Impact of Feedback Delay on Rate Adaptation for Multiple Antenna Systems

Figueiredo, Daniel Vaz Pato; Carvalho, Elisabeth de; Deneire, Luc; Prasad, Ramjee

doi:10.1109/pimrc.2006.253927

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…1) SINR Prediction: Predict the channel Δ data blocks ahead at all subchannels using (10). Use the predicted channel to compute the predicted mutual-information-averaged log-SINRγ l t,p using (2) and (7). Store this value for use Δ data blocks later.…”

Section: Summary: Rate Adaptation Algorithmmentioning

confidence: 99%

“…This problem has been solved for flat fading channels and single-input single-output transmission [5], as well as for MIMO transmission with maximum ratio combining [6], where it is possible to state the CQI statistics in closed form. For MIMO spatial multiplexing transmission and linear receivers, however, the CQI statistics are only known for special cases, e.g., accurately for zero forcing equalization [7] and approximately for minimum mean square error (MMSE) equalization [8] (in both cases in absence of feedback). A first approximate solution for flat-fading MIMO channels with delayed feedback and linear receivers as well as successive interference cancelation (SIC) receivers was presented by the authors in [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Approximate Rate Adaptation for MIMO-OFDM Systems with Delayed Feedback

Ohlmer¹,

Fettweis

2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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We present an approximate rate adaptation algorithm for MIMO spatial multiplexing transmission over multiple time-variant subchannels, based on delayed inaccurate feedback. The algorithm exploits an approximation to the mutual-information-averaged post-equalization signal-tointerference-and-noise-ratio statistics. It is suited for linear and successive interference cancelation MIMO receivers. We demonstrate that the algorithm successfully exploits frequency diversity in order to mitigate the rate loss at increasing velocities. In addition, the interference cancelation receiver gain, as compared to linear receivers, can be maintained even with error propagation.

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Section: Summary: Rate Adaptation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approximate Rate Adaptation for MIMO-OFDM Systems with Delayed Feedback

Ohlmer¹,

Fettweis

2013

2013 IEEE 78th Vehicular Technology Conference (VTC Fall)

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confidence: 99%

“…Similarly, the effect of feedback delay on MIMO systems employing rate adaptation has been presented in [14]. Both [13] and [14] study the uncoded Symbol Error rate (SER) performance for QAM constellations, and neglect channel estimation error at the receiver. In [15], the SER and outage probability of transmit antenna selection in the presence of feedback delay are derived for the case of single receive antenna and perfect CSI at the receiver.…”

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confidence: 99%

Using Delayed Feedback for Antenna Selection in MIMO Systems

Ramya

Bhashyam

2009

IEEE Trans. Wireless Commun.

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Abstract-Antenna selection in Multiple-Input-Multipleoutput (MIMO) systems preserves diversity gain while significantly reducing hardware complexity. However, imperfect Channel State Information (CSI) affects performance. In this paper, we first analyze the performance of a MIMO system employing antenna selection at the transmitter and Maximal Ratio Combining (MRC) at the receiver in the presence of feedback delay and channel estimation errors. Then, we determine whether channel prediction can compensate for the effect of feedback delay. Outage probability is analyzed as a function of , the correlation coefficient between the CSI used at the receiver for decoding (CSIR) and the CSI used at the transmitter for selection (CSIT). Analytical results show that the effect of feedback delay is more significant than the effect of estimation error. In order to overcome the effect of delay, should increase with SNR. For a given SNR, the length of the Linear Minimum Mean Square Error (LMMSE) prediction filter required is calculated and shown to increase with SNR. Finally, we determine the asymptotic diversity order as a function of the feedback quality. Results show that if 1 − ∝ −1 , the diversity order with imperfect CSI is same as that with perfect CSI.

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