Dynamic Spectrum Access via Channel-Aware Heterogeneous Multi-Channel Auction With Distributed Learning


We think about the planning of dynamic spectrum access (DSA) mechanism. Assuming heterogeneous primary channels with distinct availability statistics unknown to each secondary user (SU), we tend to think about the auction-primarily based approaches for spectrum access. We tend to initial apply a unit demand (UD) auction by exploring the instantaneous link condition of each SU for its throughput maximization. To deal with the disadvantages faced in the UD auction, we tend to propose a learning-primarily based unit demand (LBUD) auction. It incorporates a distributed learning of the primary channel availabilities into the auction mechanism to explore both primary channel availability statistics and instantaneous link gains of the SUs for his or her throughput maximization. The new mechanism not solely substantially reduces Communication overhead, but also improves the SUs' throughputs when the first channels have dissimilar availability statistics. We show that the proposed LBUD auction for channel allocation among SUs preserves the strong property of the UD auction. We further propose an adaptive price increment algorithm to improve convergence speed of the iterative procedure utilized in the auction. Numerical results show the effectiveness of our proposed auction mechanism in terms of the throughput gain.

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