Multi-Slot Spectrum Auction in Heterogeneous Networks Based on Deep Feedforward Network

A spectrum auction is a promising approach with respect to efficiently allocating spectrum among unlicensed users. In this paper, we study the spectrum auction based on the waveform and air-interface of wireless users, the interests of the channel for the auction, and the interference they suffered during communication as well as their economic capability. How to make the analysis and the integration of such multiple factors is a key problem for multi-slot spectrum auction. To address the problem, we adopt the deep feedforward network algorithm to perform waveform and air-interface 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 could be used to 5G where heterogeneous wireless networks will be applied extensively and spectrum auction decision is made based on deep learning and different user patterns.