skip to main content
article

Deep Learning for Reliable Communication Optimization on Autonomous Vehicles

Authors Info & Claims
Published:19 December 2022Publication History
Skip Abstract Section

Abstract

Recent breakthroughs in the autonomous vehicle industry have brought this technology closer to consumers. However, the cost of self-driving solutions still constitutes an entry barrier to many potential users due to its reliance on powerful onboard computers. As an alternative, autonomous driving algorithm processing may be offloaded to remote machines, which requires a reliable connection to the cloud servers. However, despite significant 5G coverage in many countries, mobile network reliability and latency are still inadequate for this purpose. This work explores deep learning concepts to forecast signal quality as a vehicle moves, predicting when periods of degraded network quality will occur. We develop a Long Short-Term Memory (LSTM)-based neural network, trained on multivariate time series containing historical data on several mobile network parameters, and evaluate the results of multi-step Reference Signal Received Power (RSRP) prediction. Results show that our model achieves a rapidly increasing Root-Mean-Square Error (RMSE), reaching over 8 dBm after 25-time steps. This error does not allow for the accurate prediction of future signal quality.

References

  1. N. Bui, M. Cesana, S. A. Hosseini, Q. Liao, I. Malanchini, and J. Widmer, "A survey of anticipatory mobile networking: Context-based classification, prediction methodologies, and optimization techniques," IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1790--1821, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D. Raca, A. H. Zahran, C. J. Sreenan, R. K. Sinha, E. Halepovic, R. Jana, and V. Gopalakrishnan, "On leveraging machine and deep learning for throughput prediction in cellular networks: Design, performance, and challenges," IEEE Communications Magazine, vol. 58, no. 3, pp. 11--17, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  3. J. Ni, Y. Chen, Y. Chen, J. Zhu, D. Ali, and W. Cao, "A survey on theories and applications for self-driving cars based on deep learning methods," Applied Sciences, vol. 10, no. 8, p. 2749, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  4. G. L. Santos, P. T. Endo, D. Sadok, and J. Kelner, "When 5g meets deep learning: a systematic review," Algorithms, vol. 13, no. 9, p. 208, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  5. D. Minovski, N. Ogren, C. Ahlund, and K.Mitra, "Throughput prediction using machine learning in lte and 5g networks," IEEE Transactions on Mobile Computing, 2021.Google ScholarGoogle ScholarCross RefCross Ref
  6. N. Prihodko, "Machine learning for forecasting signal strength in mobile networks," 2018.Google ScholarGoogle Scholar
  7. Q. V. Le, N. Jaitly, and G. E. Hinton, "A simple way to initialize recurrent networks of rectified linear units," arXiv preprint arXiv:1504.00941, 2015.Google ScholarGoogle Scholar
  8. D. Raca, J. J. Quinlan, A. H. Zahran, and C. J. Sreenan, "Beyond throughput: A 4g lte dataset with channel and context metrics," in Proceedings of the 9th ACM Multimedia Systems Conference, MMSys '18, (New York, NY, USA), p. 460--465, Association for Computing Machinery, 2018.Google ScholarGoogle Scholar
  9. D. Raca, D. Leahy, C. J. Sreenan, and J. J. Quinlan, "Beyond throughput, the next generation: A 5g dataset with channel and context metrics," in Proceedings of the 11th ACM Multimedia Systems Conference, MMSys '20, (New York, NY, USA), p. 303--308, Association for Computing Machinery, 2020.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Article Metrics

    • Downloads (Last 12 months)14
    • Downloads (Last 6 weeks)3

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader
About Cookies On This Site

We use cookies to ensure that we give you the best experience on our website.

Learn more

Got it!