PROJECT TITLE :

Iterative Receivers for Downlink MIMO-SCMA: Message Passing and Distributed Cooperative Detection - 2018

ABSTRACT:

The fast development of mobile Communications requires even higher spectral potency. Non-orthogonal multiple access (NOMA) has emerged as a promising technology to additional increase the access efficiency of wireless networks. Among many NOMA schemes, it has been shown that sparse code multiple access (SCMA) is ready to realize higher performance. In this Project, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity will increase exponentially with the merchandise of the number of users, the number of antennas and also the channel length. To tackle this challenge, we have a tendency to propose near optimal low-complexity iterative receivers based mostly on issue graph. By introducing auxiliary variables, a stretched issue graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named stretch-BP-EP, is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we have a tendency to convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named conv-BP-EP. We tend to any contemplate cooperative network and propose 2 distributed cooperative detection schemes to use the diversity gain, particularly, belief consensus-primarily based algorithm and the Bregman different direction methodology of multipliers (ADMM)-primarily based method. Simulation results verify the superior performance of the proposed conv-BP-EP receiver compared with different strategies. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the variety gain. Moreover, Bregman ADMM methodology outperforms the assumption consensus-based mostly algorithm in noisy inter-user links.


Did you like this research project?

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


PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : Iterative Refinement for Multi-source Visual Domain Adaptation ABSTRACT: One of the most difficult aspects of multi-source domain adaptation is figuring out how to minimize the differences in domains that exist
PROJECT TITLE : An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection ABSTRACT: We present a novel deep learning framework that we call the Iteratively Optimized Patch Label Inference
PROJECT TITLE : Noise-Robust Iterative Back-Projection ABSTRACT: As a result of denoising, noisy image super-resolution (SR) is a substantial challenge. There is no clean reference image for iterative back-projection (IBP), which
PROJECT TITLE :Diagnosing and Minimizing Semantic Drift in Iterative Bootstrapping Extraction - 2018ABSTRACT:Semantic drift is a common problem in iterative information extraction. Previous approaches for minimizing semantic drift

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

Project Enquiry