PROJECT TITLE :

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

ABSTRACT:

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.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : A Novel Dynamic Model Capturing Spatial and Temporal Patterns for Facial Expression Analysis ABSTRACT: Incorporating spatial and temporal patterns present in facial behavior should substantially improve facial
PROJECT TITLE : Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images ABSTRACT: The use of kinetic modeling (KM) on a voxel level in dynamic PET pictures frequently results in large amounts of noise,
PROJECT TITLE : Robust Unsupervised Multi-view Feature Learning with Dynamic Graph ABSTRACT: By modeling the affinity associations with a graph to lower the dimension, graph-based multi-view feature learning algorithms learn a
PROJECT TITLE : Deep Tone Mapping Operator for High Dynamic Range Images ABSTRACT: The need for a rapid tone mapping operator (TMO) capable of adapting to a wide range of high dynamic range (HDR) content on low dynamic range (LDR)
PROJECT TITLE : Dynamic Scene Deblurring by Depth Guided Model ABSTRACT: Object movement, depth fluctuation, and camera shake are the most common causes of dynamic scene blur. For the most part, present approaches use picture

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry