Communication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a federated multi-agent deep reinforcement learning method for vehicular networks. It enhances spectral efficiency and transmission success rates in dynamic vehicle-to-everything communications.
Area Of Science
- Vehicular Networking
- Communication Systems
- Machine Learning
Background
- Traditional resource allocation in vehicular networks suffers from low spectral efficiency due to lack of global optimization and slow response to dynamic environments.
- Sharing spectrum resources between Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) links presents significant challenges for efficient resource management.
Purpose Of The Study
- To propose a novel resource allocation method for vehicular networks using federated multi-agent deep reinforcement learning.
- To improve system spectral efficiency, V2V transmission success rate, and V2I link capacity in dynamic vehicular communication scenarios.
Main Methods
- Fusing Asynchronous Federated Learning (AFL) with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for synergistic resource allocation.
- Vehicles act as agents optimizing spectrum access, power control, and bandwidth allocation based on local channel states.
- An asynchronous federation architecture enables independent model parameter uploads, dynamic weight adjustment based on channel quality, and global model optimization.
Main Results
- Achieved an average improvement of 19.1% in system spectral efficiency compared to existing algorithms.
- Increased the average transmission success rate of V2V links by 9.3%.
- Enhanced the average total capacity of V2I links by 16.1%.
Conclusions
- The proposed federated multi-agent deep reinforcement learning method effectively optimizes resource allocation in vehicular networks.
- The approach significantly improves key performance metrics, demonstrating its superiority over traditional and other advanced algorithms.
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
Related Concept Videos
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
• If a person smiles at you after you greet them and you continue talking, the smile is a positive reinforcement for your greeting.
•...
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...