A performance bound for prediction of MIMO channels

Svantesson, T.; Swindlehurst, A. Lee

doi:10.1109/tsp.2005.861105

Cited by 43 publications

(56 citation statements)

References 14 publications

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“…Future work in this area may attempt to incorporate the spatial and delay structures of the MIMO channel into the AR model as suggested in [13]. This approach is challenging because it introduces non-linearities into the channel model.…”

Section: Discussionmentioning

confidence: 99%

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MIMO channel prediction results on outdoor collected data

Bidigare

Brown

Kraut

et al. 2013

2013 Asilomar Conference on Signals, Systems and Computers

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Abstract-In this paper we analyze the performance of an autoregressive MIMO channel predictor on outdoor data collected with both vehicular and pedestrian transmitter motion. The metric of performance considered is aggregate beamforming gain obtained using dominant eigenmode MIMO communications when the transmit beamforming vector is chosen based on the channel prediction. We show that while the prediction range obtained on this real data is somewhat less than earlier published results based on simulated data, we are still able to obtain 2X to 3X greater feedback latency tolerance than without using these predictive techniques.

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Section: Discussionmentioning

confidence: 99%

“…A CRLB analysis [13] suggests that MIMO channels can be predicted over considerably longer intervals than SISO channels by exploiting the common Doppler shifts of different propagation paths and the spatial structure imposed by the transmit and receive antenna array manifolds.…”

Section: Downlink Time Slotsmentioning

confidence: 99%

MIMO channel prediction results on outdoor collected data

Bidigare

Brown

Kraut

et al. 2013

2013 Asilomar Conference on Signals, Systems and Computers

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show abstract

“…That is, the prediction of the coefficients of the matrix H k (n, P) is decomposed into N T parallel predictors, one for each channel associated with its respective transmitting antenna [17]. Although suboptimal, this approach can greatly reduce the complexity required by joint channel prediction schemes [17], [21].…”

Section: A Channel Decompositionmentioning

confidence: 99%

Performance Evaluation of a Joint Precoding and Channel Prediction scheme for Multiuser MIMO OFDM Systems in Time Varying Wireless Channel

Leite¹,

Carvalho²,

Vieira³

2011

Journal of Communication and Information Systems

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Abstract-Multiple-input, multiple-output (MIMO) communications have attracted considerable attention in the last years. In particular, multiuser MIMO (MU MIMO) systems are able to offer higher link capacity of MIMO systems as long as channel state information (CSI) is available at the transmitter, so that the spatial properties of the channel can be exploited by precoding. However, the time varying nature of the channel may cause CSI to be outdated, thus degrading the overall performance of the system. Within this context, channel prediction can provide up-to-date channel CSI and reduce the performance loss. This paper presents a channel predictor based on the setmembership partial update affine projection (SM-PUAP) filtering as way to compensate the outdated CSI caused by the temporal variations of the communication channel in a multiuser MIMO OFDM (orthogonal frequency division multiplexing) system. A realistic standardized channel model is considered to evaluate the predictor and its influence on the broadcast channel of a multiuser MIMO scenario. The performance of the predictor is also compared to the well-known adaptive algorithm recursive least squares (RLS). Simulation results show that the SM-PUAP predictor has lower computational cost and superior performance when compared to the classical algorithm.Index Terms-Adaptive filters, broadcast channel, channel prediction, multiuser multiple-input multiple-output communications, orthogonal frequency division multiplexing, time-varying channels.

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“…Using a better channel state estimation scheme (e.g., a decision-driven scheme) may result in higher achievable data rates. Further, channel state prediction may also provide further improvements (see for example [29] and references therein).…”

Section: Uplink Channel State Estimationmentioning

confidence: 99%