3 research outputs found
Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks
This study demonstrates the feasibility of the proactive received power
prediction by leveraging spatiotemporal visual sensing information toward the
reliable millimeter-wave (mmWave) networks. Since the received power on a
mmWave link can attenuate aperiodically due to a human blockage, the long-term
series of the future received power cannot be predicted by analyzing the
received signals before the blockage occurs. We propose a novel mechanism that
predicts a time series of the received power from the next moment to even
several hundred milliseconds ahead. The key idea is to leverage the camera
imagery and machine learning (ML). The time-sequential images can involve the
spatial geometry and the mobility of obstacles representing the mmWave signal
propagation. ML is used to build the prediction model from the dataset of
sequential images labeled with the received power in several hundred
milliseconds ahead of when each image is obtained. The simulation and
experimental evaluations using IEEE 802.11ad devices and a depth camera show
that the proposed mechanism employing convolutional LSTM predicted a time
series of the received power in up to 500 ms ahead at an inference time of less
than 3 ms with a root-mean-square error of 3.5 dB
An adaptable fuzzy-based model for predicting link quality in robot networks.
It is often essential for robots to maintain wireless connectivity with other systems so that commands, sensor data, and other situational information can be exchanged. Unfortunately, maintaining sufficient connection quality between these systems can be problematic. Robot mobility, combined with the attenuation and rapid dynamics associated with radio wave propagation, can cause frequent link quality (LQ) issues such as degraded throughput, temporary disconnects, or even link failure. In order to proactively mitigate such problems, robots must possess the capability, at the application layer, to gauge the quality of their wireless connections. However, many of the existing approaches lack adaptability or the framework necessary to rapidly build and sustain an accurate LQ prediction model. The primary contribution of this dissertation is the introduction of a novel way of blending machine learning with fuzzy logic so that an adaptable, yet intuitive LQ prediction model can be formed. Another significant contribution includes the evaluation of a unique active and incremental learning framework for quickly constructing and maintaining prediction models in robot networks with minimal sampling overhead