A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network

Xiong, Lei; Zhang, Zhengyu; Yao, Dongpin

doi:10.1007/s11276-021-02849-y

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Similar work was carried out in [20], which proposed an NN-based algorithm for an LTE-A network, where the algorithm is trained on experimental data collected from a deployed base station operator, with transfer learning used to improve the performance of the network. A promising architecture of neural network for the channel adaptation task is the Convolutional Neural Network (CNN), originally designed for image processing tasks, but achieving high accuracy in the channel prediction task in [21]. Another CNN, inspired by the image superresolution technique was proposed by [22].…”

Section: Related Workmentioning

confidence: 99%

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

Glinskiy,

Kureev,

Khorov

2024

IEEE Access

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A key feature of 5G systems is the Ultra-Reliable Low-Latency Communication (URLLC), which can be used for remote surgery, smart grids, industrial control, etc. URLLC requires millisecondlevel delays and very high reliability, i.e., less than 10 −5 packet loss probability. The ability to satisfy these very strict quality of service requirements depends on selecting the Modulation and Coding Schemes (MCS) for data transmissions. On the one hand, the selected MCS shall be robust enough to avoid multiple retransmissions within a small delay budget. On the other hand, the MCS shall be high-rate to reduce channel resource consumption and, thus, shall increase the system capacity for URLLC. The MCS selection problem is extremely challenging to capture the quickly varying wireless channel effects, e.g., in highly mobile scenarios, because the decision shall be made long before the actual transmission occurs. The paper proposes a novel MCS selection algorithm called ALPACA (Asymmetric Loss Prediction Algorithm for Channel Adaptation), which relies on a widely used class of convolutional-recurrent neural networks. However, in contrast to existing approaches, ALPACA explicitly considers the asymmetric error cost for channel prediction by utilizing quantile regression loss. Both real-life channel measurements and 3GPP channel models are used to evaluate the performance of ALPACA. Numerical results demonstrate the increase in the reliability and reduction in resource consumption compared with the existing MCS selection algorithms, which results in 40% growth of the network capacity.

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Section: Related Workmentioning

confidence: 99%

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

Glinskiy,

Kureev,

Khorov

2024

IEEE Access

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“…However, for active perception, ISAC channels involve radio propagation from transmitter to receiver as well as echo propagation from transmitter to scatterers and coming back to the transmitter. Therefore, traditional channel models are inappropriate for ISAC scenarios [12], [13], and it is necessary to develop accurate ISAC channel models.…”

Section: Introductionmentioning

confidence: 99%

A General Channel Model for Integrated Sensing and Communication Scenarios

Zhang

et al. 2023

IEEE Commun. Mag.

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The emerging 6G network envisions integrated sensing and communication (ISAC) as a promising solution to meet growing demand for native perception ability. To optimize and evaluate ISAC systems and techniques, it is crucial to have an accurate and realistic wireless channel model. However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment. Moreover, sensing channels have not been well modeled in the existing standard-level channel models. Therefore, in order to better model ISAC channel, a cluster-based statistical channel model is proposed in this paper, which is based on measurements conducted at 28 GHz. In the proposed model, a new framework based on 3GPP standard is proposed, which includes communication clusters and sensing clusters. Clustering and tracking algorithms are used to extract and analyze ISAC channel characteristics. Furthermore, some special sensing cluster structures such as shared sensing cluster, newborn sensing cluster, etc., are defined to model correlation and difference between communication and sensing channels. Finally, accuracy of the proposed model is validated based on measurements and simulations.

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“…The findings reveal that the proposed CNN-based CSE approaches perfect (theoretical) channel estimation levels in terms of bit error rate (BER) values and beats LS practical estimation in terms of mean squared error (MSE). Xiong et al [22] presented a novel realtime CNN-based CSE that uses the latest reference signal (RS) for online training and extracts the temporal features of the channel, followed by prediction employing the optimal model. For high-speed moving scenarios, the proposed CSE is used in OFDM systems, such as long-term evolution (LTE) and 5G systems, to track the fast time-varying and non-stationary channels using a real-time RS-based training algorithm and obtain an accurate CSI without changing the radio frame.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

Ali¹,

Alraddady²,

Al-Thunaibat³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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For a 5G wireless communication system, a convolutional deep neural network (CNN) is employed to synthesize a robust channel state estimator (CSE). The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information. Also, it utilizes pilots to offer more helpful information about the communication channel. The proposed CNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory (BiLSTM/LSTM) NNs-based CSEs. The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators. Using three different loss function-based classification layers and the Adam optimization algorithm, a comparative study was conducted to assess the performance of the presented DNNs-based CSEs. The BiLSTM-CSE outperforms LSTM, CNN, conventional least squares (LS), and minimum mean square error (MMSE) CSEs. In addition, the computational and learning time complexities for DNN-CSEs are provided. These estimators are promising for 5G and future communication systems because they can analyze large amounts of data, discover statistical dependencies, learn correlations between features, and generalize the gotten knowledge. KEYWORDSDLNNs; channel state estimator; 5G and beyond communication systems; robust loss functions estimation challenging to implement [3]. Deep learning neural networks-based wireless communication applications recently received a lot of attention, such as coding and decoding, automatic signals classification, MIMO detection, and channel estimation [2,5-11]. As a result, several deep learning algorithms, such as recurrent neural networks (RNNs) (e.g., LSTM and BiLSTM NNs), convolutional neural networks (CNNs), and hybrid CNN and RNN structures, have utilized to estimate the channel state in 5G wireless communication networks.In [12], the channel response feature vectors and channel estimation were extracted using CNN and BiLSTM deep learning-based estimators, respectively. The main target was to enhance the estimate performance at the downlink, in communication environments concerning high-speed mobility. In [13], an online-trained CSE that integrates CNN and LSTM have developed. In addition, the authors have developed a way for combining offline and online learning for 5G systems. In [5], for OFDM systems which deal with frequency selective channels, a feedforward DLNN-based joint CSE was presented. When uncertain imperfections are taken into account, the suggested approach outperforms the classic MMSE estimate technique. In [14], Feedforward DNNs were used to develop an online CSE for doubly selective channels. The suggested CSE was found to be better than the classic LMMSE one in all circumstances studied. In [8], 1D-CNN-based CSE have introduced. Using different modulation methods, the authors compared the proposed estimator performance against FFNN, MMSE, and LS CSEs in terms of MSE and BER. 1D-CNN outs...

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A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network

Cited by 7 publications

References 27 publications

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

A General Channel Model for Integrated Sensing and Communication Scenarios

Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

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