Learning to route has received significant research momentum as a
new approach for the route planning problem in intelligent transportation
systems. By exploring global knowledge of geographical
areas and topological structures of road networks to facilitate route
planning, in this work, we propose a novel Generative Adversarial
Network (GAN) framework, namely Progressive Route Planning
GAN (ProgRPGAN), for route planning in road networks. The novelty
of ProgRPGAN lies in the following aspects: 1) we propose to
plan a route with levels of increasing map resolution, starting on a
low-resolution grid map, gradually refining it on higher-resolution
grid maps, and eventually on the road network in order to progressively
generate various realistic paths; 2) we propose to transfer
parameters of the previous-level generator and discriminator to the
subsequent generator and discriminator for parameter initialization
in order to improve the efficiency and stability in model learning;
and 3) we propose to pre-train embeddings of grid cells in grid maps
and intersections in the road network by capturing the network
topology and external factors to facilitate effective model learning.
Empirical result shows that ProgRPGAN soundly outperforms
the state-of-the-art learning to route methods, especially for long
routes, by 9.46% to 13.02% in F1-measure on multiple large-scale
real-world datasets. ProgRPGAN, moreover, effectively generates
various realistic routes for the same query.
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Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority
Radio map describes network coverage and is a
practically important tool for network planning in modern
wireless systems. Generally, radio strength measurements are
collected to construct fine-resolution radio maps for analysis.
However, certain protected areas are not accessible for measurement
due to physical constraints and security considerations,
leading to blanked spaces on a radio map. Non-uniformly spaced
measurement and uneven observation resolution make it more
difficult for radio map estimation and spectrum planning in
protected areas. This work explores the distribution of radio
spectrum strengths and proposes an exemplar-based approach
to reconstruct missing areas on a radio map. Instead of taking
generic image processing approaches, we leverage radio propagation
models to determine directions of region filling and develop
two different schemes to estimate the missing radio signal power.
Our test results based on high-fidelity simulation demonstrate
efficacy of the proposed methods for radio map reconstruction.
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- NSF-PAR ID:
- 10347283
- Date Published:
- Journal Name:
- IEEE Global Communications Conference
- Page Range / eLocation ID:
- 1217-1222
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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