Amazon in the White Space: Social Recommendation Aided Distributed Spectrum Access
Xu Chen, Xiaowen Gong, Lei Yang, Junshan Zhang

TL;DR
This paper proposes social recommendation mechanisms to improve distributed spectrum access for secondary users, leveraging social data and game theory to enhance decision-making under different information scenarios.
Contribution
It introduces social recommendation aided DSA mechanisms, including a potential game approach for strong information cases and a distributed reinforcement learning method for weak information scenarios.
Findings
Mechanisms outperform traditional methods in spectrum utilization.
Performance loss in weak information case is minimal.
Numerical results validate effectiveness with real social data.
Abstract
Distributed spectrum access (DSA) is challenging since an individual secondary user often has limited sensing capabilities only. One key insight is that channel recommendation among secondary users can help to take advantage of the inherent correlation structure of spectrum availability in both time and space, and enable users to obtain more informed spectrum opportunities. With this insight, we advocate to leverage the wisdom of crowds, and devise social recommendation aided DSA mechanisms to orient secondary users to make more intelligent spectrum access decisions, for both strong and weak network information cases. We start with the strong network information case where secondary users have the statistical information. To mitigate the difficulty due to the curse of dimensionality in the stochastic game approach, we take the one-step Nash approach and cast the social recommendation…
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Taxonomy
TopicsCognitive Radio Networks and Spectrum Sensing · Advanced MIMO Systems Optimization · Advanced Bandit Algorithms Research
