Iterative Decoding of LDPC Codes Over the $q$ -Ary Partial Erasure Channel


In this paper, we have a tendency to develop a replacement channel model, which we tend to name the -ary partial erasure channel (QPEC). The QPEC encompasses a -ary input, and its output is either the input symbol or a set of ( ) symbols, containing the input symbol. This channel serves as a generalization to the binary erasure channel and mimics situations when a symbol output from the channel is understood solely partially; that is, the output symbol contains some ambiguity, however is not absolutely erased. This type of channel is motivated by non-volatile memory multi-level scan channels. In such channels, the readout is obtained by a sequence of current/voltage measurements, that may terminate with a partial knowledge of the stored level. Our investigation is concentrated on the performance of low-density parity-check (LDPC) codes when used over this channel, thanks to their low decoding complexity using belief propagation. We provide the exact QPEC density-evolution equations that govern the decoding process, and counsel a cardinality-based mostly approximation as a proxy. We have a tendency to then give many bounds and approximations on the proxy density evolutions, and verify their tightness through numerical experiments. Finally, we offer tools for the sensible design of LDPC codes for use over the QPEC.

Did you like this research project?

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

PROJECT TITLE :Iterative Receivers for Downlink MIMO-SCMA: Message Passing and Distributed Cooperative Detection - 2018ABSTRACT:The fast development of mobile communications requires even higher spectral potency. Non-orthogonal
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
PROJECT TITLE :Iterative Block Tensor Singular Value Thresholding For Extraction Of Low Rank Component Of Image Data - 2017ABSTRACT:Tensor principal component analysis (TPCA) is a multi-linear extension of principal component
PROJECT TITLE : Efficiently Promoting Product Online Outcome: An Iterative Rating Attack Utilizing Product and Market Property - 2017 ABSTRACT: The prosperity of on-line rating system makes it a popular place for malicious
PROJECT TITLE : On Fault Tolerance for Distributed Iterative Dataflow Processing - 2017 ABSTRACT: Large-scale graph and machine learning analytics widely use distributed iterative processing. Typically, these analytics are

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

Project Enquiry