Channel-aware distributed dynamic spectrum access via learning-based heterogeneous multi-channel auction

We consider the design of a distributed online learning and access mechanism for dynamic spectrum access, where channel availability statistics are unknown to each secondary user (SU). Unlike existing distributed access policies, we explore the instantaneous channel gain of SUs' channels for multi-user multi-channel diversity gain. We consider an auction-based approach. For the primary channels with heterogeneous statistics, we apply the unit demand auction [1] to determine each SU's selection of a primary channel based on its instantaneous rate over each channel. We further propose a learning based unit demand (LBUD) auction, where each SU only bids for the M-best channels estimated by itself through distributed learning. The new mechanism not only reduces communication overhead, but also improves the throughput performance when the primary channels have dissimilar availability statistics. In addition, we show that the LBUD auction preserves the strong property of unit demand auction, i.e. it is dominant strategy incentive compatible. To improve the convergence speed of the iterative procedure of channel allocation in the auction, we also propose an adaptive price increment algorithm. Simulations show the effectiveness of our proposed auction mechanism in throughput gain by exploring instantaneous channel fade.