Multimodal data analysis and integration for multi-slot spectrum auction based on Deep Feedforward Network.

Spectrum auction is considered a suitable approach to efficiently allocate spectrum among unlicensed users. In this paper, for the first time ever, we study this problem from Pattern Recognit. point of view, where all wireless users could be classified based on their economic capability, the interests of the channel for the auction, and the interference they need to suffer during communication. These factors from wireless users are multimodal data ranging from linguistic to numerical data. How to make analysis and integration of such multimodal data is a key for multi-slot spectrum auction. We adopt the Deep Feedforward Network algorithm to perform multimodal data analysis and integration for multi-slot spectrum auction. Simulation results are presented to verify the effectiveness of the proposed algorithm in the small cell network. Our approach is essentially a Pattern Recognit. approach where spectrum auction decision is made based on deep learning and different user patterns. [ABSTRACT FROM AUTHOR]