Practical Propagation Channel Robust BLER Estimation in the OFDM/TDMA and OFDMA Broadband Wireless Access Networks

Moiseev, S. N.; Filin, Stanislav; Kondakov, M.S.; Garmonov, A.V.; Savinkov, A.Y.; Park, Yun; Yim, Do Hyon; Lee, Jae; Cheon, Seok Ho; Han, Ki-Ho

doi:10.1109/vtcf.2006.242

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…It was established that the value of the determination coefficient R 2 was 0.07, which means that, as expected, the linear model in this case does not accurately describe the measured radio parameters with the obtained error rate. It is also worth explaining that, in accordance to the form of the function (3), its arguments are the instantaneous values, not vectors of radio channel quality parameters as declared in (2). In general, it should be understood as the implementation of an additional operation in accordance with the selected radio channel prediction method, which were presented in the Section 6.1.…”

Section: Measurement Campaign Nomentioning

confidence: 99%

“…It should be mentioned that all the input data in the network were normalized to the range of values [0, 1] so that, despite the different possible ranges of the assumed values for all the radio parameters, the learning process could be implemented in the most effective manner. Additionally, due to the obvious different nature of the determining the non-linear function (2) method with respect to the linear function, the length of the sample history window was re-analyzed to determine the optimal value for the proposed DL model.…”

Section: Proposed Deep Learning Approachmentioning

confidence: 99%

“…The network heterogeneity implies the extensive changes in the UE software. If the communication can be realized by two independent RIs, the decision must be made in terms of how to organize the data transmission, minimizing e.g., BLER (Block Error Rate), especially if the data are assigned to different services [2,3]. Henceforth, the effectiveness of the radio link must be introduced.…”

Section: Introductionmentioning

confidence: 99%

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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Cwalina

Rajchowski

Olejniczak

et al. 2021

Sensors

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Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

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Section: Measurement Campaign Nomentioning

confidence: 99%

Section: Proposed Deep Learning Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Cwalina

Rajchowski

Olejniczak

et al. 2021

Sensors

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“…The 4G (LTE) uses exclusively the BLER as an indicator of the connection quality [37]. A block is considered to be in error if at least one of the bits in the block is detected incorrectly and it should be generally \%2 for in-sync and \%10 for out-of-sync.…”

Section: Block Error Ratiomentioning

confidence: 99%

On Detection in MIMO-OFDM Systems Over Highly Mobile Wireless Channels

Doğan

2015

Wireless Pers Commun

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High mobility support of multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems plays a major role in wireless communication systems. Conventional detection approaches for MIMO-OFDM systems assume the channel is constant for at least one OFDM symbol duration. However, the time variation of the channel on account of mobility over an OFDM symbol period causes a loss of subchannel orthogonality which results in inter-carrier interference. In this paper, we propose the space alternating generalized expectation maximization (SAGE) based signal detection for MIMO-OFDM systems and compared by the zero forcing (ZF), minimum mean square error (MMSE), vertical Bell laboratories layered space-time (VBLAST) detection methods by considering complexity and performance tradeoffs. Simulation results show that the SAGE based detection has comparable performance with the VBLAST while needs lower computational complexity. It is also shown that both SAGE and VBLAST have a clear BER performance advantage over the MMSE and ZF detections. We also investigate the sensitivity of detection MIMO-OFDM signals to channel estimation errors by the application of a simple frequency domain based pilot based channel estimation. It is shown that the superiority of the SAGE scheme is also valid in the case of channel imperfections.

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Prediction of the SINR RMS in the IEEE 802.16 OFDMA System

Moiseev¹,

Kondakov²

2009

IEEE Trans. Signal Process.

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Practical Propagation Channel Robust BLER Estimation in the OFDM/TDMA and OFDMA Broadband Wireless Access Networks

Cited by 7 publications

References 7 publications

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

On Detection in MIMO-OFDM Systems Over Highly Mobile Wireless Channels

Prediction of the SINR RMS in the IEEE 802.16 OFDMA System

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