Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles

Abstract

With the development of intelligent connected vehicles (ICVs), there emerge many new services and applications which involve intensive computation. To support the intensive computation in vehicle-to-everything (V2X) communication system, the framework of edge computing networks has been proposed, which exploits the computation ability of edge nodes at the cost of wireless transmission. Hence, it is of vital importance to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance. To fulfil this challenge, this paper proposes a novel prediction model based on long short-term memory (LSTM) network, which is powerful in capturing valuable information in the sequence and hence is good at analyzing the spatio-temporal correlation in the channel parameters. To validate the proposed model, we conduct extensive simulations to show that the proposed model is quite effective in the channel prediction. In particular, the proposed model can outperform the conventional ones substantially.INDEX TERMS Vehicular network, edge computing, channel prediction, LSTM network. I. INTRODUCTION

Cache-aided mobile edge computing for B5G wireless communication networks

“…In future works, we will consider the application of this work for IoT networks, such as urban environment improvement [34][35][36] and environmental monitoring [37][38][39][40]. Moreover, we will incorporate the intelligent algorithms such as learning-based algorithms [41,42], deep learning [43,44], and reinforcement learning [45][46][47] into the considered system, in order to further enhance the network performance.…”

Joint User Pairing, Channel Assignment and Power Allocation in NOMA based CR Systems

“…The results show that the proposed Opt scheme offers more rate as compared to the other cases. In future works, we will incorporate some intelligent algorithms [33,34] into the considered system, in order to enhance the system performance.…”

“…Then, optimal value of β n,i,c is obtained as given below: (34) where ( ) * = max( , 0), and the value of Λ n,i,c is given by:…”

Lessons Learned From Accident of Autonomous Vehicle Testing: An Edge Learning-Aided Offloading Framework

“…Among the few related contributions, Li et al [5] introduced a convolutional neural network (CNN) to predict the road traffic situation and then a proactive load balancing approach was proposed enabling cooperation among mobile edge servers. Liu et al [6] investigated the feature of a Rayleigh fading channel and proposed to train a long short-term memory (LSTM) model to predict the future channel parameters. Additionally, Cheng et al [7] studied a case where two classical supervise machine learning methods were used to detect the Non-Line-of-Sight (NLoS) conditions by learning the V2V measurement data.…”

“…Therefore, to identify the pedestrian with maximum inference accuracy within a certain delay, the vehicle needs to perform pre-braking with the probability η m and offload their DLTs with the optimal offloading probability ̺ * m derived in (11). Calculate optimal offloading probability (̺ * m ); 5 Calculate the optimal inference error rate threshold (ǫ th * m ); 6 Evaluate the inference error rate, i.e., ǫ L m = g(Q, D V m ); 7 if ǫ L m ≥ ǫ th * m then 8 Offload the DLT to the MES; Observation 4. There exists a trade-off between the inference error rate and inference delay, as indicated by (6) and (7).…”