Adaptive and Linear Prediction Channel Tracking Algorithms for Mobile OFDM-MIMO Applications

Gifford, S.; Bergstrom, C.S.; Chuprun, S.

doi:10.1109/milcom.2005.1605857

Cited by 15 publications

(5 citation statements)

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“…For the time series training data of length S , to choose R as the trajectory length, a total of

S-R

groups of time series samples are generated. The MRDNN‐based channel prediction method is compared with traditional AR method [10] and NL kalman method [13]. The performance of traditional AR methods compared with MRDNN is discussed.…”

Section: Complexity Analysis and Simulation Resultsmentioning

confidence: 99%

“…The Kalman filter is a type of efficient autoregressive filter that has the advantage of dealing well with sensor noise. However, the precise Doppler rate, which is particularly challenging to measure in highly dynamic situations, must be known beforehand in order to use the Kalman filter [13]. In addition, the Kalman filter based estimators suffer from huge computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Mixed RNN‐DNN based channel prediction for massive MIMO‐OFDM systems

Ge,

Shi,

Niu

et al. 2023

IET Communications

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Channel state information (CSI), which is crucial for resource allocation and system performance in time division duplex (TDD) massive multiple‐input multiple‐output (MIMO) systems, is difficult to predict because of the channel's time‐varying nature. To overcome this limitation, a scheme for channel prediction combined with deep learning (DL) is proposed. The system uses a deep neural network (DNN) to interpolate channel estimates from a few received pilot signals and a recurrent neural network (RNN) to train through the current time and the recent historical channel estimates to predict the CSI while the channel is constantly varying. In the end, a mixed neural network of RNN and DNN, is called MRDNN. In addition, the proposed DL‐based method does not rely on the relevant feature information about the channel, such as internal characteristics and parameters of the channel itself or channel statistical information, which improves its effectiveness in practical applications. The results of the simulation show that the MRDNN‐based method is better than the existing methods, like traditional AR method and NL Kalman method, and also can be effective in improving the quality of channel prediction and the performance of the system under the dynamic change scenario of low mobility.

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“…For the time series training data of length S , to choose R as the trajectory length, a total of

S-R

Section: Complexity Analysis and Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mixed RNN‐DNN based channel prediction for massive MIMO‐OFDM systems

Ge,

Shi,

Niu

et al. 2023

IET Communications

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“…Therefore, a kernel with larger size may not improve the decision accuracy because of increased "interference". Then, we verify the accuracy of the CSI prediction for the proposed ML-based architecture, and choose the AR estimator and nonlinear (NL) Kalman predictor [37,39] as the benchmarks to illustrate the performance improvement. It is worth noting that the parameters in NL Kalman predictor require real-time training, and the results for NL Kalman in Fig.…”

Section: Propositionmentioning

confidence: 99%

Machine Learning-Based Channel Prediction in Massive MIMO With Channel Aging

Yuan

Ngo

Matthaiou

2020

IEEE Trans. Wireless Commun.

124

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To support the ever increasing number of devices in massive multiple-input multiple-output (mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation schemes. To circumvent this fundamental constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The presence of the temporal channel correlation is due to the stationarity of the propagation environment across time. The proposed ML-based predictors involve a pattern extraction implemented via a convolutional neural network, and a CSI predictor realized by an autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network. Closed-form expressions for the user uplink and downlink achievable spectral efficiency and average per-user throughput are provided for the ML-based time division duplex schemes. Our numerical results demonstrate that the proposed ML-based predictors can remarkably improve the prediction quality for both low and high mobility scenarios, and offer great performance gains on the per-user achievable throughput.

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“…While there has been extensive literature on the prediction and tracking of MIMO channels using Bayesian approaches, particularly the KF in conjunction with Autoregressive Model (AR) models (see. e.g [67,102,116,209]), there exist few results on the application of these methods to the joint tracking of MIMO multipath parameters.…”

Section: Discussionmentioning

confidence: 99%

Channel Prediction for Mobile MIMO Wireless Communication Systems

Adeogun¹

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<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>

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Adaptive and Linear Prediction Channel Tracking Algorithms for Mobile OFDM-MIMO Applications

Cited by 15 publications

References 10 publications

Mixed RNN‐DNN based channel prediction for massive MIMO‐OFDM systems

Mixed RNN‐DNN based channel prediction for massive MIMO‐OFDM systems

Machine Learning-Based Channel Prediction in Massive MIMO With Channel Aging

Channel Prediction for Mobile MIMO Wireless Communication Systems

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