中文版本

Opportunity  

The deployment of communication infrastructure, such as Roadside Units (RSUs) for Vehicle-to-Everything (V2X) networks, is a complex and costly process. Existing deployment schemes are typically designed for specific, static requirements. When deployment objectives change—such as shifting focus from maximizing road coverage to optimizing communication latency or integrating new data sources like traffic flow patterns—a completely new deployment model must be developed from scratch. This approach lacks generality and flexibility, leading to significant increases in design time, computational cost, and human resource expenditure for each new scenario. The absence of a unified, adaptable framework that can handle diverse and evolving deployment demands (e.g., for smart cities, highways, or campuses) represents a major bottleneck in efficiently scaling and optimizing communication networks.

Technology  

This patent introduces a generic, data-driven framework for automated communication device deployment. Its core innovation lies in the synergistic integration of an Analysis Neural Network and a Reinforcement Learning (RL) network. The process begins by determining Target Deployment Performance Indicators (e.g., road coverage, communication quality) from an extensible Output Metrics Library based on the current deployment needs. An extensible Input Information Library provides diverse data sources like maps, GPS trajectories, and POI data. The Analysis Neural Network, comprising a feature extraction network and a fusion network, automatically processes these data sources. It extracts multiple base features and, crucially, uses an attention-based mechanism within the fusion network to learn feature weights. These weights dynamically highlight or suppress base features based on their importance for the current target performance indicators, generating an optimized "Fused Feature" vector. This fused feature, along with information about already deployed devices, is fed into a Deep Reinforcement Learning (DRL) network modeled as a Markov Decision Process (MDP). The DRL agent (e.g., using a Proximal Policy Optimization algorithm) interacts with an environment representing the target area. It sequentially chooses deployment locations (actions) to maximize the cumulative reward, which is directly derived from the target performance indicators. The framework jointly trains the fusion network and the RL network, ensuring the learned feature weights are aligned with the deployment objectives. This end-to-end system automatically discovers near-optimal deployment strategies without manual feature engineering or model redesign for each new requirement.

Advantages  

• High Generality and Flexibility: The extensible input and output libraries allow the same framework to adapt to various deployment demands (e.g., coverage, latency, prediction accuracy) by simply selecting different performance indicators, without redesigning the core model.
• Automated Feature Engineering: The analysis neural network automatically learns and fuses relevant features from raw data sources, eliminating the need for manual, domain-expert-driven feature selection and reducing subjectivity.
• Optimized Long-term Planning: The DRL component models deployment as a sequential decision-making process, finding globally optimal or near-optimal strategies that consider the incremental nature of real-world infrastructure rollout.
• Reduced Design Cost and Time: By automating both feature extraction and strategy optimization, the framework significantly cuts down the time, computational resources, and expert labor required to develop new deployment plans.
• Improved Deployment Efficiency: Techniques like action space masking (using information about already covered areas) and candidate region filtering prune inefficient options, speeding up the learning process and improving solution quality.
  
• Scalability: The framework can handle large-scale deployment scenarios and can be integrated with various V2X simulators (e.g., SUMO, Carla) for testing and validation.
  

Applications  

• Large-scale RSU Deployment for C-V2X Networks: Planning optimal RSU placements along highways and urban roads to enhance connectivity for connected and autonomous vehicles.
  
• Smart City Infrastructure Planning: Deploying communication nodes (e.g., for IoT networks) to support city management services, traffic monitoring, and public safety.
  
• Specialized Area Coverage Optimization: Designing communication infrastructure layouts for airports, smart campuses, ports, and industrial zones.
  
• Network Planning for Emerging Services: Adapting deployment strategies for new V2X applications like high-definition map updates, collective perception, or mobility prediction.
  
• Integration with Simulation Platforms: Serving as a planning tool within V2X simulation environments to evaluate and generate deployment strategies under simulated conditions.