向认知无线电网络中的专家学习:主动性范式

Ana Galindo-Serrano, L. Giupponi, Pol Blasco, M. Dohler
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引用次数: 23

摘要

本文介绍了认知无线电(CR)网络的一种新的定位范式。我们认为cr是智能无线电,实现了一个学习过程,通过这个过程,它们与周围环境相互作用,做出自适应决策。然而,在分布式环境中,由于交互式决策过程,学习可能是复杂和缓慢的,这导致了非平稳环境。主动性范式提出了一种基于知识共享的及时解决方案,它允许cr开发选择行动的新能力。我们证明了这提高了cr的学习能力和准确性,并为他们提供了在未访问状态下的行动选择策略。我们在建模为多智能体系统的二级系统的背景下评估了主动范式,其中智能体是IEEE 802.22 CR基站,实现了一种称为分散q学习的实时多智能体强化学习技术。我们的目标是解决主系统接收机处多个CR系统产生的聚合干扰问题。我们提出了三种不同的训练算法,并展示了它们在收敛速度和精度方面优于众所周知的自主学习范式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning from experts in cognitive radio networks: The docitive paradigm
In this paper we introduce the novel paradigm of docition for cognitive radio (CR) networks. We consider that the CRs are intelligent radios implementing a learning process through which they interact with the surrounding environment to make self-adaptive decisions. However, in distributed settings the learning may be complex and slow, due to interactive decision making processes, which results in a non-stationary environment. The docitive paradigm proposes a timely solution based on knowledge sharing, which allows CRs to develop new capacities for selecting actions. We demonstrate that this improves the CRs' learning ability and accuracy, and gives them strategies for action selection in unvisited states. We evaluate the docitive paradigm in the context of a secondary system modeled as a multi-agent system, where the agents are IEEE 802.22 CR base stations, implementing a real-time multi-agent reinforcement learning technique known as decentralized Q-learning. Our goal is to solve the aggregated interference problem generated by multiple CR systems at the receivers of a primary system. We propose three different docitive algorithms and we show their superiority to the well know paradigm of independent learning in terms of speed of convergence and precision.
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