Exploration vs exploitation for distributed channel access in cognitive radio networks: A multi-user case study

Longwei Wang, Xianfu Chen, Zhifeng Zhao, Honggang Zhang
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引用次数: 7

Abstract

In this paper, we consider the design of efficient strategies that allow multiple secondary users to choose channels with unknown idle probabilities to sense and access without centralized control. Due to the limited sensing capability of CR, the secondary user cannot sense all the channels simultaneously. How to design intelligent sensing strategy is crucial to track the varying spectrum opportunities. However, the availability probability of each channel is not known a prior. The need to learning the information creates a fundamental trade-off between exploration and exploitation. First, the scenario in which a single cognitive user wishes to opportunistically exploit the availability of idle spectrum is considered. An index based strategy from the classical multi-armed bandit problem is efficient to achieve asymptotically optimal performance. Then, the multiuser case is considered. However, the index based strategy for the single user cannot be applied to multiuser scenario directly. If so, collisions among secondary users degrade the overall network performance greatly. We find that randomized selection of the channel to sense is essential to avoid collisions. We extend the exploration and exploitation idea to the multiuser scenario and come up with a randomization based mixed strategy which takes the activity of other secondary users into consideration in the learning process and at the same time achieves exploration and exploitation tradeoff. Numerical simulation results show that the proposed scheme can achieve near optimal in terms of total network performance as the centralized scenario without any information exchange among cognitive users.
认知无线网络中分布式信道访问的探索与开发:一个多用户案例研究
在本文中,我们考虑设计一种有效的策略,允许多个辅助用户选择具有未知空闲概率的信道进行感知和访问,而无需集中控制。由于CR的感知能力有限,辅助用户无法同时感知所有信道。如何设计智能传感策略是跟踪变化频谱机会的关键。然而,每个通道的可用性概率事先是未知的。学习信息的需求在探索和利用之间产生了一种基本的权衡。首先,考虑了单个认知用户希望投机地利用空闲频谱可用性的场景。针对经典多臂盗匪问题,提出了一种基于指标的策略,可以有效地达到渐近最优性能。然后,考虑多用户情况。但是,单用户基于索引的策略不能直接应用于多用户场景。如果是这样,辅助用户之间的冲突会大大降低整个网络的性能。我们发现随机选择要感知的通道对于避免碰撞至关重要。我们将探索和利用思想扩展到多用户场景,提出了一种基于随机化的混合策略,该策略在学习过程中考虑了其他二次用户的活动,同时实现了探索和利用的权衡。数值仿真结果表明,在不需要认知用户之间进行任何信息交换的集中式场景下,所提出的方案在网络总体性能方面接近最优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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