scholarly journals Multi-Agent Deep Reinforcement Learning Based Cooperative Edge Caching for Ultra-Dense Next-Generation Networks

2020 ◽  
pp. 1-1
Author(s):  
Shuangwu Chen ◽  
Zhen Yao ◽  
Xiaofeng Jiang ◽  
Jian Yang ◽  
Lajos Hanzo
Keyword(s):  
Reinforcement Learning ◽  
Next Generation Networks ◽  
Next Generation ◽  
Multi Agent ◽  
Edge Caching

Context-Aware Service Provisioning in Next-Generation Networks

2011 ◽  
pp. 19-38 ◽  
Author(s):  
Vedran Podobnik ◽  
Krunoslav Trzec ◽  
Gordan Jezic
Keyword(s):  
Value Chain ◽  
Service Discovery ◽  
Point Of View ◽  
Next Generation Networks ◽  
Context Aware ◽  
Service Provisioning ◽  
Next Generation ◽  
Vast Number ◽  
Multi Agent ◽  
Context Aware Service

This paper presents an application of multi-agent system in ubiquitous computing scenarios characteristic of next-generation networks. Next-generation networks will create environments populated with a vast number of consumers, which will possess diverse types of context-aware devices. In such environments the consumer should be able to access all the available services anytime, from any place, and by using any of its communication-enabled devices. Consequently, next-generation networks will require efficient mechanisms which can match consumers’ demands (requested services) to network-operators’ supplies (available services). The authors propose an agent approach for enabling autonomous coordination between all the entities across the telecom value chain, thus enabling automated context-aware service provisioning for the consumers. Furthermore, the authors hope that the proposed service discovery model will not only be interesting from a scientific point of view, but also amenable to real-world applications.

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