A Reinforcement Learning Approach for Scheduling in mmWave Networks

• URL: http://arxiv.org/abs/2108.00548v1
• Date: Sun, 1 Aug 2021 21:47:47 GMT
• Title: A Reinforcement Learning Approach for Scheduling in mmWave Networks
• Authors: Mine Gokce Dogan, Yahya H. Ezzeldin, Christina Fragouli, Addison W. Bohannon
• Abstract summary: We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network. To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning algorithm.
• Score: 24.46948464551684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network where links are subject to blockage and nodes to failure (e.g., in a hostile military environment). To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning algorithm, that adapts the information flow through the network, without using knowledge of the link capacities or network topology. Numerical evaluations show that our algorithm can achieve the desired rate even in dynamic environments and it is robust against blockage.

Related papers

• Flexible Qubit Allocation of Network Resource States [37.8666266153972]
  We explore the use of graph states with flexible, non-trivial qubit-to-node assignments.<n>We focus on cluster states with arbitrary allocation as network resource states.<n>We introduce a modeling framework for overlaying entanglement topologies on physical networks.
  arXiv  Detail & Related papers  (2025-10-17T16:01:17Z)
• Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions [5.246986428523558]
  This paper presents a framework for quantum network routing that combines belief-state planning with Graph Neural Networks (GNNs)<n>Our approach encodes complex quantum network dynamics, including entanglement degradation and time-varying channel noise, into a low-dimensional feature space.<n> Experiments on simulated quantum networks with up to 100 nodes demonstrate significant improvements in routing fidelity and entanglement delivery rates.
  arXiv  Detail & Related papers  (2025-09-10T14:50:03Z)
• Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version) [50.894272363373126]
  In wireless networks characterized by dense connectivity, the significant signaling overhead generated by distributed link scheduling algorithms can exacerbate issues like congestion, energy consumption, and radio footprint expansion.<n>We propose a distributed link sparsification scheme employing graph neural networks (GNNs) to reduce scheduling overhead for delay-tolerant traffic while maintaining network capacity.<n>A GNN module is trained to adjust contention thresholds for individual links based on traffic statistics and network topology, enabling links to withdraw from scheduling contention when they are unlikely to succeed.
  arXiv  Detail & Related papers  (2025-09-05T18:59:14Z)
• Learning State-Augmented Policies for Information Routing in Communication Networks [92.59624401684083]
  We develop a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms.
  arXiv  Detail & Related papers  (2023-09-30T04:34:25Z)
• The Integrated Forward-Forward Algorithm: Integrating Forward-Forward and Shallow Backpropagation With Local Losses [0.0]
  We propose an integrated method that combines the strengths of both FFA and shallow backpropagation. We show that training neural networks with the Integrated Forward-Forward Algorithm has the potential of generating neural networks with advantageous features like robustness.
  arXiv  Detail & Related papers  (2023-05-22T12:10:47Z)
• Identifying network topologies via quantum walk distributions [0.0]
  We use a genetic algorithm to retrieve the topology of a network from the measured probability distribution. Our result shows that the algorithm is capable of efficiently retrieving the required information even in the presence of noise.
  arXiv  Detail & Related papers  (2023-01-31T18:38:44Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• Proactive Resilient Transmission and Scheduling Mechanisms for mmWave Networks [29.17280879786624]
  This paper aims to develop resilient transmission mechanisms to suitably distribute traffic across multiple paths in an arbitrary millimeter-wave (mmWave) network. To achieve resilience to link failures, a state-of-the-art Soft Actor-Critic DRL, which adapts the information flow through the network, is investigated.
  arXiv  Detail & Related papers  (2022-11-17T02:52:27Z)
• Learning to Estimate RIS-Aided mmWave Channels [50.15279409856091]
  We focus on uplink cascaded channel estimation, where known and fixed base station combining and RIS phase control matrices are considered for collecting observations. To boost the estimation performance and reduce the training overhead, the inherent channel sparsity of mmWave channels is leveraged in the deep unfolding method. It is verified that the proposed deep unfolding network architecture can outperform the least squares (LS) method with a relatively smaller training overhead and online computational complexity.
  arXiv  Detail & Related papers  (2021-07-27T06:57:56Z)
• On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-based Approach [70.85399600288737]
  We study the problem of topology optimization and routing in IAB networks. We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution. We discuss the main challenges for enabling mesh-based IAB networks.
  arXiv  Detail & Related papers  (2021-02-14T21:52:05Z)
• Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks [122.42812336946756]
  We design an unsupervised learning method based on Aggregation Graph Neural Networks (Agg-GNNs) We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based resource allocation method.
  arXiv  Detail & Related papers  (2020-11-05T03:38:36Z)
• A Multi-step and Resilient Predictive Q-learning Algorithm for IoT with Human Operators in the Loop: A Case Study in Water Supply Networks [4.9550706407171585]
  We consider the problem of recommending resilient and predictive actions for an IoT network in the presence of faulty components. We use anonymized data from Arlington County, Virginia, to compute predictive and resilient scheduling policies for a smart water supply system.
  arXiv  Detail & Related papers  (2020-06-06T15:51:52Z)
• HYDRA: Pruning Adversarially Robust Neural Networks [58.061681100058316]
  Deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural network size. We propose to make pruning techniques aware of the robust training objective and let the training objective guide the search for which connections to prune. We demonstrate that our approach, titled HYDRA, achieves compressed networks with state-of-the-art benign and robust accuracy, simultaneously.
  arXiv  Detail & Related papers  (2020-02-24T19:54:53Z)

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.