DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
- URL: http://arxiv.org/abs/2112.12985v2
- Date: Wed, 5 Apr 2023 12:45:33 GMT
- Title: DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
- Authors: Daniel F. Perez-Ramirez, Carlos P\'erez-Penichet, Nicolas Tsiftes,
Thiemo Voigt, Dejan Kostic, Magnus Boman
- Abstract summary: DeepGANTT is a scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling.
We show that DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x larger in the number of tags than those used for training.
- Score: 6.412612364488614
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Novel backscatter communication techniques enable battery-free sensor tags to
interoperate with unmodified standard IoT devices, extending a sensor network's
capabilities in a scalable manner. Without requiring additional dedicated
infrastructure, the battery-free tags harvest energy from the environment,
while the IoT devices provide them with the unmodulated carrier they need to
communicate. A schedule coordinates the provision of carriers for the
communications of battery-free devices with IoT nodes. Optimal carrier
scheduling is an NP-hard problem that limits the scalability of network
deployments. Thus, existing solutions waste energy and other valuable resources
by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning
scheduler that leverages graph neural networks to efficiently provide
near-optimal carrier scheduling. We train our scheduler with relatively small
optimal schedules obtained from a constraint optimization solver, achieving a
performance within 3% of the optimal scheduler. Without the need to retrain,
DeepGANTT generalizes to networks 6x larger in the number of nodes and 10x
larger in the number of tags than those used for training, breaking the
scalability limitations of the optimal scheduler and reducing carrier
utilization by up to 50% compared to the state-of-the-art heuristic. Our
scheduler efficiently reduces energy and spectrum utilization in backscatter
networks.
Related papers
- Robust Generalization of Graph Neural Networks for Carrier Scheduling [4.311529300510196]
This paper introduces RobustGANTT, a GNN-based scheduler that improves generalization (without re-training) to networks up to 1000 nodes.
Our work not only improves resource utilization in large-scale backscatter networks, but also offers valuable insights in learning-based scheduling.
arXiv Detail & Related papers (2024-07-11T13:13:24Z) - Energy-Efficient On-Board Radio Resource Management for Satellite
Communications via Neuromorphic Computing [59.40731173370976]
We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management.
For relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$times$ as compared to the CNN-based reference platform.
arXiv Detail & Related papers (2023-08-22T03:13:57Z) - Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays [55.736718475856726]
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
arXiv Detail & Related papers (2023-08-03T02:49:50Z) - Adaptive ResNet Architecture for Distributed Inference in
Resource-Constrained IoT Systems [7.26437825413781]
This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model's performance.
Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution.
arXiv Detail & Related papers (2023-07-21T11:07:21Z) - Distributed Link Sparsification for Scalable Scheduling Using Graph
Neural Networks [37.84368235950714]
We propose a distributed scheme for link sparsification with graph convolutional networks (GCNs)
In medium-sized wireless networks, our proposed sparse scheduler beats threshold-based sparsification policies by retaining almost $70%$ of the total capacity achieved by a greedy scheduler.
arXiv Detail & Related papers (2022-03-27T16:02:12Z) - Deep Reinforcement Learning Based Multidimensional Resource Management
for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency.
In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
arXiv Detail & Related papers (2021-09-17T08:55:48Z) - Energy-Efficient Model Compression and Splitting for Collaborative
Inference Over Time-Varying Channels [52.60092598312894]
We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.
Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
arXiv Detail & Related papers (2021-06-02T07:36:27Z) - Deep Learning for Radio Resource Allocation with Diverse
Quality-of-Service Requirements in 5G [53.23237216769839]
We develop a deep learning framework to approximate the optimal resource allocation policy for base stations.
We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers.
Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
arXiv Detail & Related papers (2020-03-29T04:48:22Z) - Wireless Power Control via Counterfactual Optimization of Graph Neural
Networks [124.89036526192268]
We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium.
To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture.
We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
arXiv Detail & Related papers (2020-02-17T07:54:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.