Machine Learning Approach to Estimating mmWave Signal Measurements During Handover

10/05/2017
by   Brian L. Evans, et al.
0

We propose to estimate mmWave received signal power levels using supervised machine learning and LTE signal measurements in sub-6 GHz bands. Contributions of this paper include 1) introduction of partially blind handovers, 2) demonstration that LTE measurements from sub-6 GHz LTE bands can help improve handover to mmWave frequency bands, and 3) an algorithm to improve handover success rates using an extreme gradient boosting classifier. Simulation results show an improvement of 1.5 over the baseline method.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/05/2017

Partially Blind Handovers for mmWave New Radio Aided by Sub-6 GHz LTE Signaling

For a base station that supports cellular communications in sub-6 GHz LT...
research
09/05/2018

Classification Algorithms for Semi-Blind Uplink/Downlink Decoupling in sub-6 GHz/mmWave 5G Networks

Reliability and latency challenges in future mixed sub-6 GHz/millimeter ...
research
06/11/2019

Beam Learning -- Using Machine Learning for Finding Beam Directions

Beamforming is the key enabler for wireless communications in the mmWave...
research
08/09/2018

Routing Protocols Performance in Mobile Ad-Hoc Networks Using Millimeter Wave

Self-Organized networks (SONs) have been studied for many years, and hav...
research
08/01/2021

A Comparison Study of Cellular Deployments in Chicago and Miami Using Apps on Smartphones

Cellular operators have begun deploying 5G New Radio (NR) in all availab...
research
07/16/2021

Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems

We propose a deep learning-based method that uses spatial and temporal i...
research
01/26/2023

mmFlexible: Flexible Directional Frequency Multiplexing for Multi-user mmWave Networks

Modern mmWave systems have limited scalability due to inflexibility in p...

Please sign up or login with your details

Forgot password? Click here to reset