Linear prediction of time-varying MIMO systems using Givens rotations

Godana, Bruhtesfa; Ekman, Torbjörn

doi:10.1109/spawc.2011.5990433

“…Although the overhead of acquiring full CSIT is prohibitive in FDD systems, the eNBs can still choose to attain CSIT of partial channels, when only the selected antenna elements transmit the training reference signal (RS). In order to efficiently utilize the partial CSIT to further improve the system performance, the proposed scheme transforms the conventional BF scheme that exploits only the spatial correlation, via Givens rotations which is used to predict time-varying MIMO system in [6]. Therefore, the eNBs can adjust the proportion of selected antennas to make a balance between the system performance and the CSITacquiring overhead in FDD FD-MIMO system.…”

Section: Introductionmentioning

confidence: 99%

A novel beamforming scheme in FD-MIMO systems with spatial correlation

Shao

¹

,

Zhang

²

,

Xing

³

et al. 2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

View full text Add to dashboard Cite

Compact structure of the large-scale antenna array and low power angular spread (PAS) in vertical dimension improve the fading correlation of wireless channel dramatically in full-dimension multiple input multiple output (FD-MIMO) systems. Whereby beamforming (BF) can fully exploit the quasistatic spatial correlation to reduce dependence on the instantaneous channel state information at the transmitter (CSIT) and simplify the complexity of signal process, but also incur a loss of system performance. In this paper, we analyze the potential of exploiting spatial correlation in FD-MIMO systems, and propose an interesting spatial-correlation-based partial-channelaware beamforming (SP-BF) scheme. Through the rationale of Givens rotation, SP-BF leverages the CSIT of partial channels, i.e., reduced-dimension channel vector, to further boost the system performance achieved by exploiting the spatial correlation. By applying this scheme, FD-MIMO in frequency division duplex (FDD) systems, where no channel reciprocity could be exploited, can make a tradeoff between the system performance and the overhead of acquiring CSIT. In addition, we validate the proposed scheme by virtue of the Monte Carlo simulations.

show abstract

“…A method for predicting channel direction information (CDI) on the Grassmanian manifold using the concept of tangents, parallel transport and geodesic interpolation is presented in [42,44]. Other codebook based schemes adopt Givens rotation to transform the precoding matrix and perform prediction on the Givens parameter [68]. These schemes are limited to the prediction of the prediction and/or CDI one step ahead and the channel model is often assumed to be independent and identically distributed (i.i.d).…”

Section: Mimo Channel Prediction: a Literature Surveymentioning

confidence: 99%

Channel Prediction for Mobile MIMO Wireless Communication Systems

Adeogun¹

2

0

View full text Add to dashboard Cite

<p>Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes.</p>

show abstract

“…A method for predicting channel direction information (CDI) on the Grassmanian manifold using the concept of tangents, parallel transport and geodesic interpolation is presented in [42,44]. Other codebook based schemes adopt Givens rotation to transform the precoding matrix and perform prediction on the Givens parameter [68]. These schemes are limited to the prediction of the prediction and/or CDI one step ahead and the channel model is often assumed to be independent and identically distributed (i.i.d).…”

Section: Mimo Channel Prediction: a Literature Surveymentioning

confidence: 99%

Channel Prediction for Mobile MIMO Wireless Communication Systems

Adeogun¹

0

View full text Add to dashboard Cite

<p>Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes.</p>

show abstract

Linear prediction of time-varying MIMO systems using Givens rotations

Cited by 3 publications

References 15 publications

A novel beamforming scheme in FD-MIMO systems with spatial correlation

A novel beamforming scheme in FD-MIMO systems with spatial correlation

Channel Prediction for Mobile MIMO Wireless Communication Systems

Channel Prediction for Mobile MIMO Wireless Communication Systems

Contact Info

Product

Resources

About