2 research outputs found
RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network
Outdoor radio map estimation is an important tool for network planning and
resource management in modern Internet of Things (IoT) and cellular systems.
Radio map describes spatial signal strength distribution and provides network
coverage information. A practical goal is to estimate fine-resolution radio
maps from sparse radio strength measurements. However, non-uniformly positioned
measurements and access obstacles can make it difficult for accurate radio map
estimation (RME) and spectrum planning in many outdoor environments. In this
work, we develop a two-phase learning framework for radio map estimation by
integrating radio propagation model and designing a conditional generative
adversarial network (cGAN). We first explore global information to extract the
radio propagation patterns. We then focus on the local features to estimate the
effect of shadowing on radio maps in order to train and optimize the cGAN. Our
experimental results demonstrate the efficacy of the proposed framework for
radio map estimation based on generative models from sparse observations in
outdoor scenarios
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification
Among applications of deep learning (DL) involving low cost sensors, remote
image classification involves a physical channel that separates edge sensors
and cloud classifiers. Traditional DL models must be divided between an encoder
for the sensor and the decoder + classifier at the edge server. An important
challenge is to effectively train such distributed models when the connecting
channels have limited rate/capacity. Our goal is to optimize DL models such
that the encoder latent requires low channel bandwidth while still delivers
feature information for high classification accuracy. This work proposes a
three-step joint learning strategy to guide encoders to extract features that
are compact, discriminative, and amenable to common
augmentations/transformations. We optimize latent dimension through an initial
screening phase before end-to-end (E2E) training. To obtain an adjustable bit
rate via a single pre-deployed encoder, we apply entropy-based quantization
and/or manual truncation on the latent representations. Tests show that our
proposed method achieves accuracy improvement of up to 1.5% on CIFAR-10 and 3%
on CIFAR-100 over conventional E2E cross-entropy training