LSTM-Based Multi-Link Prediction for mmWave and Sub-THz Wireless Systems

Syed Hashim Ali Shah, Manali Sharma, Sundeep Rangan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A key challenge in mmWave systems is the rapid variations in channel quality along different beam directions. MmWave links are highly susceptible to blockage and small changes in the orientation of the device or appearance of blockers can lead to dramatic changes in link quality along any given direction. Many low-latency applications need to accurately predict link quality from multiple directions and multiple cells. This paper presents a novel long short term memory (LSTM)-based method for predicting multi-directional link quality in mmWave systems. The method is validated on two problems: A realistic simulation of multi-cell link tracking in an environment with randomly moving human and vehicular blockers at 28 and 140 GHz, and beam prediction in a real indoor setting at 60 GHz.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150895
DOIs
StatePublished - Jun 2020
Event2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
Duration: Jun 7 2020Jun 11 2020

Publication series

NameIEEE International Conference on Communications
Volume2020-June
ISSN (Print)1550-3607

Conference

Conference2020 IEEE International Conference on Communications, ICC 2020
CountryIreland
CityDublin
Period6/7/206/11/20

Keywords

  • cellular wireless
  • LSTM
  • machine learning
  • Millimeter wave

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'LSTM-Based Multi-Link Prediction for mmWave and Sub-THz Wireless Systems'. Together they form a unique fingerprint.

Cite this