helps to reduce the signal peakiness in the flash scheme. And larger coherence interval always improves energy efficiency at all SNR. Finally, the validity of the proposed results is verified by numerical simulations.

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Optimal Training Sequences for MIMO Channel Estimation with Spatial Correlation

Cited by 25 publications

References 10 publications

Pilot contamination mitigation with reduced RF chains

Pilot contamination mitigation with reduced RF chains

Performance Evaluation of Beamspace MIMO Systems with Channel Estimation in Realistic Environments

Capacity lower bound and energy efficiency of training‐based MIMO systems over correlated channels

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