Deep Learning-Based Channel Prediction in Realistic Vehicular Communications

Abstract

Access to reliable estimates of the wireless channel, such as the channel state information (CSI) and the received signal strength would open opportunities for timely adaptation of transmission parameters and consequently increased throughput and transmission efficiency in vehicular communications. To design the adaptive transmission schemes, it is important to understand the realistic channel properties, especially in vehicular environments where the mobility of communication devices causes rapid channel variation. However, getting CSI estimates is challenging due to the lack of support for obtaining CSI from the chipset. In this paper, we present our efforts towards enabling reliable, up-to-date channel estimates in vehicular communications. We begin by designing and conducting a measurement campaign where we collect IQ (inphase and quadrature) samples of the IEEE 802.11p transmission and implement CSI extraction algorithms to obtain and analyze wireless channel estimates from various real-world environments. We then propose a deep learning-based channel prediction for predicting future CSI and received signal levels. The trace-based evaluation demonstrates that our prediction approach improves the future power level estimate by 15% to 25% in terms of the root-mean-square-error compared to the latest known channel properties, thus, providing a sound basis for future efforts in anticipatory vehicular communication transmission adaptation. INDEX TERMS Channel state information, channel prediction, vehicular communications, neural networks, LSTM.

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

“…Tripathi et al [ 28 ] proposed novel channel prediction frameworks by using stochastic modeling, as well as data-driven learning of channel variability. A deep learning-based algorithm was proposed in Reference [ 29 ] to predict future channel state information (CSI) and received signal levels. Due to the complex fluctuation of underwater channel state with environmental noise, non-linear prediction methods can significantly improve accuracy [ 30 ].…”

Edge-Based V2X Communications With Big Data Intelligence

“…These studies include vehicular routing protocols [4], broadcasting protocols [5], an integration of different wireless spectrums [6], and clustering [7]. Recent studies on the temporal challenges include the prediction of vehicular communication channel [8], and online adaptive routing protocols [9]. However, none of these studies addresses the use of vehicular big data in the online decision making process.…”

Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems

“…In the past few years, several researches were conducted on channel prediction. The existing researches on channel prediction fall into three main categories: 1) the auto-regressive (AR) model [3], [10], [11], 2) the sum-ofsinusoids (SOS) [12]- [15], [17], 3) the neural network model [18]- [20]. The AR model, which adopts the AR algorithm, uses the outdated CSI to predict the future CSI.…”

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