MIMO-OFDM Downlink Channel Prediction for IEEE802.16e Systems Using Kalman Filter

Min, Changkee; Chang, Namseok; Cha, Jongsub; Kang, Joonhyuk

doi:10.1109/wcnc.2007.179

Cited by 41 publications

(24 citation statements)

References 8 publications

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“…To track the channel variation, here we adopt the channel prediction method proposed in [8], in which the channel coefficients in future slots are predicted based on previous channel feedback, by applying the auto-regressive (AR) model and Kalman filter. The actual channel matrix H u can be written as…”

Section: System Modelmentioning

confidence: 99%

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Downlink Scheduling in Network MIMO Using Two-Stage Channel State Feedback

Tseng

Jin

Marzouki

et al. 2012

2012 IEEE Vehicular Technology Conference (VTC Fall)

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In this paper, we propose a user scheduling scheme which is based merely on the ergodic rate. We derive the ergodic rate of each user for our network MIMO system from the average Signal-to-Noise Ratio (SNR) and the covariance of prediction error. Further more, we design a joint precoding for the selected users. The precoding matrices are contrived according to the predicted Channel State Information at Transmitter (CSIT) in the future time slot to reduce the deviation caused by delay. Such a network MIMO system is under consideration, where the users are cooperatively served by multiple Base Stations (BSs). User scheduling and joint precoding are executed by a central unit connected to all BSs. Both the above functions require the CSIT which is acquired by an uplink feedback overhead. Simulation results demonstrate a conspicuous improvement in user spectral efficiency with reduced feedback.

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Section: System Modelmentioning

confidence: 99%

“…Similarly, the effective channel error matrix E ef f,u =Ê uTu distributes as E ef f,u ∼ CN (0 Nr , κ 2 u I Nr ). Interpreting the system in (9) as a MIMO channel under interference, the achievable rate in (8) can be obtained using the approximation in [9], where η 1 and η 2 are solutions to…”

Section: Asymptotic Rate For Mu Network Mimomentioning

confidence: 99%

Downlink Scheduling in Network MIMO Using Two-Stage Channel State Feedback

Tseng

Jin

Marzouki

et al. 2012

2012 IEEE Vehicular Technology Conference (VTC Fall)

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“…Although several studies have been reported on the channel prediction in single-carrier correlated MIMO systems, no results have been reported on the MIMO-OFDM systems which take the spatial correlation into account to the best of our knowledge. On the other hand, several researchers have extended the channel prediction to the MIMO-OFDM systems [10] [11]. However, they only consider the temporal correlations similar to the ones used in the SISO case.…”

Section: Introductionmentioning

confidence: 99%

MIMO-OFDM wireless channel prediction by exploiting spatial correlation

Feng

Zhang

2012

2012 International Conference on Wireless Communications and Signal Processing (WCSP)

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Channel prediction is an appealing approach to mitigate channel mismatch in frequency division duplex (FDD) systems. In the presence of correlation among the transmit or receive antennas, it will improve the performance of channel prediction if utilizing the correlation information. In this article, we first propose a MIMO-OFDM channel prediction model on time domain, which considers and exploits the spatial correlation. Then we derive two predictors based on the proposed model, which select data for auto-regressive (AR) modeling in different ways. The first predictor, called the best data predictor, chooses the desired data set via minimizing the mean square error (MSE) of prediction model. Yet the second predictor, called reducedcomplexity subset predictor, chooses the data in a heuristic way, which aims to reduce the computational complexity. Both of the two predictors can exploit the temporal and spatial correlation adaptively. Our simulations show that the new algorithms outperform conventional methods which ignore the spatial correlation.

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“…Hence, channel prediction becomes essential to alleviate this problem. A channel prediction scheme based on a Kalman filter for MIMO − OFDM systems operating in TDD mode is proposed in [13]. In this work we propose a channel prediction scheme for MIMO − OFDM systems operating in FDD mode.…”

Section: Introductionmentioning

confidence: 99%

Channel prediction for limited feedback precoded MIMO-OFDM systems

Khrwat

2012

2012 19th International Conference on Telecommunications (ICT)

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In this paper, the performance of limited feedback precoded spatial multiplexing multiple-input multiple-output orthogonal frequency division multiplexing (MIMO − OFDM) systems is investigated in time varying fading channels. Most studies into limited feedback precoded MIMO − OFDM systems are based on the notion of time invariant channels throughout transmission. Therefore, the precoding matrix for each subcarrier can be designed at the receiver in terms of the current channel state, fed back to the transmitter, and used for the subsequent block of data symbols. However, in time varying channels, the feedback information becomes outdated, which results in significant system performance degradation. To mitigate this system performance degradation caused by outdated information due to a delay in the feedback channel, this paper considers a more practical system, where the channel varies from one block of symbols to another. A method is proposed based on modelling the channel as an autoregressive (AR) model and using a Kalman filter linear predictor at the receiver to predict the channels states, required to design the precoding matrices for the next OFDM symbol carriers whose indexes are fed back to the transmitter. The performance of this method is investigated using computer simulation, and the results obtained for the proposed channel prediction demonstrate improved bit error rate performance for time-varying Rayleigh fading channels, even in systems with high mobility

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MIMO-OFDM Downlink Channel Prediction for IEEE802.16e Systems Using Kalman Filter

Cited by 41 publications

References 8 publications

Downlink Scheduling in Network MIMO Using Two-Stage Channel State Feedback

Downlink Scheduling in Network MIMO Using Two-Stage Channel State Feedback

MIMO-OFDM wireless channel prediction by exploiting spatial correlation

Channel prediction for limited feedback precoded MIMO-OFDM systems

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